analise_de_dados.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise de dados: Anúncios em redes sociais\n",
    "\n",
    "Este é um projeto de machine learning para análise de dados de usuários que efetuaram ou não uma compra, os dados foram disponibilizados na plataforma [Kaggle](https://www.kaggle.com/datasets/dragonheir/logistic-regression).\n",
    "\n",
    "Não estão definidos no dataset o caso proposto e o problema a ser solucionado, então baseados nos dados serem de compras efetuadas ou não por usuários e arquivo de dados ser nomeado como \"anúncios de redes sociais\", será adotada a seguinte interpretação: *O arquivo Social_Network_Ads.csv contém dados de usuários de uma rede social que tiveram alguma interação com um anúncio e foram incentivados ou não a efetuar uma compra, sendo assim o objetivo do desenvolvimento do modelo será em prever quais clientes estimulados pelo(s) anúncio(s) realizará ou não uma compra.*\n",
    "\n",
    "## Demanda da análise\n",
    "\n",
    "- Analisar a correlação dos dados dos usuários.\n",
    "- Gerar um modelo preditivo de compra ou não por um usuário.\n",
    "- Simular novas entradas de dados e previsões.\n",
    "\n",
    "## Tópicos da análise\n",
    "\n",
    "1. Análise das medidas estatísticas dos dados\n",
    "2. Distribuição dos dados entre as variáveis independentes (features) e dependente (target)\n",
    "3. Correlação entre as variáveis\n",
    "4. Criação do modelo de regressão logística\n",
    "   1. Separação dos dados de treino e teste\n",
    "   2. Treinamento e predições do modelo\n",
    "   3. Métricas do modelo\n",
    "   4. Previsões com novos dados\n",
    "5. Conclusão\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importação de pacotes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
    "from sklearn.metrics import (accuracy_score,\n",
    "                             classification_report,\n",
    "                             confusion_matrix,\n",
    "                             roc_curve,\n",
    "                             roc_auc_score)\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from tabulate import tabulate\n",
    "\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "sns.set_style('darkgrid')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Carregamento dos dados\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Efetuando limpeza de memória antes do carregamento de dados\n",
    "gc.collect()\n",
    "\n",
    "# Caminho do arquivo\n",
    "path = 'dados/Social_Network_Ads.csv'\n",
    "\n",
    "# Criando um dataframe a partir do arquivo no diretório dados\n",
    "df = pd.read_csv(path, encoding='utf-8', sep=',')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detalhes do dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   User ID          400 non-null    int64 \n",
      " 1   Gender           400 non-null    object\n",
      " 2   Age              400 non-null    int64 \n",
      " 3   EstimatedSalary  400 non-null    int64 \n",
      " 4   Purchased        400 non-null    int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 36.9 KB\n"
     ]
    }
   ],
   "source": [
    "# Informações do dataset como nome das colunas, contagem de linhas,\n",
    "# tipo de dados e memória utilizada\n",
    "\n",
    "df.info(memory_usage='deep')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>15704583</td>\n",
       "      <td>Male</td>\n",
       "      <td>46</td>\n",
       "      <td>28000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>15566689</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>15673539</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>73000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>15756820</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>37000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>15642725</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>59000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>15746203</td>\n",
       "      <td>Female</td>\n",
       "      <td>47</td>\n",
       "      <td>144000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>15726358</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>15727467</td>\n",
       "      <td>Male</td>\n",
       "      <td>57</td>\n",
       "      <td>74000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>15679760</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>15756932</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>80000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "18   15704583    Male   46            28000          1\n",
       "294  15566689  Female   35            57000          0\n",
       "296  15673539    Male   42            73000          1\n",
       "99   15756820  Female   28            37000          0\n",
       "118  15642725    Male   40            59000          0\n",
       "268  15746203  Female   47           144000          1\n",
       "71   15726358  Female   24            27000          0\n",
