previsao-avc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Science Aplicada à Área de Saúde"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Projeto de análise de dados da formação FIAMED da [DSA](https://www.datascienceacademy.com.br/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulando Dados Para Previsão de AVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O Acidente Vascular Cerebral (AVC) acontece quando vasos que levam sangue ao cérebro entopem ou se rompem, provocando a paralisia da área cerebral que ficou sem circulação sanguínea. É uma doença que acomete mais os homens e é uma das principais causas de morte, incapacitação e internações em todo o mundo.\n",
    "\n",
    "Quanto mais rápido for o diagnóstico e o tratamento do AVC, maiores serão as chances de recuperação completa. Desta forma, torna-se primordial ficar atento aos sinais e sintomas e procurar atendimento médico imediato.\n",
    "\n",
    "Existem dois tipos de AVC, que ocorrem por motivos diferentes: AVC hemorrágico e AVC isquêmico."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_O que é um AVC isquêmico_\n",
    "\n",
    "O AVC isquêmico ocorre quando há obstrução de uma artéria, impedindo a passagem de oxigênio para células cerebrais, que acabam morrendo. Essa obstrução pode acontecer devido a um trombo (trombose) ou a um êmbolo (embolia). O AVC isquêmico é o mais comum e representa 85% de todos os casos.\n",
    "\n",
    "_O que é um AVC hemorrágico_\n",
    "\n",
    "O AVC hemorrágico ocorre quando há rompimento de um vaso cerebral, provocando hemorragia. Esta hemorragia pode acontecer dentro do tecido cerebral ou na superfície entre o cérebro e a meninge. É responsável por 15% de todos os casos de AVC, mas pode causar a morte com mais frequência do que o AVC isquêmico."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Quais os principais fatores de risco para desenvolver um AVC?_\n",
    "\n",
    "Existem diversos fatores que aumentam a probabilidade de ocorrência de um AVC, seja ele hemorrágico ou isquêmico. Os principais fatores causais das doenças são:\n",
    "\n",
    "- Hipertensão;\n",
    "- Diabetes tipo 2;\n",
    "- Colesterol alto;\n",
    "- Sobrepeso;\n",
    "- Obesidade;\n",
    "- Tabagismo;\n",
    "- Uso excessivo de álcool;\n",
    "- Idade avançada;\n",
    "- Sedentarismo;\n",
    "- Uso de drogas ilícitas;\n",
    "- Histórico familiar;\n",
    "- Ser do sexo masculino."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O AVC está altamente relacionado à idade, hipertensão, nível médio de glicose, status de tabagismo e IMC, enquanto isso depende parcialmente do sexo, doença cardíaca, tipo de trabalho e tipo de residência."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Carregando Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importar pacotes\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carregando os dados\n",
    "dataset = pd.read_csv('dados/dataset-data-munging-01.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>id</th>\n",
       "      <th>sexo</th>\n",
       "      <th>idade</th>\n",
       "      <th>hipertensao</th>\n",
       "      <th>doenca_coracao</th>\n",
       "      <th>casado</th>\n",
       "      <th>tipo_trabalho</th>\n",
       "      <th>tipo_residencia</th>\n",
       "      <th>media_nivel_glicose</th>\n",
       "      <th>imc</th>\n",
       "      <th>status_tabagismo</th>\n",
       "      <th>avc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30669</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>crianca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>95.12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30468</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>87.96</td>\n",
       "      <td>39.2</td>\n",
       "      <td>nunca fumou</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16523</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>110.89</td>\n",
       "      <td>17.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56543</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Rural</td>\n",
       "      <td>69.04</td>\n",
       "      <td>35.9</td>\n",
       "      <td>fuma eventualmente</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46136</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Nunca_trabalhou</td>\n",
       "      <td>Rural</td>\n",
       "      <td>161.28</td>\n",
       "      <td>19.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id       sexo  idade  hipertensao  doenca_coracao casado  \\\n",
       "0  30669  Masculino    3.0            0               0     No   \n",
       "1  30468  Masculino   58.0            1               0    Yes   \n",
       "2  16523   Feminino    8.0            0               0     No   \n",
       "3  56543   Feminino   70.0            0               0    Yes   \n",
       "4  46136  Masculino   14.0            0               0     No   \n",
       "\n",
       "     tipo_trabalho tipo_residencia  media_nivel_glicose   imc  \\\n",
       "0          crianca           Rural                95.12  18.0   \n",
       "1          Privado          Urbana                87.96  39.2   \n",
       "2          Privado          Urbana               110.89  17.6   \n",
       "3          Privado           Rural                69.04  35.9   \n",
       "4  Nunca_trabalhou           Rural               161.28  19.1   \n",
       "\n",
       "     status_tabagismo  avc  \n",
       "0                 NaN    0  \n",
       "1         nunca fumou    0  \n",
       "2                 NaN    0  \n",
       "3  fuma eventualmente    0  \n",
       "4                 NaN    0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando os primriros registros\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de linhas:\t43400\n",
      "Número de colunas:\t12\n"
     ]
    }
   ],
   "source": [
    "# Formato dos dados\n",
    "print(f'Número de linhas:\\t{dataset.shape[0]}')\n",
    "print(f'Número de colunas:\\t{dataset.shape[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                       int64\n",
       "sexo                    object\n",
       "idade                  float64\n",
       "hipertensao              int64\n",
       "doenca_coracao           int64\n",
       "casado                  object\n",
       "tipo_trabalho           object\n",
       "tipo_residencia         object\n",
       "media_nivel_glicose    float64\n",
       "imc                    float64\n",
       "status_tabagismo        object\n",
       "avc                      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tipos de dados\n",
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pessoas que tiveram AVC:\n",
      "freq: 783\tProp: 0.018\n",
      "\n",
      "Pessoas que não tiveram AVC:\n",
      "freq: 42617\tProp: 0.982\n"
     ]
    }
   ],
   "source": [
    "# Análise da variável target\n",
    "print(f'Pessoas que tiveram AVC:\\nfreq: {dataset.avc.sum()}\\tProp: {dataset.avc.mean():.3f}')\n",
    "print(f'\\nPessoas que não tiveram AVC:\\nfreq: {dataset.shape[0] - dataset.avc.sum()}\\tProp: {1 - dataset.avc.mean():.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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30VnI5r5zAdLabOk6b+LPkiRdZNOGQVX9LXBqQnkDsKtt7wJu66o/Xh3P0lnsfgmwDjhQVaeq6jRwABhuxz5eVT9s6yE/3nUtSdIcme0zg+uq6k2A9v7JVl8KHO9qN9ZqU9XHetQlSXPoQj9A7jXeX7Oo9754siXJSJKR8fHxWXZRkjTRbMPgrTbEQ3s/2epjwPKudsuAE9PUl/Wo91RVO6pqqKqGBgYGZtl1SdJEsw2DvcC5GUGbgCe76ne2WUVrgHfaMNJ+YG2Sxe3B8Vpgfzv2bpI1bRbRnV3XkiTNkYXTNUjybeCLwLVJxujMCrof2JNkM/Aj4PbWfB9wKzAK/BS4C6CqTiX5BnCwtft6VZ17KP0VOjOWrgK+116SpDk0bRhU1R2THLqlR9sCtk5ynZ3Azh71EeAz0/VDknTx+A1kSZJhIEkyDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAksR5hkGSN5K8lORQkpFWuybJgSRH2/viVk+Sh5KMJjmc5HNd19nU2h9NsmmynydJujguxJ3Bf6yq1VU11Pa3AU9V1SDwVNsHWA8MttcW4BHohAed1dNuAm4E7jsXIJKkuXExhok2ALva9i7gtq7649XxLHB1kiXAOuBAVZ2qqtPAAWD4IvRLkjSJ8w2DAv5nkheSbGm169pC97T3T7b6UuB417ljrTZZXZI0R6ZdA3kaX6iqE0k+CRxI8g9TtE2PWk1R/+AFOoGzBeD666+faV8lSZM4rzuDqjrR3k8Cf0VnzP+tNvxDez/Zmo8By7tOXwacmKLe6+ftqKqhqhoaGBg4n65LkrrMOgyS/Jsk//bcNrAWeBnYC5ybEbQJeLJt7wXubLOK1gDvtGGk/cDaJIvbg+O1rSZJmiPnM0x0HfBXSc5d539U1d8kOQjsSbIZ+BFwe2u/D7gVGAV+CtwFUFWnknwDONjafb2qTp1HvyRJMzTrMKiqY8Cv9qj/X+CWHvUCtk5yrZ3Aztn2RZJ0fvwGsiTJMJAkGQaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkcX4rnV1QSYaB/wYsAP60qu6f5y5JH2krtv31fHfhI+WN+7803104L5fEnUGSBcDDwHpgFXBHklXz2ytJunxcEmEA3AiMVtWxqnof2A1smOc+SdJl41IZJloKHO/aHwNumtgoyRZgS9v9SZLX5qBvl4NrgR/PdyemkwfmuweaJ/77vLD+fa/ipRIG6VGrDxSqdgA7Ln53Li9JRqpqaL77IfXiv8+5cakME40By7v2lwEn5qkvknTZuVTC4CAwmGRlkkXARmDvPPdJki4bl8QwUVWdSXI3sJ/O1NKdVXVknrt1OXHoTZcy/33OgVR9YGheknSZuVSGiSRJ88gwkCQZBpKkS+QBsuZWkl+h8w3vpXS+z3EC2FtVr85rxyTNG+8MLjNJ7qHz5z4CPE9nWm+AbyfZNp99k6aS5K757sNHmbOJLjNJ/jdwQ1X964T6IuBIVQ3OT8+kqSX5UVVdP9/9+KhymOjy8zPg3wH/OKG+pB2T5k2Sw5MdAq6by75cbgyDy8/XgKeSHOXnfxzweuBTwN3z1iup4zpgHXB6Qj3A/5r77lw+DIPLTFX9TZL/QOfPhi+l8x/ZGHCwqs7Oa+ck+C7wsao6NPFAkmfmvjuXD58ZSJKcTSRJMgwkSRgGkiQMA0kShoEkCfh/3tv1w+wWZkgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizar balanceamento\n",
