Mod07 - Tarefa 01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/rhatiro/Curso_EBAC-Profissao_Cientista_de_Dados/main/ebac-course-utils/media/logo/newebac_logo_black_half.png\" alt=\"ebac-logo\">\n",
    "\n",
    "---\n",
    "\n",
    "# **Profissão: Cientista de Dados**\n",
    "### **Módulo 7** | Exercício 1\n",
    "\n",
    "Aluno [Lays Félix](https://www.linkedin.com/in/laysfelixbusiness)<br>\n",
    "Data: 02 de janeiro de 2024\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importando bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos começar a mexer na nossa base de projeto? Já fizemos um exercício de montar a variável resposta, já pudemos perceber que essa atividade pode não ser trivial. Vamos agora trabalhar a base para que fique propícia ao *scikitlearn* para trabalharmos.\n",
    "\n",
    "Lembrando, a base se chama demo01.csv, e originalmente está publicada [aqui](https://www.kaggle.com/rikdifos/credit-card-approval-prediction)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1) Carregue a base e avalie:\n",
    "\n",
    "- As variáveis\n",
    "- Tipos de dados de cada variável\n",
    "- Quantidade de missings\n",
    "- Distribuição da variável resposta (mau)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sexo</th>\n",
       "      <th>posse_de_veiculo</th>\n",
       "      <th>posse_de_imovel</th>\n",
       "      <th>qtd_filhos</th>\n",
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       "      <th>tipo_residencia</th>\n",
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       "      <th>possui_fone</th>\n",
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       "      <th>0</th>\n",
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       "      <td>M</td>\n",
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       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>46.224658</td>\n",
       "      <td>2.106849</td>\n",
       "      <td>1</td>\n",
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       "      <td>F</td>\n",
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       "      <td>M</td>\n",
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       "      <td>F</td>\n",
       "      <td>N</td>\n",
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       "      <td>Pensioner</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>33.936986</td>\n",
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       "<p>16650 rows × 16 columns</p>\n",
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       "      sexo posse_de_veiculo posse_de_imovel  qtd_filhos            tipo_renda  \\\n",
       "0        M                Y               Y           0               Working   \n",
       "1        F                N               Y           0  Commercial associate   \n",
       "2        F                N               Y           0  Commercial associate   \n",
       "3        M                Y               Y           0               Working   \n",
       "4        F                Y               N           0               Working   \n",
       "...    ...              ...             ...         ...                   ...   \n",
       "16645    F                N               Y           0               Working   \n",
       "16646    F                N               Y           0  Commercial associate   \n",
       "16647    M                Y               Y           0               Working   \n",
       "16648    M                Y               Y           0               Working   \n",
       "16649    F                N               Y           0             Pensioner   \n",
       "\n",
       "                            educacao          estado_civil    tipo_residencia  \\\n",
       "0      Secondary / secondary special               Married  House / apartment   \n",
       "1      Secondary / secondary special  Single / not married  House / apartment   \n",
       "2      Secondary / secondary special  Single / not married  House / apartment   \n",
       "3                   Higher education               Married  House / apartment   \n",
       "4                  Incomplete higher               Married  House / apartment   \n",
       "...                              ...                   ...                ...   \n",
       "16645  Secondary / secondary special        Civil marriage  House / apartment   \n",
       "16646  Secondary / secondary special               Married  House / apartment   \n",
       "16647  Secondary / secondary special               Married  House / apartment   \n",
       "16648  Secondary / secondary special               Married  House / apartment   \n",
       "16649               Higher education               Married  House / apartment   \n",
       "\n",
       "           idade  tempo_emprego  possui_celular  possui_fone_comercial  \\\n",
       "0      58.832877       3.106849               1                      0   \n",
       "1      52.356164       8.358904               1                      0   \n",
       "2      52.356164       8.358904               1                      0   \n",
       "3      46.224658       2.106849               1                      1   \n",