       "275  15727467    Male   57            74000          1\n",
       "66   15679760    Male   24            19000          0\n",
       "70   15756932    Male   25            80000          0"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Amostra de dados do dataframe\n",
    "\n",
    "df.sample(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Nomes originais das colunas\n",
      "\n",
      " ['User ID', 'Gender', 'Age', 'EstimatedSalary', 'Purchased']\n",
      "\n",
      "Novos nomes das colunas\n",
      "\n",
      " ['id_usuario', 'genero', 'idade', 'salario_estimado', 'comprado']\n"
     ]
    }
   ],
   "source": [
    "# Renomeando as colunas\n",
    "\n",
    "print(\"\\nNomes originais das colunas\\n\\n\", df.columns.to_list())\n",
    "\n",
    "df.rename(columns={'User ID': 'id_usuario',\n",
    "                   'Gender': 'genero',\n",
    "                   'Age': 'idade',\n",
    "                   'EstimatedSalary': 'salario_estimado',\n",
    "                   'Purchased': 'comprado'},\n",
    "          inplace=True)\n",
    "\n",
    "print(\"\\nNovos nomes das colunas\\n\\n\", df.columns.to_list())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 5)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantidade de linhas e colunas\n",
    "\n",
    "df.shape\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removendo colunas que não serão utilizadas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['id_usuario'])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verificando e tratando se necessário os valores duplicados\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    380\n",
       "True      20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identificando a quantidade de valores duplicados (True)\n",
    "\n",
    "df.duplicated(keep='first').value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "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>genero</th>\n",
       "      <th>idade</th>\n",
       "      <th>salario_estimado</th>\n",
       "      <th>comprado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>18000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>83000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>82000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>42000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>72000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>Male</td>\n",
       "      <td>41</td>\n",
       "      <td>52000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>65000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>75000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Male</td>\n",
       "      <td>38</td>\n",
       "      <td>61000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>72000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>54000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>Male</td>\n",
       "      <td>38</td>\n",
       "      <td>71000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>Female</td>\n",
       "      <td>37</td>\n",
       "      <td>80000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>28000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>Male</td>\n",
       "      <td>48</td>\n",
       "      <td>33000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Male</td>\n",
       "      <td>60</td>\n",
       "      <td>42000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     genero  idade  salario_estimado  comprado\n",
       "74     Male     32             18000         0\n",
       "94   Female     29             83000         0\n",
       "186  Female     20             82000         0\n",
       "192    Male     29             43000         0\n",
       "231    Male     39             42000         0\n",
       "236    Male     40             57000         0\n",
       "256  Female     41             72000         0\n",
       "257    Male     40             57000         0\n",
       "312  Female     38             50000         0\n",
       "322    Male     41             52000         0\n",
       "332    Male     42             65000         0\n",
       "341    Male     35             75000         0\n",
       "349    Male     38             61000         0\n",
       "357  Female     41             72000         0\n",
       "359    Male     42             54000         0\n",
       "368    Male     38             71000         0\n",
       "374  Female     37             80000         0\n",
       "383    Male     49             28000         1\n",
       "390    Male     48             33000         1\n",
       "393    Male     60             42000         1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embora exista uma quantidade considerável de dados duplicados se \n",
      "comparado a toda a amostra de dados, cada registro é identificado pela \n",
      "coluna 'id_usuario' ('User ID') que é exclusivo/único de cada usuário, as \n",