    "dataset.avc.value_counts().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identificando e Removendo Colunas Irrelevantes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O AVC está altamente relacionado à idade, hipertensão, nível médio de glicose, status de tabagismo e IMC, enquanto isso depende parcialmente do sexo, doença cardíaca, tipo de trabalho e tipo de residência."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excluímos a coluna 'id' porque esse é apenas um número arbitrário a cada paciente,\n",
    "# sem relevância com AVC\n",
    "del dataset['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excluímos a coluna 'casado', já que não parece ser um bom identificador para prever um AVC\n",
    "del dataset['casado']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>idade</th>\n",
       "      <th>hipertensao</th>\n",
       "      <th>doenca_coracao</th>\n",
       "      <th>media_nivel_glicose</th>\n",
       "      <th>imc</th>\n",
       "      <th>avc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>43400.000000</td>\n",
       "      <td>43400.000000</td>\n",
       "      <td>43400.000000</td>\n",
       "      <td>43400.000000</td>\n",
       "      <td>41938.000000</td>\n",
       "      <td>43400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.217894</td>\n",
       "      <td>0.093571</td>\n",
       "      <td>0.047512</td>\n",
       "      <td>104.482750</td>\n",
       "      <td>28.605038</td>\n",
       "      <td>0.018041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.519649</td>\n",
       "      <td>0.291235</td>\n",
       "      <td>0.212733</td>\n",
       "      <td>43.111751</td>\n",
       "      <td>7.770020</td>\n",
       "      <td>0.133103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.540000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.580000</td>\n",
       "      <td>27.700000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112.070000</td>\n",
       "      <td>32.900000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>291.050000</td>\n",
       "      <td>97.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              idade   hipertensao  doenca_coracao  media_nivel_glicose  \\\n",
       "count  43400.000000  43400.000000    43400.000000         43400.000000   \n",
       "mean      42.217894      0.093571        0.047512           104.482750   \n",
       "std       22.519649      0.291235        0.212733            43.111751   \n",
       "min        0.080000      0.000000        0.000000            55.000000   \n",
       "25%       24.000000      0.000000        0.000000            77.540000   \n",
       "50%       44.000000      0.000000        0.000000            91.580000   \n",
       "75%       60.000000      0.000000        0.000000           112.070000   \n",
       "max       82.000000      1.000000        1.000000           291.050000   \n",
       "\n",
       "                imc           avc  \n",
       "count  41938.000000  43400.000000  \n",
       "mean      28.605038      0.018041  \n",
       "std        7.770020      0.133103  \n",
       "min       10.100000      0.000000  \n",
       "25%       23.200000      0.000000  \n",
       "50%       27.700000      0.000000  \n",
       "75%       32.900000      0.000000  \n",
       "max       97.600000      1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coletando estatísticas das colunas\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sexo', 'tipo_trabalho', 'tipo_residencia', 'status_tabagismo'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extraindo variáveis do tipo categóricas \n",
    "dfcatname = dataset.dtypes[dataset.dtypes == 'object'].index\n",
    "dfcatname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['idade', 'hipertensao', 'doenca_coracao', 'media_nivel_glicose', 'imc', 'avc']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extraindo variáveis do tipo numéricas\n",
    "dfconname = [item for item in list(dataset.columns) if item not in list(dfcatname)]\n",
    "dfconname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sexo</th>\n",
       "      <th>tipo_trabalho</th>\n",
       "      <th>tipo_residencia</th>\n",
       "      <th>status_tabagismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>43400</td>\n",
       "      <td>43400</td>\n",
       "      <td>43400</td>\n",
       "      <td>30108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>nunca fumou</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>25665</td>\n",
       "      <td>24834</td>\n",
       "      <td>21756</td>\n",
       "      <td>16053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sexo tipo_trabalho tipo_residencia status_tabagismo\n",
       "count      43400         43400           43400            30108\n",
       "unique         3             5               2                3\n",
       "top     Feminino       Privado          Urbana      nunca fumou\n",
       "freq       25665         24834           21756            16053"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descrevendo as variávies categóricas \n",
    "dataset[dfcatname].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tratamento de Valores Missing no IMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    41938.000000\n",
       "mean        28.605038\n",
       "std          7.770020\n",
       "min         10.100000\n",
       "25%         23.200000\n",
       "50%         27.700000\n",
       "75%         32.900000\n",
       "max         97.600000\n",
       "Name: imc, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descrevendo a coluna IMC\n",
    "dataset['imc'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1462"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checando valores missing\n",
    "dataset['imc'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma maneira de tratar um valor ausente é simplesmente remover o registro completo, e em nosso exemplo teríamos que remover 1462/43400 o que é um número relativamente pequeno."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outra solução quando trabalhamos com variáveis numéricas é imputar zero, valores centrais ou estatisticamente estimados, no lugar de NAN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para verificar se podemos atribuir valor central, vamos plotar o histograma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.hist(column = 'imc', figsize=(9,6), bins=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vê-se no histograma que o valor de IMC entre 20 e 40 é o mais comum, portanto, não é totalmente irracional utilizá-lo no lugar de valores missing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos imputar a tendência central (média) no lugar de valores ausentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gera o novo imc\n",
    "novo_imc = np.where(dataset['imc'].isnull(), dataset['imc'].mean(), dataset['imc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18. , 39.2, 17.6, ..., 28.9, 33.2, 20.6])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "novo_imc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grava o novo valor do imc no dataset\n",
    "dataset['imc'] = novo_imc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    43400.000000\n",
       "mean        28.605038\n",
       "std          7.638023\n",
       "min         10.100000\n",
       "25%         23.400000\n",
       "50%         28.100000\n",
       "75%         32.600000\n",
       "max         97.600000\n",
       "Name: imc, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['imc'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checando o histograma\n",
    "dataset.hist(column = 'imc', figsize=(9,6), bins=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tratamento de Valores Missing no Status de Tabagismo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>sexo</th>\n",
       "      <th>tipo_trabalho</th>\n",
       "      <th>tipo_residencia</th>\n",
       "      <th>status_tabagismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>43400</td>\n",
       "      <td>43400</td>\n",
       "      <td>43400</td>\n",
       "      <td>30108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>nunca fumou</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>25665</td>\n",
       "      <td>24834</td>\n",
       "      <td>21756</td>\n",
       "      <td>16053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sexo tipo_trabalho tipo_residencia status_tabagismo\n",
       "count      43400         43400           43400            30108\n",
       "unique         3             5               2                3\n",
       "top     Feminino       Privado          Urbana      nunca fumou\n",
       "freq       25665         24834           21756            16053"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descrevendo as variáveis categóricas\n",
    "dataset[dfcatname].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count           30108\n",
       "unique              3\n",
       "top       nunca fumou\n",
       "freq            16053\n",