       "4      29.230137       3.021918               1                      0   \n",
       "...          ...            ...             ...                    ...   \n",
       "16645  54.109589       9.884932               1                      0   \n",
       "16646  43.389041       7.380822               1                      1   \n",
       "16647  30.005479       9.800000               1                      1   \n",
       "16648  30.005479       9.800000               1                      1   \n",
       "16649  33.936986       3.630137               1                      0   \n",
       "\n",
       "       possui_fone  possui_email  qt_pessoas_residencia    mau  \n",
       "0                0             0                    2.0  False  \n",
       "1                1             1                    1.0  False  \n",
       "2                1             1                    1.0  False  \n",
       "3                1             1                    2.0  False  \n",
       "4                0             0                    2.0  False  \n",
       "...            ...           ...                    ...    ...  \n",
       "16645            0             0                    2.0   True  \n",
       "16646            1             0                    2.0   True  \n",
       "16647            0             0                    2.0   True  \n",
       "16648            0             0                    2.0   True  \n",
       "16649            1             1                    2.0   True  \n",
       "\n",
       "[16650 rows x 16 columns]"
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     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "# Carregando a base\n",
    "\n",
    "aval_cliente = pd.read_csv('demo01.csv')\n",
    "aval_cliente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16650 entries, 0 to 16649\n",
      "Data columns (total 16 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   sexo                   16650 non-null  object \n",
      " 1   posse_de_veiculo       16650 non-null  object \n",
      " 2   posse_de_imovel        16650 non-null  object \n",
      " 3   qtd_filhos             16650 non-null  int64  \n",
      " 4   tipo_renda             16650 non-null  object \n",
      " 5   educacao               16650 non-null  object \n",
      " 6   estado_civil           16650 non-null  object \n",
      " 7   tipo_residencia        16650 non-null  object \n",
      " 8   idade                  16650 non-null  float64\n",
      " 9   tempo_emprego          16650 non-null  float64\n",
      " 10  possui_celular         16650 non-null  int64  \n",
      " 11  possui_fone_comercial  16650 non-null  int64  \n",
      " 12  possui_fone            16650 non-null  int64  \n",
      " 13  possui_email           16650 non-null  int64  \n",
      " 14  qt_pessoas_residencia  16650 non-null  float64\n",
      " 15  mau                    16650 non-null  bool   \n",
      "dtypes: bool(1), float64(3), int64(5), object(7)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# Exibindo os tipos de dados de cada variável\n",
    "\n",
    "aval_cliente.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantidade de missings\n",
    "\n",
    "A coluna Non-Null Count exibida na execução acima mostra que não há dados ausentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    16260\n",
       "True       390\n",
       "Name: mau, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distribuição da variável resposta (mau)\n",
    "\n",
    "aval_cliente.mau.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) Vamos montar um metadados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.1. Crie um dataframe com os nomes de cada variável e o tipo de dados de cada variável."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>educacao</th>\n",
       "      <td>object</td>\n",
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       "      <th>estado_civil</th>\n",
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       "      <th>tempo_emprego</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>possui_celular</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>possui_email</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>qt_pessoas_residencia</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mau</th>\n",
       "      <td>bool</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                             0\n",
       "sexo                    object\n",
       "posse_de_veiculo        object\n",
       "posse_de_imovel         object\n",
       "qtd_filhos               int64\n",
       "tipo_renda              object\n",
       "educacao                object\n",
       "estado_civil            object\n",
       "tipo_residencia         object\n",
       "idade                  float64\n",
       "tempo_emprego          float64\n",
       "possui_celular           int64\n",
       "possui_fone_comercial    int64\n",
       "possui_fone              int64\n",
       "possui_email             int64\n",
       "qt_pessoas_residencia  float64\n",
       "mau                       bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colunas = list(aval_cliente.columns)\n",
    "tipos = list(aval_cliente.dtypes)\n",