      "colunas 'genero', 'idade', 'salario_estimado' e 'comprado', sendo que a \n",
      "coluna 'salario_estimado', como o próprio nome diz, é uma estimativa de \n",
      "ganho do usuário, logo estes dados apontados como duplicados serão mantidos \n",
      "no dataframe.\n"
     ]
    }
   ],
   "source": [
    "# Listando os valores duplicados e definindo a ação a ser tomada\n",
    "\n",
    "display(df[df.duplicated()])\n",
    "\n",
    "\n",
    "print('''Embora exista uma quantidade considerável de dados duplicados se \n",
    "comparado a toda a amostra de dados, cada registro é identificado pela \n",
    "coluna 'id_usuario' ('User ID') que é exclusivo/único de cada usuário, as \n",
    "colunas 'genero', 'idade', 'salario_estimado' e 'comprado', sendo que a \n",
    "coluna 'salario_estimado', como o próprio nome diz, é uma estimativa de \n",
    "ganho do usuário, logo estes dados apontados como duplicados serão mantidos \n",
    "no dataframe.''')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verificando e tratando se necessário os valores únicos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genero                2\n",
       "idade                43\n",
       "salario_estimado    117\n",
       "comprado              2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a quantidade de valores únicos\n",
    "\n",
    "df.nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Female  Male\n",
      "genero     204   196\n",
      "\n",
      "        18  19  20  21  22  23  24  25  26  27  ...  51  52  53  54  55  56  \\\n",
      "idade   5   7   7   4   5   6   9   6  16  13  ...   3   6   5   4   3   3   \n",
      "\n",
      "       57  58  59  60  \n",
      "idade   5   6   7   7  \n",
      "\n",
      "[1 rows x 43 columns]\n",
      "\n",
      "             0    1\n",
      "comprado  257  143\n"
     ]
    }
   ],
   "source": [
    "# Verificando a quantidade dos valores únicos de colunas específicas\n",
    "\n",
    "genero_qtde = pd.DataFrame(df['genero'].value_counts().sort_index())\n",
    "genero_qtde = genero_qtde.T\n",
    "print(genero_qtde)\n",
    "\n",
    "idade_qtde = pd.DataFrame(df['idade'].value_counts().sort_index())\n",
    "idade_qtde = idade_qtde.T\n",
    "print('\\n', idade_qtde)\n",
    "\n",
    "comprado_qtde = pd.DataFrame(df['comprado'].value_counts().sort_index())\n",
    "comprado_qtde = comprado_qtde.T\n",
    "print('\\n', comprado_qtde)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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>genero</th>\n",
       "      <th>idade</th>\n",
       "      <th>salario_estimado</th>\n",
       "      <th>comprado</th>\n",
       "      <th>feminino</th>\n",
       "      <th>masculino</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>27</td>\n",
       "      <td>58000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>27</td>\n",
       "      <td>84000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>32</td>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>25</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>35</td>\n",
       "      <td>65000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      genero  idade  salario_estimado  comprado  feminino  masculino\n",
       "0  Masculino     19             19000         0       0.0        1.0\n",
       "1  Masculino     35             20000         0       0.0        1.0\n",
       "2   Feminino     26             43000         0       1.0        0.0\n",
       "3   Feminino     27             57000         0       1.0        0.0\n",
       "4  Masculino     19             76000         0       0.0        1.0\n",
       "5  Masculino     27             58000         0       0.0        1.0\n",
       "6   Feminino     27             84000         0       1.0        0.0\n",
       "7   Feminino     32            150000         1       1.0        0.0\n",
       "8  Masculino     25             33000         0       0.0        1.0\n",
       "9   Feminino     35             65000         0       1.0        0.0"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformando os valores da categoria 'genero' em categorias distintas e\n",
    "# traduzindo os valores da categoria 'genero'\n",
    "\n",
    "arr_genero = OneHotEncoder().fit_transform(\n",
    "    df['genero'].values.reshape(-1, 1)).toarray()\n",
    "df_genero = pd.DataFrame(arr_genero)\n",
    "df_genero.rename(columns={0: 'feminino', 1: 'masculino'}, inplace=True)\n",
    "df = pd.concat([df, df_genero], axis=1)\n",
    "df['genero'].replace({'Male': 'Masculino', 'Female': 'Feminino'}, inplace=True)\n",
    "df.head(10)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verificando e tratando se necessário os tipos de valores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genero               object\n",
       "idade                 int64\n",
       "salario_estimado      int64\n",
       "comprado              int64\n",