       "Name: status_tabagismo, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['status_tabagismo'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13292"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['status_tabagismo'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['status_tabagismo'].value_counts().plot(kind='barh');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos preencher os valores missing com uma nova categoria chamada 'desconhecido'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "novo_status_tabagismo = np.where(dataset['status_tabagismo'].isnull(), \"desconhecido\", dataset['status_tabagismo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['status_tabagismo'] = novo_status_tabagismo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['status_tabagismo'].value_counts().plot(kind='barh');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sexo                   43400\n",
       "idade                  43400\n",
       "hipertensao            43400\n",
       "doenca_coracao         43400\n",
       "tipo_trabalho          43400\n",
       "tipo_residencia        43400\n",
       "media_nivel_glicose    43400\n",
       "imc                    43400\n",
       "status_tabagismo       43400\n",
       "avc                    43400\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tratamento de Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idade</th>\n",
       "      <th>hipertensao</th>\n",
       "      <th>doenca_coracao</th>\n",
       "      <th>media_nivel_glicose</th>\n",
       "      <th>imc</th>\n",
       "      <th>avc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>95.12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>87.96</td>\n",
       "      <td>39.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110.89</td>\n",
       "      <td>17.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69.04</td>\n",
       "      <td>35.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>161.28</td>\n",
       "      <td>19.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43395</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58.64</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43396</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>213.61</td>\n",
       "      <td>55.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43397</th>\n",
       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>91.94</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43398</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.16</td>\n",
       "      <td>33.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43399</th>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79.48</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43400 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       idade  hipertensao  doenca_coracao  media_nivel_glicose   imc  avc\n",
       "0        3.0            0               0                95.12  18.0    0\n",
       "1       58.0            1               0                87.96  39.2    0\n",
       "2        8.0            0               0               110.89  17.6    0\n",
       "3       70.0            0               0                69.04  35.9    0\n",
       "4       14.0            0               0               161.28  19.1    0\n",
       "...      ...          ...             ...                  ...   ...  ...\n",
       "43395   10.0            0               0                58.64  20.4    0\n",
       "43396   56.0            0               0               213.61  55.4    0\n",
       "43397   82.0            1               0                91.94  28.9    0\n",
       "43398   40.0            0               0                99.16  33.2    0\n",
       "43399   82.0            0               0                79.48  20.6    0\n",
       "\n",
       "[43400 rows x 6 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dfconname]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    43400.000000\n",
       "mean        42.217894\n",
       "std         22.519649\n",
       "min          0.080000\n",
       "25%         24.000000\n",
       "50%         44.000000\n",
       "75%         60.000000\n",
       "max         82.000000\n",
       "Name: idade, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dfconname[0]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    43400.000000\n",
       "mean       104.482750\n",
       "std         43.111751\n",
       "min         55.000000\n",
       "25%         77.540000\n",
       "50%         91.580000\n",
       "75%        112.070000\n",
       "max        291.050000\n",
       "Name: media_nivel_glicose, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dfconname[3]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    43400.000000\n",
       "mean        28.605038\n",
       "std          7.638023\n",
       "min         10.100000\n",
       "25%         23.400000\n",
       "50%         28.100000\n",
       "75%         32.600000\n",
       "max         97.600000\n",
       "Name: imc, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dfconname[4]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Verificando outliers para recursos numéricos em nosso conjunto de dados.\n",
    "# Variável media_nivel_glicose\n",
    "dataset[dfconname[3]].plot(kind='box', figsize=(9,9));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removendo outliers\n",
    "index1 = np.where(dataset['media_nivel_glicose'] == max(dataset['media_nivel_glicose']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sexo</th>\n",
       "      <th>idade</th>\n",
       "      <th>hipertensao</th>\n",
       "      <th>doenca_coracao</th>\n",
       "      <th>tipo_trabalho</th>\n",
       "      <th>tipo_residencia</th>\n",
       "      <th>media_nivel_glicose</th>\n",
       "      <th>imc</th>\n",
       "      <th>status_tabagismo</th>\n",
       "      <th>avc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38446</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Rural</td>\n",
       "      <td>291.05</td>\n",
       "      <td>28.7</td>\n",
       "      <td>nunca fumou</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sexo  idade  hipertensao  doenca_coracao tipo_trabalho  \\\n",
       "38446  Feminino   80.0            0               0       Privado   \n",
       "\n",
       "      tipo_residencia  media_nivel_glicose   imc status_tabagismo  avc  \n",
       "38446           Rural               291.05  28.7      nunca fumou    0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.loc[index1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.drop(index1[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Verificando outliers para recursos numéricos em nosso conjunto de dados.\n",
    "dataset[dfconname[3]].plot(kind='box', figsize=(9,9));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Verificando outliers para recursos numéricos em nosso conjunto de dados.\n",
    "# Variável imc\n",
    "dataset[dfconname[4]].plot(kind='box', figsize=(9,9));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "index2 = np.where(dataset['imc'] == max(dataset['imc']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>sexo</th>\n",
       "      <th>idade</th>\n",
       "      <th>hipertensao</th>\n",
       "      <th>doenca_coracao</th>\n",
       "      <th>tipo_trabalho</th>\n",
       "      <th>tipo_residencia</th>\n",
       "      <th>media_nivel_glicose</th>\n",
       "      <th>imc</th>\n",
       "      <th>status_tabagismo</th>\n",
       "      <th>avc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Rural</td>\n",
       "      <td>61.67</td>\n",
       "      <td>97.6</td>\n",
       "      <td>desconhecido</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sexo  idade  hipertensao  doenca_coracao tipo_trabalho  \\\n",
       "1913  Masculino   17.0            1               0       Privado   \n",
       "\n",
       "     tipo_residencia  media_nivel_glicose   imc status_tabagismo  avc  \n",
       "1913           Rural                61.67  97.6     desconhecido    0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.loc[index2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O valor do IMC pode chegar até aproximadamente 54, o que também é um caso muito raro, mas nosso conjunto de dados tem alguns valores maiores que 54, que também atuam apenas como outliers, eu prefiro removê-los mantendo a margem. Isso não afetará os dados, e os manterão realistas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset[dataset['imc'] < 60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sexo                   43309\n",
       "idade                  43309\n",
       "hipertensao            43309\n",
       "doenca_coracao         43309\n",
       "tipo_trabalho          43309\n",
       "tipo_residencia        43309\n",
       "media_nivel_glicose    43309\n",
       "imc                    43309\n",
       "status_tabagismo       43309\n",
       "avc                    43309\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idade</th>\n",
       "      <th>hipertensao</th>\n",
       "      <th>doenca_coracao</th>\n",
       "      <th>media_nivel_glicose</th>\n",
       "      <th>imc</th>\n",
       "      <th>avc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.00000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.213087</td>\n",
       "      <td>0.09326</td>\n",
       "      <td>0.047542</td>\n",