    "dados_cliente = pd.DataFrame(tipos,colunas)\n",
    "dados_cliente"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2 Adicione uma coluna nesse *dataframe* chamada \"qtd_categorias\" e coloque nela o número de categorias correspondente de cada variável. <br>\n",
    "    Dica: <br>\n",
    "        1. inicie uma lista vazia<br>\n",
    "        2. faça um for ao longo dos nomes das variáveis, <br>\n",
    "        3. conte o número de categorias dessa variável <br>\n",
    "        4. acumule essa informação de 3. na lista que você criou em 1. <br>\n",
    "        5. No final, essa lista pode ser atribuída à nossa variável.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 2, 2, 8, 5, 5, 5, 6, 5298, 3005, 1, 2, 2, 2, 9, 2]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd_categorias = list(aval_cliente.nunique(axis=0))\n",
    "qtd_categorias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>0</th>\n",
       "      <th>qtd_categorias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sexo</th>\n",
       "      <td>object</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posse_de_veiculo</th>\n",
       "      <td>object</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posse_de_imovel</th>\n",
       "      <td>object</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qtd_filhos</th>\n",
       "      <td>int64</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tipo_renda</th>\n",
       "      <td>object</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educacao</th>\n",
       "      <td>object</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>estado_civil</th>\n",
       "      <td>object</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tipo_residencia</th>\n",
       "      <td>object</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>idade</th>\n",
       "      <td>float64</td>\n",
       "      <td>5298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tempo_emprego</th>\n",
       "      <td>float64</td>\n",
       "      <td>3005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>possui_celular</th>\n",
       "      <td>int64</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>possui_fone_comercial</th>\n",
       "      <td>int64</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>possui_fone</th>\n",
       "      <td>int64</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>possui_email</th>\n",
       "      <td>int64</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qt_pessoas_residencia</th>\n",
       "      <td>float64</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mau</th>\n",
       "      <td>bool</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0  qtd_categorias\n",
       "sexo                    object               2\n",
       "posse_de_veiculo        object               2\n",
       "posse_de_imovel         object               2\n",
       "qtd_filhos               int64               8\n",
       "tipo_renda              object               5\n",
       "educacao                object               5\n",
       "estado_civil            object               5\n",
       "tipo_residencia         object               6\n",
       "idade                  float64            5298\n",
       "tempo_emprego          float64            3005\n",
       "possui_celular           int64               1\n",
       "possui_fone_comercial    int64               2\n",
       "possui_fone              int64               2\n",
       "possui_email             int64               2\n",
       "qt_pessoas_residencia  float64               9\n",
       "mau                       bool               2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_cliente.insert(1,'qtd_categorias', qtd_categorias)\n",
    "dados_cliente"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.3. Crie variáveis dummy para as variáveis necessárias (i.e. aquelas que são qualitativas e não estão armazenadas como {0, 1} ou {True, False}."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>qtd_filhos</th>\n",
       "      <th>idade</th>\n",
       "      <th>tempo_emprego</th>\n",
       "      <th>possui_celular</th>\n",
       "      <th>possui_fone_comercial</th>\n",
       "      <th>possui_fone</th>\n",
       "      <th>possui_email</th>\n",
       "      <th>qt_pessoas_residencia</th>\n",
       "      <th>mau</th>\n",
       "      <th>sexo_M</th>\n",
       "      <th>...</th>\n",
       "      <th>educacao_Secondary / secondary special</th>\n",
       "      <th>estado_civil_Married</th>\n",
       "      <th>estado_civil_Separated</th>\n",
       "      <th>estado_civil_Single / not married</th>\n",
       "      <th>estado_civil_Widow</th>\n",
       "      <th>tipo_residencia_House / apartment</th>\n",
       "      <th>tipo_residencia_Municipal apartment</th>\n",
       "      <th>tipo_residencia_Office apartment</th>\n",
       "      <th>tipo_residencia_Rented apartment</th>\n",
       "      <th>tipo_residencia_With parents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>58.832877</td>\n",
       "      <td>3.106849</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>52.356164</td>\n",
       "      <td>8.358904</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>52.356164</td>\n",