       "feminino            float64\n",
       "masculino           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identificando os tipos de dados das colunas\n",
    "\n",
    "df.dtypes\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verificando e tratando se necessário os valores nulos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genero              0\n",
       "idade               0\n",
       "salario_estimado    0\n",
       "comprado            0\n",
       "feminino            0\n",
       "masculino           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identificando a quantidade de valores nulos\n",
    "\n",
    "df.isnull().sum()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Amostra de dados do dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "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>genero</th>\n",
       "      <th>idade</th>\n",
       "      <th>salario_estimado</th>\n",
       "      <th>comprado</th>\n",
       "      <th>feminino</th>\n",
       "      <th>masculino</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>36</td>\n",
       "      <td>125000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>32</td>\n",
       "      <td>117000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>46</td>\n",
       "      <td>96000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>59</td>\n",
       "      <td>88000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>36</td>\n",
       "      <td>118000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>33</td>\n",
       "      <td>41000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>38</td>\n",
       "      <td>61000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>40</td>\n",
       "      <td>61000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>31</td>\n",
       "      <td>74000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>53</td>\n",
       "      <td>72000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        genero  idade  salario_estimado  comprado  feminino  masculino\n",
       "308  Masculino     36            125000         1       0.0        1.0\n",
       "182   Feminino     32            117000         1       1.0        0.0\n",
       "218   Feminino     46             96000         0       1.0        0.0\n",
       "280   Feminino     59             88000         1       1.0        0.0\n",
       "328  Masculino     36            118000         1       0.0        1.0\n",
       "187   Feminino     33             41000         0       1.0        0.0\n",
       "349  Masculino     38             61000         0       0.0        1.0\n",
       "269  Masculino     40             61000         0       0.0        1.0\n",
       "30   Masculino     31             74000         0       0.0        1.0\n",
       "346  Masculino     53             72000         1       0.0        1.0"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Análise das medidas estatísticas dos dados\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Função para gerar gráficos boxplot\n",
    "# param1: coluna do dataframe\n",
    "# param2: label para gráfico\n",
    "# param3: axis\n",
    "# param4: cor, defaut: 'yellowgreen'\n",
    "\n",
    "def graf_boxplot(param1, param2, param3, param4='yellowgreen'):\n",
    "    sns.boxplot(data=param1, ax=param3, color=param4, notch=True, orient='h')\n",
    "    param3.set(xlabel='', ylabel='')\n",
    "    param3.tick_params(labelsize=12)\n",
    "    param3.ticklabel_format(style='plain', axis='x')\n",
    "    param3.set_title(param2, loc='right', fontsize=12)\n",
    "\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(20, 7))\n",
    "\n",
    "graf_boxplot(df['idade'], 'Idade', ax1)\n",
    "graf_boxplot(df['salario_estimado'], 'Salário estimado', ax2)\n",
    "\n",
    "plt.subplots_adjust(top=1.9, bottom=0.95)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>idade</th>\n",
       "      <td>400.0</td>\n",
       "      <td>37.655</td>\n",
       "      <td>10.482877</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.75</td>\n",
       "      <td>37.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salario_estimado</th>\n",
       "      <td>400.0</td>\n",
       "      <td>69742.500</td>\n",
       "      <td>34096.960282</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>43000.00</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>88000.0</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count       mean           std      min       25%      50%  \\\n",
       "idade             400.0     37.655     10.482877     18.0     29.75     37.0   \n",
       "salario_estimado  400.0  69742.500  34096.960282  15000.0  43000.00  70000.0   \n",
       "\n",
       "                      75%       max  \n",
       "idade                46.0      60.0  \n",
       "salario_estimado  88000.0  150000.0  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descrição dos dados estatísticos\n",