       "      <td>104.442117</td>\n",
       "      <td>28.526248</td>\n",
       "      <td>0.018079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.533671</td>\n",
       "      <td>0.29080</td>\n",
       "      <td>0.212798</td>\n",
       "      <td>43.060134</td>\n",
       "      <td>7.438084</td>\n",
       "      <td>0.133240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.540000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.570000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112.030000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>281.590000</td>\n",
       "      <td>59.900000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              idade  hipertensao  doenca_coracao  media_nivel_glicose  \\\n",
       "count  43309.000000  43309.00000    43309.000000         43309.000000   \n",
       "mean      42.213087      0.09326        0.047542           104.442117   \n",
       "std       22.533671      0.29080        0.212798            43.060134   \n",
       "min        0.080000      0.00000        0.000000            55.000000   \n",
       "25%       24.000000      0.00000        0.000000            77.540000   \n",
       "50%       44.000000      0.00000        0.000000            91.570000   \n",
       "75%       60.000000      0.00000        0.000000           112.030000   \n",
       "max       82.000000      1.00000        1.000000           281.590000   \n",
       "\n",
       "                imc           avc  \n",
       "count  43309.000000  43309.000000  \n",
       "mean      28.526248      0.018079  \n",
       "std        7.438084      0.133240  \n",
       "min       10.100000      0.000000  \n",
       "25%       23.400000      0.000000  \n",
       "50%       28.000000      0.000000  \n",
       "75%       32.600000      0.000000  \n",
       "max       59.900000      1.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sexo</th>\n",
       "      <th>idade</th>\n",
       "      <th>hipertensao</th>\n",
       "      <th>doenca_coracao</th>\n",
       "      <th>tipo_trabalho</th>\n",
       "      <th>tipo_residencia</th>\n",
       "      <th>media_nivel_glicose</th>\n",
       "      <th>imc</th>\n",
       "      <th>status_tabagismo</th>\n",
       "      <th>avc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>crianca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>95.12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>desconhecido</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>87.96</td>\n",
       "      <td>39.2</td>\n",
       "      <td>nunca fumou</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>110.89</td>\n",
       "      <td>17.6</td>\n",
       "      <td>desconhecido</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Rural</td>\n",
       "      <td>69.04</td>\n",
       "      <td>35.9</td>\n",
       "      <td>fuma eventualmente</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Nunca_trabalhou</td>\n",
       "      <td>Rural</td>\n",
       "      <td>161.28</td>\n",
       "      <td>19.1</td>\n",
       "      <td>desconhecido</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sexo  idade  hipertensao  doenca_coracao    tipo_trabalho  \\\n",
       "0  Masculino    3.0            0               0          crianca   \n",
       "1  Masculino   58.0            1               0          Privado   \n",
       "2   Feminino    8.0            0               0          Privado   \n",
       "3   Feminino   70.0            0               0          Privado   \n",
       "4  Masculino   14.0            0               0  Nunca_trabalhou   \n",
       "\n",
       "  tipo_residencia  media_nivel_glicose   imc    status_tabagismo  avc  \n",
       "0           Rural                95.12  18.0        desconhecido    0  \n",
       "1          Urbana                87.96  39.2         nunca fumou    0  \n",
       "2          Urbana               110.89  17.6        desconhecido    0  \n",
       "3           Rural                69.04  35.9  fuma eventualmente    0  \n",
       "4           Rural               161.28  19.1        desconhecido    0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convertendo Variáveis Categóricas em Valores Numéricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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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       "        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>sexo</th>\n",
       "      <th>tipo_trabalho</th>\n",
       "      <th>tipo_residencia</th>\n",
       "      <th>status_tabagismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>crianca</td>\n",
       "      <td>Rural</td>\n",
       "      <td>desconhecido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>nunca fumou</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>desconhecido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Rural</td>\n",
       "      <td>fuma eventualmente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>Nunca_trabalhou</td>\n",
       "      <td>Rural</td>\n",
       "      <td>desconhecido</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sexo    tipo_trabalho tipo_residencia    status_tabagismo\n",
       "0  Masculino          crianca           Rural        desconhecido\n",
       "1  Masculino          Privado          Urbana         nunca fumou\n",
       "2   Feminino          Privado          Urbana        desconhecido\n",
       "3   Feminino          Privado           Rural  fuma eventualmente\n",
       "4  Masculino  Nunca_trabalhou           Rural        desconhecido"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dfcatname].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>sexo</th>\n",
       "      <th>tipo_trabalho</th>\n",
       "      <th>tipo_residencia</th>\n",
       "      <th>status_tabagismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>43309</td>\n",
       "      <td>43309</td>\n",
       "      <td>43309</td>\n",
       "      <td>43309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>Privado</td>\n",
       "      <td>Urbana</td>\n",
       "      <td>nunca fumou</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>25605</td>\n",
       "      <td>24770</td>\n",
       "      <td>21712</td>\n",
       "      <td>16016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sexo tipo_trabalho tipo_residencia status_tabagismo\n",
       "count      43309         43309           43309            43309\n",
       "unique         3             5               2                4\n",
       "top     Feminino       Privado          Urbana      nunca fumou\n",
       "freq       25605         24770           21712            16016"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dfcatname].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Masculino', 'Feminino', 'Outro'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['sexo'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Rural', 'Urbana'], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['tipo_residencia'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usaremos Label Encoding nas colunas tendo 3 ou menos categorias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['sexo'] = dataset['sexo'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['tipo_residencia'] = dataset['tipo_residencia'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['sexo'] = dataset['sexo'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['tipo_residencia'] = dataset['tipo_residencia'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sexo</th>\n",
       "      <th>idade</th>\n",
       "      <th>hipertensao</th>\n",
       "      <th>doenca_coracao</th>\n",
       "      <th>tipo_trabalho</th>\n",
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       "      <th>avc</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>crianca</td>\n",
       "      <td>0</td>\n",
       "      <td>95.12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>desconhecido</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Privado</td>\n",
       "      <td>1</td>\n",
       "      <td>87.96</td>\n",
       "      <td>39.2</td>\n",
       "      <td>nunca fumou</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Privado</td>\n",
       "      <td>1</td>\n",
       "      <td>110.89</td>\n",
       "      <td>17.6</td>\n",
       "      <td>desconhecido</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Privado</td>\n",
       "      <td>0</td>\n",
       "      <td>69.04</td>\n",
       "      <td>35.9</td>\n",
       "      <td>fuma eventualmente</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Nunca_trabalhou</td>\n",
       "      <td>0</td>\n",
       "      <td>161.28</td>\n",
       "      <td>19.1</td>\n",
       "      <td>desconhecido</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sexo  idade  hipertensao  doenca_coracao    tipo_trabalho  tipo_residencia  \\\n",
       "0     1    3.0            0               0          crianca                0   \n",
       "1     1   58.0            1               0          Privado                1   \n",
       "2     0    8.0            0               0          Privado                1   \n",