       "      <td>8.358904</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>46.224658</td>\n",
       "      <td>2.106849</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>29.230137</td>\n",
       "      <td>3.021918</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   qtd_filhos      idade  tempo_emprego  possui_celular  \\\n",
       "0           0  58.832877       3.106849               1   \n",
       "1           0  52.356164       8.358904               1   \n",
       "2           0  52.356164       8.358904               1   \n",
       "3           0  46.224658       2.106849               1   \n",
       "4           0  29.230137       3.021918               1   \n",
       "\n",
       "   possui_fone_comercial  possui_fone  possui_email  qt_pessoas_residencia  \\\n",
       "0                      0            0             0                    2.0   \n",
       "1                      0            1             1                    1.0   \n",
       "2                      0            1             1                    1.0   \n",
       "3                      1            1             1                    2.0   \n",
       "4                      0            0             0                    2.0   \n",
       "\n",
       "     mau  sexo_M  ...  educacao_Secondary / secondary special  \\\n",
       "0  False       1  ...                                       1   \n",
       "1  False       0  ...                                       1   \n",
       "2  False       0  ...                                       1   \n",
       "3  False       1  ...                                       0   \n",
       "4  False       0  ...                                       0   \n",
       "\n",
       "   estado_civil_Married  estado_civil_Separated  \\\n",
       "0                     1                       0   \n",
       "1                     0                       0   \n",
       "2                     0                       0   \n",
       "3                     1                       0   \n",
       "4                     1                       0   \n",
       "\n",
       "   estado_civil_Single / not married  estado_civil_Widow  \\\n",
       "0                                  0                   0   \n",
       "1                                  1                   0   \n",
       "2                                  1                   0   \n",
       "3                                  0                   0   \n",
       "4                                  0                   0   \n",
       "\n",
       "   tipo_residencia_House / apartment  tipo_residencia_Municipal apartment  \\\n",
       "0                                  1                                    0   \n",
       "1                                  1                                    0   \n",
       "2                                  1                                    0   \n",
       "3                                  1                                    0   \n",
       "4                                  1                                    0   \n",
       "\n",
       "   tipo_residencia_Office apartment  tipo_residencia_Rented apartment  \\\n",
       "0                                 0                                 0   \n",
       "1                                 0                                 0   \n",
       "2                                 0                                 0   \n",
       "3                                 0                                 0   \n",
       "4                                 0                                 0   \n",
       "\n",
       "   tipo_residencia_With parents  \n",
       "0                             0  \n",
       "1                             0  \n",
       "2                             0  \n",
       "3                             0  \n",
       "4                             0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_cliente= pd.get_dummies(aval_cliente, columns=['sexo', 'posse_de_veiculo', 'posse_de_imovel',\n",
    "       'tipo_renda', 'educacao', 'estado_civil', 'tipo_residencia'], drop_first=True)\n",
    "dados_cliente.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3) Crie variáveis dummy para as variáveis necessárias (i.e. aquelas que são qualitativas e não estão armazenadas como {0, 1} ou {True, False}. Crie um *dataframe* apenas com as variáveis apropriadas para entrada no scikitlearn - elimine as variáveis tipo *str*, mantendo apenas suas versões *dummy*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16650 entries, 0 to 16649\n",
      "Data columns (total 29 columns):\n",
      " #   Column                                  Non-Null Count  Dtype  \n",
      "---  ------                                  --------------  -----  \n",
      " 0   qtd_filhos                              16650 non-null  int64  \n",
      " 1   idade                                   16650 non-null  float64\n",
      " 2   tempo_emprego                           16650 non-null  float64\n",
      " 3   possui_celular                          16650 non-null  int64  \n",
      " 4   possui_fone_comercial                   16650 non-null  int64  \n",