    "\n",
    "df[['idade', 'salario_estimado']].describe().T\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Distribuição dos dados entre as variáveis independentes (features) e dependente (target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Função para gerar gráficos stripplot\n",
    "# param1: nome do dataframe\n",
    "# param2: variável do eixo X\n",
    "# param3: variável do eixo Y\n",
    "# param4: axis\n",
    "# param5: spec\n",
    "# param6: label para gráfico\n",
    "# param7: paleta de cores, default: Dark2\n",
    "\n",
    "comprado = df['comprado'].replace({1: 'Sim', 0: 'Não'})\n",
    "\n",
    "\n",
    "def graf_2_stripplot(param1, param2, param3, param4, param5, param6, param7='Dark2'):\n",
    "    param4 = fig.add_subplot(param5)\n",
    "    sns.stripplot(data=param1, x=param2, y=param3,\n",
    "                  ax=param4, hue=comprado, palette=param7, s=7, alpha=0.7)\n",
    "    param4.set(xlabel='', ylabel='')\n",
    "    param4.legend(title='Comprado\\n', fontsize=12, title_fontsize=12)\n",
    "    param4.tick_params(labelsize=12)\n",
    "    param4.ticklabel_format(style='plain', axis='y')\n",
    "    param4.set_title(param6, fontsize=15)\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(20, 10), constrained_layout=True)\n",
    "spec = fig.add_gridspec(2, 2)\n",
    "\n",
    "ax1 = ax2 = ax3 = None\n",
    "\n",
    "graf_2_stripplot(df, 'genero', 'idade', ax1, spec[0, 0],\n",
    "                 'Gênero x Idade')\n",
    "graf_2_stripplot(df, 'genero', 'salario_estimado', ax2, spec[0, 1],\n",
    "                 'Gênero x Salário estimado')\n",
    "graf_2_stripplot(df, 'idade', 'salario_estimado', ax3, spec[1, 0:],\n",
    "                 '\\nIdade x Salário estimado')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Função para gerar gráficos displot\n",
    "# param1: nome do dataframe\n",
    "# param2: variável do eixo X\n",
    "# param3: label para gráfico\n",
    "# param4: cor, default: royalblue\n",
    "\n",
    "def graf_displot(param1, param2, param3, param4='royalblue'):\n",
    "    plot = sns.displot(data=param1, x=param2, col=comprado,\n",
    "                       color=param4, kde=True, bins=20, hue=comprado, legend=False)\n",
    "    plot.tick_params(labelsize=12)\n",
    "    plot.set_axis_labels(param3, 'Contagem\\n', size=15)\n",
    "    plot.set_titles('Comprado: {col_name}', size=15)\n",
    "    plot.figure.set_size_inches(15, 5)\n",
    "\n",
    "\n",
    "graf_displot(df, 'idade', '\\nIdade')\n",
    "graf_displot(df, 'salario_estimado', '\\nSalário estimado')\n",
    "graf_displot(df, 'genero', '\\nGênero')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Correlação entre as variáveis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "corr = df[['idade', 'masculino', 'feminino',\n",
    "           'salario_estimado', 'comprado', ]].corr()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "plot = sns.heatmap(data=corr,\n",
    "                   annot=True, annot_kws={'size': 15, 'fontweight': 'bold'},\n",
    "                   cbar_kws={'extendfrac': .1, 'drawedges': True},\n",
    "                   cmap='seismic', vmin=-1, linewidths=.2,\n",
    "                   xticklabels=['idade', 'masculino', 'feminino',\n",
    "                                'salario_estimado', 'comprado'],\n",
    "                   yticklabels=['idade', 'masculino', 'feminino',\n",
    "                                'salario_estimado', 'comprado']\n",
    "                   )\n",
    "plot.set_yticklabels(plot.get_yticklabels(), rotation=0)\n",
    "plot.set_title('Correlação entre as variáveis\\n', loc='center', fontsize=15)\n",
    "plot.tick_params(labelsize=13)\n",
    "plot.xaxis.tick_top()\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Criação do modelo de regressão logística\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Separação dos dados de treino e teste\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Quantidade de dados divididos em treino e teste\n",
      "\n",
      "X_train: (300, 4)\n",
      "y_train: (300,)\n",
      "\n",
      "X_test: (100, 4)\n",
      "y_test: (100,)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# features, variáveis independentes\n",
    "X = df.drop(['comprado', 'genero'], 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['comprado'].copy()\n",
    "\n",
    "# definindo o percentual de dados para teste\n",
    "perc = 0.25\n",
    "\n",
    "# definindo o random_state\n",
    "seed = 10\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",
    "print('\\nX_train:', X_train.shape)\n",
    "print('y_train:', y_train.shape)\n",
    "print('\\nX_test:', X_test.shape)\n",
    "print('y_test:', y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Desbalanceado\n",
      "0    188\n",
      "1    112\n",
      "Name: comprado, dtype: int64\n",
      "\n",
      "Balanceado\n",
      "0    188\n",
      "1    188\n",