       "3     0   70.0            0               0          Privado                0   \n",
       "4     1   14.0            0               0  Nunca_trabalhou                0   \n",
       "\n",
       "   media_nivel_glicose   imc    status_tabagismo  avc  \n",
       "0                95.12  18.0        desconhecido    0  \n",
       "1                87.96  39.2         nunca fumou    0  \n",
       "2               110.89  17.6        desconhecido    0  \n",
       "3                69.04  35.9  fuma eventualmente    0  \n",
       "4               161.28  19.1        desconhecido    0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usaremos One-Hot Encoding nas colunas com mais de 3 categorias. Essa técnica amenta o número de colunas no conjunto de dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.get_dummies(\n",
    "    data = dataset, \n",
    "    columns = ['tipo_trabalho', 'status_tabagismo'], \n",
    "    prefix = ['trabalho', 'tabagismo']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\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>sexo</th>\n",
       "      <th>idade</th>\n",
       "      <th>hipertensao</th>\n",
       "      <th>doenca_coracao</th>\n",
       "      <th>tipo_residencia</th>\n",
       "      <th>media_nivel_glicose</th>\n",
       "      <th>imc</th>\n",
       "      <th>avc</th>\n",
       "      <th>trabalho_Autonomo</th>\n",
       "      <th>trabalho_Nunca_trabalhou</th>\n",
       "      <th>trabalho_Privado</th>\n",
       "      <th>trabalho_Servidor_Publico</th>\n",
       "      <th>trabalho_crianca</th>\n",
       "      <th>tabagismo_desconhecido</th>\n",
       "      <th>tabagismo_fuma</th>\n",
       "      <th>tabagismo_fuma eventualmente</th>\n",
       "      <th>tabagismo_nunca fumou</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>95.12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>87.96</td>\n",
       "      <td>39.2</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>110.89</td>\n",
       "      <td>17.6</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>1</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>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69.04</td>\n",
       "      <td>35.9</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>161.28</td>\n",
       "      <td>19.1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sexo  idade  hipertensao  doenca_coracao  tipo_residencia  \\\n",
       "0     1    3.0            0               0                0   \n",
       "1     1   58.0            1               0                1   \n",
       "2     0    8.0            0               0                1   \n",
       "3     0   70.0            0               0                0   \n",
       "4     1   14.0            0               0                0   \n",
       "\n",
       "   media_nivel_glicose   imc  avc  trabalho_Autonomo  \\\n",
       "0                95.12  18.0    0                  0   \n",
       "1                87.96  39.2    0                  0   \n",
       "2               110.89  17.6    0                  0   \n",
       "3                69.04  35.9    0                  0   \n",
       "4               161.28  19.1    0                  0   \n",
       "\n",
       "   trabalho_Nunca_trabalhou  trabalho_Privado  trabalho_Servidor_Publico  \\\n",
       "0                         0                 0                          0   \n",
       "1                         0                 1                          0   \n",
       "2                         0                 1                          0   \n",
       "3                         0                 1                          0   \n",
       "4                         1                 0                          0   \n",
       "\n",
       "   trabalho_crianca  tabagismo_desconhecido  tabagismo_fuma  \\\n",
       "0                 1                       1               0   \n",
       "1                 0                       0               0   \n",
       "2                 0                       1               0   \n",
       "3                 0                       0               0   \n",
       "4                 0                       1               0   \n",
       "\n",
       "   tabagismo_fuma eventualmente  tabagismo_nunca fumou  \n",
       "0                             0                      0  \n",
       "1                             0                      1  \n",
       "2                             0                      0  \n",
       "3                             1                      0  \n",
       "4                             0                      0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning - Classificação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sexo</th>\n",
       "      <th>idade</th>\n",
       "      <th>hipertensao</th>\n",
       "      <th>doenca_coracao</th>\n",
       "      <th>tipo_residencia</th>\n",
       "      <th>media_nivel_glicose</th>\n",
       "      <th>imc</th>\n",
       "      <th>avc</th>\n",
       "      <th>trabalho_Autonomo</th>\n",
       "      <th>trabalho_Nunca_trabalhou</th>\n",
       "      <th>trabalho_Privado</th>\n",
       "      <th>trabalho_Servidor_Publico</th>\n",
       "      <th>trabalho_crianca</th>\n",
       "      <th>tabagismo_desconhecido</th>\n",
       "      <th>tabagismo_fuma</th>\n",
       "      <th>tabagismo_fuma eventualmente</th>\n",
       "      <th>tabagismo_nunca fumou</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.00000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "      <td>43309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.409037</td>\n",
       "      <td>42.213087</td>\n",
       "      <td>0.09326</td>\n",
       "      <td>0.047542</td>\n",
       "      <td>0.501328</td>\n",
       "      <td>104.442117</td>\n",
       "      <td>28.526248</td>\n",
       "      <td>0.018079</td>\n",
       "      <td>0.156480</td>\n",
       "      <td>0.004087</td>\n",
       "      <td>0.571937</td>\n",
       "      <td>0.125378</td>\n",
       "      <td>0.142118</td>\n",
       "      <td>0.306380</td>\n",
       "      <td>0.151123</td>\n",
       "      <td>0.172689</td>\n",
       "      <td>0.369808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.492178</td>\n",
       "      <td>22.533671</td>\n",
       "      <td>0.29080</td>\n",
       "      <td>0.212798</td>\n",
       "      <td>0.500004</td>\n",
       "      <td>43.060134</td>\n",
       "      <td>7.438084</td>\n",
       "      <td>0.133240</td>\n",
       "      <td>0.363314</td>\n",
       "      <td>0.063799</td>\n",
       "      <td>0.494804</td>\n",
       "      <td>0.331151</td>\n",
       "      <td>0.349175</td>\n",
       "      <td>0.460995</td>\n",
       "      <td>0.358173</td>\n",
       "      <td>0.377983</td>\n",
       "      <td>0.482758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.540000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>91.570000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>112.030000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>281.590000</td>\n",
       "      <td>59.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               sexo         idade  hipertensao  doenca_coracao  \\\n",
       "count  43309.000000  43309.000000  43309.00000    43309.000000   \n",
       "mean       0.409037     42.213087      0.09326        0.047542   \n",
       "std        0.492178     22.533671      0.29080        0.212798   \n",
       "min        0.000000      0.080000      0.00000        0.000000   \n",
       "25%        0.000000     24.000000      0.00000        0.000000   \n",
       "50%        0.000000     44.000000      0.00000        0.000000   \n",
       "75%        1.000000     60.000000      0.00000        0.000000   \n",
       "max        2.000000     82.000000      1.00000        1.000000   \n",
       "\n",
       "       tipo_residencia  media_nivel_glicose           imc           avc  \\\n",
       "count     43309.000000         43309.000000  43309.000000  43309.000000   \n",
       "mean          0.501328           104.442117     28.526248      0.018079   \n",
       "std           0.500004            43.060134      7.438084      0.133240   \n",
       "min           0.000000            55.000000     10.100000      0.000000   \n",
       "25%           0.000000            77.540000     23.400000      0.000000   \n",
       "50%           1.000000            91.570000     28.000000      0.000000   \n",
       "75%           1.000000           112.030000     32.600000      0.000000   \n",
       "max           1.000000           281.590000     59.900000      1.000000   \n",
       "\n",
       "       trabalho_Autonomo  trabalho_Nunca_trabalhou  trabalho_Privado  \\\n",
       "count       43309.000000              43309.000000      43309.000000   \n",
       "mean            0.156480                  0.004087          0.571937   \n",
       "std             0.363314                  0.063799          0.494804   \n",
       "min             0.000000                  0.000000          0.000000   \n",