      " 5   possui_fone                             16650 non-null  int64  \n",
      " 6   possui_email                            16650 non-null  int64  \n",
      " 7   qt_pessoas_residencia                   16650 non-null  float64\n",
      " 8   mau                                     16650 non-null  bool   \n",
      " 9   sexo_M                                  16650 non-null  uint8  \n",
      " 10  posse_de_veiculo_Y                      16650 non-null  uint8  \n",
      " 11  posse_de_imovel_Y                       16650 non-null  uint8  \n",
      " 12  tipo_renda_Pensioner                    16650 non-null  uint8  \n",
      " 13  tipo_renda_State servant                16650 non-null  uint8  \n",
      " 14  tipo_renda_Student                      16650 non-null  uint8  \n",
      " 15  tipo_renda_Working                      16650 non-null  uint8  \n",
      " 16  educacao_Higher education               16650 non-null  uint8  \n",
      " 17  educacao_Incomplete higher              16650 non-null  uint8  \n",
      " 18  educacao_Lower secondary                16650 non-null  uint8  \n",
      " 19  educacao_Secondary / secondary special  16650 non-null  uint8  \n",
      " 20  estado_civil_Married                    16650 non-null  uint8  \n",
      " 21  estado_civil_Separated                  16650 non-null  uint8  \n",
      " 22  estado_civil_Single / not married       16650 non-null  uint8  \n",
      " 23  estado_civil_Widow                      16650 non-null  uint8  \n",
      " 24  tipo_residencia_House / apartment       16650 non-null  uint8  \n",
      " 25  tipo_residencia_Municipal apartment     16650 non-null  uint8  \n",
      " 26  tipo_residencia_Office apartment        16650 non-null  uint8  \n",
      " 27  tipo_residencia_Rented apartment        16650 non-null  uint8  \n",
      " 28  tipo_residencia_With parents            16650 non-null  uint8  \n",
      "dtypes: bool(1), float64(3), int64(5), uint8(20)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "# Dataframe criado acima\n",
    "\n",
    "dados_cliente.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4) Qual variável é mais poderosa?\n",
    "\n",
    "Considere as variáveis ```possui_email``` e ```posse_de_veiculo```. Faça uma tabela cruzada entre elas e responda qual delas te parece mais poderosa para prever a probabilidade de ```mau = 1```?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_cliente.mau.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_cliente.possui_email.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=uint8)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_cliente.posse_de_veiculo_Y.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>posse_de_veiculo_Y</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mau</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>0.610763</td>\n",
       "      <td>0.389237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.366667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "posse_de_veiculo_Y         0         1\n",
       "mau                                   \n",
       "False               0.610763  0.389237\n",
       "True                0.633333  0.366667"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Tabela cruzada das variáveis mau pagador e possui veículo\n",
    "\n",
    "mau_veiculo = pd.crosstab(dados_cliente['mau'],dados_cliente['posse_de_veiculo_Y'])\n",
    "mau_veiculo = mau_veiculo.div(mau_veiculo.sum(axis=1),axis=0)\n",
    "mau_veiculo\n",
    "\n",
    "#cliente_avaliacao = pd.crosstab(dados_cliente['mau'], dados_cliente['posse_de_veiculo_Y'])\n",
    "#cliente_avaliacao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>possui_email</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mau</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>0.910824</td>\n",
       "      <td>0.089176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "possui_email         0         1\n",
       "mau                             \n",
       "False         0.910824  0.089176\n",
       "True          0.923077  0.076923"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Tabela cruzada das variáveis mau pagador e possui e-mail\n",
    "\n",
    "mau_email = pd.crosstab(dados_cliente['mau'],dados_cliente['possui_email'])\n",
    "mau_email = mau_email.div(mau_email.sum(axis=1),axis=0)\n",
    "mau_email\n",
    "\n",
    "#mau_email = pd.crosstab(dados_cliente['mau'], dados_cliente['possui_email'])\n",
    "#mau_email"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A variável <b> possui veículo </b> é ideal para prever a probabilidade de mau pagador, pois ter um veículo gera custos e despesas não organizadas torna o cliente inadimplente. <br>\n",
    "Grande parte dos maus pagadores não tem <b> e-mail </b> cadastrado. O que dificultaria num contato ao cliente por e-mail.<br>\n",
    "Analisando a variável <b> possui veículo</b>, verificamos que 63% dos inadimplentes não possui veículo.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5) Salve a base, pois ela será utilizada no final deste módulo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_cliente.to_csv('dados_cliente.csv')"
   ]
  }
 ],
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