      "Name: comprado, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# aplicando balanceamento\n",
    "\n",
    "print(\"\\nDesbalanceado\")\n",
    "print(y_train.value_counts())\n",
    "\n",
    "sample = SMOTE(sampling_strategy='minority', random_state=42)\n",
    "\n",
    "X_train_b, y_train_b = sample.fit_resample(X_train, y_train)\n",
    "\n",
    "print(\"\\nBalanceado\")\n",
    "print(y_train_b.value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Modelo selecionado: LogisticRegression(solver='liblinear')\n"
     ]
    }
   ],
   "source": [
    "# definição do modelo a ser utilizado nos dados\n",
    "# o algoritmo 'liblinear' é utilizado para pequenos conjuntos de dados\n",
    "modelo_rl = LogisticRegression(solver='liblinear')\n",
    "print('\\nModelo selecionado:', modelo_rl)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Treinamento e predições do modelo\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# treinamento do modelo\n",
    "modelo_rl.fit(X_train_b, y_train_b)\n",
    "\n",
    "# predição com dados de teste\n",
    "pred_test = modelo_rl.predict(X_test)\n",
    "\n",
    "pred_test\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Métricas do modelo\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "O modelo obteve 90.00 de acurácia.\n"
     ]
    }
   ],
   "source": [
    "# Acurácia do modelo\n",
    "acuracia = accuracy_score(y_test, pred_test) * 100\n",
    "print('\\nO modelo obteve %0.2f de acurácia.' % acuracia)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Matriz de confusão\n",
      "+----+-----+-----+\n",
      "|    |   0 |   1 |\n",
      "+====+=====+=====+\n",
      "|  0 |  61 |   8 |\n",
      "+----+-----+-----+\n",
      "|  1 |   2 |  29 |\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 (1 - sim) e os verdadeiros negativos (0 - não)\n",
    "\n",
    "matrix = confusion_matrix(y_test, pred_test)\n",
    "\n",
    "arr = matrix.T\n",
    "\n",
    "data = {\n",
    "    '': ['0', '1'],\n",
    "    '0': [i for i in arr[0]],\n",
    "    '1': [i for i in arr[1]],\n",
    "}\n",
    "\n",
    "print('\\nMatriz de confusão')\n",
    "\n",
    "print(tabulate(data, headers='keys', tablefmt='grid'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.88      0.92        69\n",
      "           1       0.78      0.94      0.85        31\n",
      "\n",
      "    accuracy                           0.90       100\n",
      "   macro avg       0.88      0.91      0.89       100\n",
      "weighted avg       0.91      0.90      0.90       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# - Precision: percentual de acerto ao classificar uma predição em verdadeira positiva (1 - sim)\n",
    "#   em relação as predições verdadeiras e falsas positivas.\n",
    "# - Recall: percentual de acerto ao classificar uma predição em verdadeira positiva (1 - sim)\n",
    "#   em relação as predições verdadeiras positivas e falsas negativas.\n",
    "# - F1-score: média ponderada entre 'precision' e 'recall'.\n",
    "# - Support: número de classificações das predições.\n",
    "\n",
    "print('\\n', classification_report(y_test, pred_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculando as probabilidades com dados de treino e teste\n",
    "pred_test_prob = modelo_rl.predict_proba(X_test)[:, 1]\n",
    "pred_train_prob = modelo_rl.predict_proba(X_train)[:, 1]\n",
    "\n",
    "# Gera fpr (Taxa de falso positivo), tpr (Taxa de falso positivo) e thresholds\n",
    "fpr1, tpr1, thresholds1 = roc_curve(y_test, pred_test_prob)\n",
    "fpr2, tpr2, thresholds2 = roc_curve(y_train, pred_train_prob)\n",
    "\n",
    "# Gráfico da curva ROC\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.plot([0, 1], [0, 1], '--')\n",
    "plt.plot(fpr1, tpr1, label='TESTE')\n",
    "plt.plot(fpr2, tpr2, label='TREINO')\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.xlabel('\\nTaxa de falso positivo (TPR)', fontsize=13)\n",
    "plt.ylabel('Taxa de verdadeiro positivo (FPR)\\n', fontsize=13)\n",
    "plt.title('Curva ROC', fontsize=15)\n",
    "plt.legend(loc='center right', fontsize=13)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "AUC dados de teste:  0.9495091164095372\n",
      "\n",
      "AUC dados de treino: 0.9191441869300913\n"
     ]
    }
   ],
   "source": [
    "# Calculando o AUC (Área sob a curva ROC)\n",
    "auc_score_test = roc_auc_score(y_test, pred_test_prob)\n",
    "auc_score_train = roc_auc_score(y_train, pred_train_prob)\n",
    "\n",
    "print('\\nAUC dados de teste: ', auc_score_test)\n",
    "print('\\nAUC dados de treino:', auc_score_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Métrica do modelo utilizando Cross Validation\n",
      "\n",
      "Score de cada fold: [85.  87.5 87.5 80.  90.  85.  82.5 77.5 82.5 87.5]\n",
      "\n",
      "Score (média): 84.5\n"
     ]
    }
   ],
   "source": [
    "# definindo os valores dos hiperparâmetros\n",
    "\n",