       "25%             0.000000                  0.000000          0.000000   \n",
       "50%             0.000000                  0.000000          1.000000   \n",
       "75%             0.000000                  0.000000          1.000000   \n",
       "max             1.000000                  1.000000          1.000000   \n",
       "\n",
       "       trabalho_Servidor_Publico  trabalho_crianca  tabagismo_desconhecido  \\\n",
       "count               43309.000000      43309.000000            43309.000000   \n",
       "mean                    0.125378          0.142118                0.306380   \n",
       "std                     0.331151          0.349175                0.460995   \n",
       "min                     0.000000          0.000000                0.000000   \n",
       "25%                     0.000000          0.000000                0.000000   \n",
       "50%                     0.000000          0.000000                0.000000   \n",
       "75%                     0.000000          0.000000                1.000000   \n",
       "max                     1.000000          1.000000                1.000000   \n",
       "\n",
       "       tabagismo_fuma  tabagismo_fuma eventualmente  tabagismo_nunca fumou  \n",
       "count    43309.000000                  43309.000000           43309.000000  \n",
       "mean         0.151123                      0.172689               0.369808  \n",
       "std          0.358173                      0.377983               0.482758  \n",
       "min          0.000000                      0.000000               0.000000  \n",
       "25%          0.000000                      0.000000               0.000000  \n",
       "50%          0.000000                      0.000000               0.000000  \n",
       "75%          0.000000                      0.000000               1.000000  \n",
       "max          1.000000                      1.000000               1.000000  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divisão dos dados em X e y\n",
    "X = dataset.loc[:, dataset.columns != 'avc']\n",
    "y = dataset['avc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parâmnetros para a divisão dos dados em treino e teste\n",
    "seed = 7\n",
    "test_size = 0.33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divisão dos dados em treino e teste\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_treino, X_teste, y_treino, y_teste = train_test_split(X, y, test_size = test_size, random_state = seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29017, 16)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_treino.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14292, 16)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_teste.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29017,)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_treino.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14292,)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_teste.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criando o Classificador"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importar pacotes\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cria o classificador \n",
    "modelo = RandomForestClassifier()\n",
    "\n",
    "# Treina e cira o modelo\n",
    "modelo.fit(X_treino, y_treino)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Previsões com dados de teste\n",
    "y_pred = modelo.predict(X_teste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia: 98.27%\n"
     ]
    }
   ],
   "source": [
    "# Avaliando as previsões\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "acuracia = accuracy_score(y_teste, y_pred)\n",
    "print(f'Acurácia: {acuracia*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fazendo Previsões com Novos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carrega os dados\n",
    "dataset_novos_dados = pd.read_csv('dados/dataset-data-munging-01-novos_dados.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aplicamos aos novos dados as mesmas manipulações que foram feitas com os dados de treinamento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "del dataset_novos_dados['casado']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "novo_status_tabagismo = np.where(\n",
    "    dataset_novos_dados['status_tabagismo'].isnull(), \n",
    "    \"desconhecido\",\n",
    "    dataset_novos_dados['status_tabagismo']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_novos_dados['status_tabagismo'] = novo_status_tabagismo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     False\n",
       "sexo                   False\n",
       "idade                  False\n",
       "hipertensao            False\n",
       "doenca_coracao         False\n",
       "tipo_trabalho          False\n",
       "tipo_residencia        False\n",
       "media_nivel_glicose    False\n",
       "imc                     True\n",
       "status_tabagismo       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_novos_dados.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "novo_imc = np.where(\n",
    "    dataset_novos_dados['imc'].isnull(),\n",
    "    dataset_novos_dados['imc'].mean(),\n",
    "    dataset_novos_dados['imc']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_novos_dados['imc'] = novo_imc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     False\n",
       "sexo                   False\n",
       "idade                  False\n",
       "hipertensao            False\n",
       "doenca_coracao         False\n",
       "tipo_trabalho          False\n",
       "tipo_residencia        False\n",
       "media_nivel_glicose    False\n",
       "imc                    False\n",
       "status_tabagismo       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_novos_dados.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sexo</th>\n",
       "      <th>idade</th>\n",
       "      <th>hipertensao</th>\n",
       "      <th>doenca_coracao</th>\n",
       "      <th>tipo_residencia</th>\n",
       "      <th>media_nivel_glicose</th>\n",
       "      <th>imc</th>\n",
       "      <th>trabalho_Autonomo</th>\n",
       "      <th>trabalho_Never_worked</th>\n",
       "      <th>trabalho_Privado</th>\n",
       "      <th>trabalho_Servidor_Publico</th>\n",
       "      <th>trabalho_crianca</th>\n",
       "      <th>tabagismo_desconhecido</th>\n",
       "      <th>tabagismo_fuma</th>\n",
       "      <th>tabagismo_fuma eventualmente</th>\n",
       "      <th>tabagismo_nunca fumou</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36306</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>83.84</td>\n",
       "      <td>21.1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61829</td>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>179.50</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14152</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>95.16</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12997</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94.76</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40801</td>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83.57</td>\n",
       "      <td>27.6</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",
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       "      <td>1</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>18596</th>\n",
       "      <td>67353</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74.43</td>\n",
       "      <td>18.4</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",
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       "    <tr>\n",
       "      <th>18597</th>\n",
       "      <td>362</td>\n",
       "      <td>1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211.55</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>125.74</td>\n",
       "      <td>29.4</td>\n",
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       "    <tr>\n",
       "      <th>18599</th>\n",
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       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69.46</td>\n",
       "      <td>33.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>18600</th>\n",
       "      <td>16770</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91.23</td>\n",
       "      <td>24.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18601 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  sexo  idade  hipertensao  doenca_coracao  tipo_residencia  \\\n",
       "0      36306     1   80.0            0               0                1   \n",
       "1      61829     0   74.0            0               1                0   \n",
       "2      14152     0   14.0            0               0                0   \n",
       "3      12997     1   28.0            0               0                1   \n",
       "4      40801     0   63.0            0               0                0   \n",
       "...      ...   ...    ...          ...             ...              ...   \n",
       "18596  67353     1   20.0            0               0                0   \n",
       "18597    362     1   61.0            0               0                0   \n",