    "# definindo o número de folds, número de divisões dos dados\n",
    "numero_folds = 10\n",
    "\n",
    "# definindo o random_state\n",
    "seed = 5\n",
    "\n",
    "# separando os dados em folds\n",
    "kfold = KFold(n_splits=numero_folds, shuffle=True, random_state=seed)\n",
    "\n",
    "# array de score de cada fold\n",
    "resultado = cross_val_score(modelo_rl, X, y, cv=kfold)\n",
    "\n",
    "print('\\nMétrica do modelo utilizando Cross Validation')\n",
    "print('\\nScore de cada fold:', resultado * 100)\n",
    "print('\\nScore (média):', resultado.mean() * 100)\n"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "### 4. Previsões com novos dados\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒════╤═════════╤═════════════╤════════════╤════════════════════╤════════════╕\n",
      "│    │   idade │   masculino │   feminino │   salario_estimado │   comprado │\n",
      "╞════╪═════════╪═════════════╪════════════╪════════════════════╪════════════╡\n",
      "│  0 │      19 │           0 │          1 │          130754.00 │          0 │\n",
      "├────┼─────────┼─────────────┼────────────┼────────────────────┼────────────┤\n",
      "│  1 │      26 │           0 │          1 │          148398.00 │          0 │\n",
      "├────┼─────────┼─────────────┼────────────┼────────────────────┼────────────┤\n",
      "│  2 │      19 │           1 │          0 │           42037.00 │          0 │\n",
      "├────┼─────────┼─────────────┼────────────┼────────────────────┼────────────┤\n",
      "│  3 │      35 │           1 │          0 │          102935.00 │          1 │\n",
      "├────┼─────────┼─────────────┼────────────┼────────────────────┼────────────┤\n",
      "│  4 │      41 │           1 │          0 │          102119.00 │          1 │\n",
      "├────┼─────────┼─────────────┼────────────┼────────────────────┼────────────┤\n",
      "│  5 │      33 │           0 │          1 │           43919.00 │          0 │\n",
      "├────┼─────────┼─────────────┼────────────┼────────────────────┼────────────┤\n",
      "│  6 │      58 │           0 │          1 │          141094.00 │          1 │\n",
      "├────┼─────────┼─────────────┼────────────┼────────────────────┼────────────┤\n",
      "│  7 │      34 │           0 │          1 │          111118.00 │          0 │\n",
      "├────┼─────────┼─────────────┼────────────┼────────────────────┼────────────┤\n",
      "│  8 │      36 │           0 │          1 │          114059.00 │          0 │\n",
      "├────┼─────────┼─────────────┼────────────┼────────────────────┼────────────┤\n",
      "│  9 │      50 │           0 │          1 │          133254.00 │          1 │\n",
      "╘════╧═════════╧═════════════╧════════════╧════════════════════╧════════════╛\n"
     ]
    }
   ],
   "source": [
    "# Função para testar o modelo com novos dados\n",
    "# param1: quantidade de entradas de novos dados\n",
    "\n",
    "def teste_modelo(param1):\n",
    "\n",
    "    arrx = []\n",
    "\n",
    "    for i in range(param1):\n",
    "        idade = np.random.randint(df['idade'].min(), df['idade'].max())\n",
    "        masculino = np.random.randint(2)\n",
    "        if masculino == 1:\n",
    "            feminino = 0\n",
    "        else:\n",
    "            feminino = 1\n",
    "        salario_estimado = np.random.randint(\n",
    "            df['salario_estimado'].min(), df['salario_estimado'].max())\n",
    "        arr = np.array([idade, masculino,\n",
    "                        feminino, salario_estimado])\n",
    "        arrx.append(arr)\n",
    "\n",
    "    novos_X = np.array(arrx)\n",
    "    std = StandardScaler().fit(novos_X)\n",
    "    novos_X = std.transform(novos_X)\n",
    "    novos_Y = modelo_rl.predict(novos_X)\n",
    "\n",
    "    df_tab = pd.DataFrame(arrx)\n",
    "    df_Y = pd.DataFrame(novos_Y)\n",
    "    df_tab[4] = df_Y[0]\n",
    "\n",
    "    print(tabulate(df_tab, headers=['idade', 'masculino',\n",
    "                                    'feminino', 'salario_estimado', 'comprado'],\n",
    "                   tablefmt='fancy_grid',\n",
    "                   floatfmt=('.0f', '.0f', '.0f', '.0f', '.2f', '.0f')))\n",
    "\n",
    "\n",
    "teste_modelo(10)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Conclusão\n",
    "\n",
    "Após efetuar ajustes nos dados, a fim de identificar padrões e correlações que auxiliaram na interpretação das informações sobre usuários que realizaram uma compra ou não, foi construído um modelo com **acurácia de 90%** e **pontuação média de 84,5%**. Foram executados testes com novas entradas de dados indicando clientes que potencialmente efetuariam uma compra (comprado: 1) estimulados por um anúncio específico, assim o modelo também irá permitir traçar perfis, baseado nas variáveis independentes, com maior probabilidade de efetuar uma compra e assim direcionar os anúncios para esse público específico."
   ]
  }
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