       "18598  29839     0   79.0            0               0                0   \n",
       "18599   6438     1   55.0            0               0                0   \n",
       "18600  16770     0   38.0            0               0                0   \n",
       "\n",
       "       media_nivel_glicose   imc  trabalho_Autonomo  trabalho_Never_worked  \\\n",
       "0                    83.84  21.1                  0                      0   \n",
       "1                   179.50  26.0                  1                      0   \n",
       "2                    95.16  21.2                  0                      0   \n",
       "3                    94.76  23.4                  0                      0   \n",
       "4                    83.57  27.6                  0                      0   \n",
       "...                    ...   ...                ...                    ...   \n",
       "18596                74.43  18.4                  0                      0   \n",
       "18597               211.55  31.6                  0                      0   \n",
       "18598               125.74  29.4                  0                      0   \n",
       "18599                69.46  33.8                  0                      0   \n",
       "18600                91.23  24.4                  0                      0   \n",
       "\n",
       "       trabalho_Privado  trabalho_Servidor_Publico  trabalho_crianca  \\\n",
       "0                     1                          0                 0   \n",
       "1                     0                          0                 0   \n",
       "2                     0                          0                 1   \n",
       "3                     1                          0                 0   \n",
       "4                     0                          1                 0   \n",
       "...                 ...                        ...               ...   \n",
       "18596                 1                          0                 0   \n",
       "18597                 0                          1                 0   \n",
       "18598                 1                          0                 0   \n",
       "18599                 0                          1                 0   \n",
       "18600                 1                          0                 0   \n",
       "\n",
       "       tabagismo_desconhecido  tabagismo_fuma  tabagismo_fuma eventualmente  \\\n",
       "0                           0               0                             1   \n",
       "1                           0               0                             1   \n",
       "2                           1               0                             0   \n",
       "3                           1               0                             0   \n",
       "4                           0               0                             0   \n",
       "...                       ...             ...                           ...   \n",
       "18596                       0               0                             1   \n",
       "18597                       0               1                             0   \n",
       "18598                       0               0                             0   \n",
       "18599                       0               0                             0   \n",
       "18600                       1               0                             0   \n",
       "\n",
       "       tabagismo_nunca fumou  \n",
       "0                          0  \n",
       "1                          0  \n",
       "2                          0  \n",
       "3                          0  \n",
       "4                          1  \n",
       "...                      ...  \n",
       "18596                      0  \n",
       "18597                      0  \n",
       "18598                      1  \n",
       "18599                      1  \n",
       "18600                      0  \n",
       "\n",
       "[18601 rows x 17 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_novos_dados['sexo'] = dataset_novos_dados['sexo'].astype('category')\n",
    "dataset_novos_dados['tipo_residencia'] = dataset_novos_dados['tipo_residencia'].astype('category')\n",
    "dataset_novos_dados['sexo'] = dataset_novos_dados['sexo'].cat.codes\n",
    "dataset_novos_dados['tipo_residencia'] = dataset_novos_dados['tipo_residencia'].cat.codes\n",
    "dataset_novos_dados = pd.get_dummies(\n",
    "    data = dataset_novos_dados,\n",
    "    columns = ['tipo_trabalho', 'status_tabagismo'],\n",
    "    prefix = ['trabalho', 'tabagismo']\n",
    ")\n",
    "dataset_novos_dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sexo</th>\n",
       "      <th>idade</th>\n",
       "      <th>hipertensao</th>\n",
       "      <th>doenca_coracao</th>\n",
       "      <th>tipo_residencia</th>\n",
       "      <th>media_nivel_glicose</th>\n",
       "      <th>imc</th>\n",
       "      <th>trabalho_Autonomo</th>\n",
       "      <th>trabalho_Never_worked</th>\n",
       "      <th>trabalho_Privado</th>\n",
       "      <th>trabalho_Servidor_Publico</th>\n",
       "      <th>trabalho_crianca</th>\n",
       "      <th>tabagismo_desconhecido</th>\n",
       "      <th>tabagismo_fuma</th>\n",
       "      <th>tabagismo_fuma eventualmente</th>\n",
       "      <th>tabagismo_nunca fumou</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>83.84</td>\n",
       "      <td>21.1</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>179.50</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>95.16</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94.76</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83.57</td>\n",
       "      <td>27.6</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18596</th>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74.43</td>\n",
       "      <td>18.4</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18597</th>\n",
       "      <td>1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211.55</td>\n",
       "      <td>31.6</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18598</th>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125.74</td>\n",
       "      <td>29.4</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18599</th>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69.46</td>\n",
       "      <td>33.8</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18600</th>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91.23</td>\n",
       "      <td>24.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18601 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sexo  idade  hipertensao  doenca_coracao  tipo_residencia  \\\n",
       "0         1   80.0            0               0                1   \n",
       "1         0   74.0            0               1                0   \n",
       "2         0   14.0            0               0                0   \n",
       "3         1   28.0            0               0                1   \n",
       "4         0   63.0            0               0                0   \n",
       "...     ...    ...          ...             ...              ...   \n",
       "18596     1   20.0            0               0                0   \n",
       "18597     1   61.0            0               0                0   \n",
       "18598     0   79.0            0               0                0   \n",
       "18599     1   55.0            0               0                0   \n",
       "18600     0   38.0            0               0                0   \n",
       "\n",
       "       media_nivel_glicose   imc  trabalho_Autonomo  trabalho_Never_worked  \\\n",
       "0                    83.84  21.1                  0                      0   \n",
       "1                   179.50  26.0                  1                      0   \n",
       "2                    95.16  21.2                  0                      0   \n",
       "3                    94.76  23.4                  0                      0   \n",
       "4                    83.57  27.6                  0                      0   \n",
       "...                    ...   ...                ...                    ...   \n",
       "18596                74.43  18.4                  0                      0   \n",
       "18597               211.55  31.6                  0                      0   \n",
       "18598               125.74  29.4                  0                      0   \n",
       "18599                69.46  33.8                  0                      0   \n",
       "18600                91.23  24.4                  0                      0   \n",
       "\n",
       "       trabalho_Privado  trabalho_Servidor_Publico  trabalho_crianca  \\\n",
       "0                     1                          0                 0   \n",
       "1                     0                          0                 0   \n",
       "2                     0                          0                 1   \n",
       "3                     1                          0                 0   \n",
       "4                     0                          1                 0   \n",
       "...                 ...                        ...               ...   \n",
       "18596                 1                          0                 0   \n",
       "18597                 0                          1                 0   \n",
       "18598                 1                          0                 0   \n",
       "18599                 0                          1                 0   \n",
       "18600                 1                          0                 0   \n",
       "\n",
       "       tabagismo_desconhecido  tabagismo_fuma  tabagismo_fuma eventualmente  \\\n",
       "0                           0               0                             1   \n",
       "1                           0               0                             1   \n",
       "2                           1               0                             0   \n",
       "3                           1               0                             0   \n",
       "4                           0               0                             0   \n",
       "...                       ...             ...                           ...   \n",
       "18596                       0               0                             1   \n",
       "18597                       0               1                             0   \n",
       "18598                       0               0                             0   \n",
       "18599                       0               0                             0   \n",
       "18600                       1               0                             0   \n",
       "\n",
       "       tabagismo_nunca fumou  \n",
       "0                          0  \n",
       "1                          0  \n",
       "2                          0  \n",
       "3                          0  \n",
       "4                          1  \n",
       "...                      ...  \n",
       "18596                      0  \n",
       "18597                      0  \n",
       "18598                      1  \n",
       "18599                      1  \n",
       "18600                      0  \n",
       "\n",
       "[18601 rows x 16 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separando a coluna id para usarmos mais tarde\n",
    "X_novos_dados = dataset_novos_dados.loc[:, dataset_novos_dados.columns != 'id']\n",
    "paciente_id = dataset_novos_dados['id']\n",
    "X_novos_dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Previsões com o Modelo Treinado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>18600</th>\n",
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       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91.23</td>\n",
       "      <td>24.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18601 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sexo  idade  hipertensao  doenca_coracao  tipo_residencia  \\\n",
       "0         1   80.0            0               0                1   \n",
       "1         0   74.0            0               1                0   \n",
       "2         0   14.0            0               0                0   \n",
       "3         1   28.0            0               0                1   \n",
       "4         0   63.0            0               0                0   \n",
       "...     ...    ...          ...             ...              ...   \n",
       "18596     1   20.0            0               0                0   \n",
       "18597     1   61.0            0               0                0   \n",
       "18598     0   79.0            0               0                0   \n",
       "18599     1   55.0            0               0                0   \n",
       "18600     0   38.0            0               0                0   \n",
       "\n",
       "       media_nivel_glicose   imc  trabalho_Autonomo  trabalho_Never_worked  \\\n",
       "0                    83.84  21.1                  0                      0   \n",
       "1                   179.50  26.0                  1                      0   \n",
       "2                    95.16  21.2                  0                      0   \n",
       "3                    94.76  23.4                  0                      0   \n",
       "4                    83.57  27.6                  0                      0   \n",
       "...                    ...   ...                ...                    ...   \n",
       "18596                74.43  18.4                  0                      0   \n",
       "18597               211.55  31.6                  0                      0   \n",
       "18598               125.74  29.4                  0                      0   \n",
       "18599                69.46  33.8                  0                      0   \n",
       "18600                91.23  24.4                  0                      0   \n",
       "\n",
       "       trabalho_Privado  trabalho_Servidor_Publico  trabalho_crianca  \\\n",
       "0                     1                          0                 0   \n",
       "1                     0                          0                 0   \n",
       "2                     0                          0                 1   \n",
       "3                     1                          0                 0   \n",
       "4                     0                          1                 0   \n",
       "...                 ...                        ...               ...   \n",
       "18596                 1                          0                 0   \n",
       "18597                 0                          1                 0   \n",
       "18598                 1                          0                 0   \n",
       "18599                 0                          1                 0   \n",
       "18600                 1                          0                 0   \n",
       "\n",
       "       tabagismo_desconhecido  tabagismo_fuma  tabagismo_fuma eventualmente  \\\n",
       "0                           0               0                             1   \n",
       "1                           0               0                             1   \n",
       "2                           1               0                             0   \n",
       "3                           1               0                             0   \n",
       "4                           0               0                             0   \n",
       "...                       ...             ...                           ...   \n",
       "18596                       0               0                             1   \n",
       "18597                       0               1                             0   \n",
       "18598                       0               0                             0   \n",
       "18599                       0               0                             0   \n",
       "18600                       1               0                             0   \n",
       "\n",
       "       tabagismo_nunca fumou  \n",
       "0                          0  \n",
       "1                          0  \n",
       "2                          0  \n",
       "3                          0  \n",
       "4                          1  \n",
       "...                      ...  \n",
       "18596                      0  \n",
       "18597                      0  \n",
       "18598                      1  \n",
       "18599                      1  \n",
       "18600                      0  \n",
       "\n",
       "[18601 rows x 16 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_novos_dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Usando dados de teste para as previsões\n",
    "y_novos_dados_label = modelo.predict(X_novos_dados)\n",
    "y_novos_dados_prob = modelo.predict_proba(X_novos_dados)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_novos_dados_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.95, 0.05],\n",
       "       [0.95, 0.05],\n",
       "       [1.  , 0.  ],\n",
       "       ...,\n",
       "       [0.95, 0.05],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_novos_dados_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extraindo todas as previsões\n",
    "## Cria um vetor vazio\n",
    "y_novos_dados = [None] * len(X_novos_dados)\n",
    "\n",
    "## Loop pelas previsões e gravações no vetor de previsões\n",
    "for i in range(len(X_novos_dados)):\n",
    "    y_novos_dados[i] = y_novos_dados_prob[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0.95, 0.05]),\n",
       " array([0.95, 0.05]),\n",
       " array([1., 0.]),\n",
       " array([1., 0.]),\n",
       " array([1., 0.])]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vetor de previsões\n",
    "y_novos_dados[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Salvando o resultado com as previsões para cada paciente\n",
    "resultado = pd.DataFrame()\n",
    "resultado['id'] = paciente_id\n",
    "resultado['avc'] = y_novos_dados\n",
    "resultado.to_csv('dados/resultado.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
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    "name": "ipython",
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