06. Auditoria_em_Planos_Saúde/Trabalho_MD_Auditorias_Planos_Saude_Andre_Jefferson_Wendel.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Trabalho_MD_Auditorias_Planos_Saude_Andre_Jefferson_Wendel.ipynb","provenance":[],"collapsed_sections":["536G0KIcDLi7","eq94yfCHcyLe","UIjF97TcTGyQ","2si5ArKHJcqi","yoxdwcg1PsJl","nRzKQx1VjG7r","vDLpwssljzjI","gmFfeCqzkoaQ","mcQe_qwDksjD","rN6PGfhak34P","UjQKhmLkldWn","X6QLTuhil4zg","0hq6T7rbl8_x","WEQasMLYl9LP","3CC4Firul9U4","F9i9mzC_l9f4","Ne06F8qwSQfq"]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# **Trabalho prático de Mineração de Dados e Aprendizado de Máquina**\n","### **Auditorias em Planos de Saúde**\n","\n","Autorização de procedimentos de uma operadora de Planos de Saúde\n","\n","</br>\n","\n","---\n","**Instituto de Informática - UFG**\n","\n","Disciplina: Mineração de Dados e Aprendizado de Máquina\n","\n","Prof. Rogério Salvini\n","\n","---\n","\n","Discentes:\n"," \n","* André Ribeiro Miranda - 201904191\n","* Jefferson Carvalho Silva\n","* Wendel Marques de Jesus Souza - 201702793\n","\n","---\n","\n","Links úteis:\n","* [Descrição do Trabalho](#)\n","* [Dicionário de Dados](#)\n","* [Competição no Kaggle](#)\n","* [Base de Dados](#)\n","* [Pasta no Google Drive](#)\n"],"metadata":{"id":"a7uxz6edHZYG"}},{"cell_type":"markdown","metadata":{"id":"e0IEfqwfSX5N"},"source":["# Objetivos gerais deste artefato"]},{"cell_type":"markdown","metadata":{"id":"SfGRAAi2Sbnd"},"source":["1. Obtenção de *insights* sobre o conjunto de dados.  Para tanto, serão utilizadas técnicas de visualização de dados e alguns cálculos estátisticos básicos. Essa etapa é chamada de análise exploratória de dados (AED). Por meio dela, é possível também encontrar problemas, padrões e relacionamentos que poderão apoiar a etapa de pré-processamento, que antecede a implementação. \n","\n","2. Documentação de todo o processo de geração e avaliação de um modelo, bem como a geração do artefato que será submetido no Kaggle."]},{"cell_type":"markdown","source":["#Especificação do problema "],"metadata":{"id":"Kl9An7IuI2i_"}},{"cell_type":"markdown","source":["## Problema \n","Os auditores são profissionais especializados (médicos e enfermeiros) que geram custos elevados para a operadora. Quanto maior o número de requisições encaminhadas para auditoria, maior será o gasto administrativo. Os sistemas atuais se limitam a analisar as regras parametrizadas (se/então), não conseguem analisar o comportamento dos auditores para prever desfechos prováveis. \n","\n","## Proposta \n","Gerar modelos capazes de combinar as variáveis de uma requisição e, baseada no comportamento histórico dos auditores, consiga automatizar a análise de novas solicitações."],"metadata":{"id":"29pqgmySI84A"}},{"cell_type":"markdown","metadata":{"id":"ao9brJj5T136"},"source":["# Aquisição de bibliotecas e dados"]},{"cell_type":"markdown","metadata":{"id":"78jz3gvIn6Tp"},"source":["###Bibliotecas utilizadas"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UrZLeRfp5qzY","executionInfo":{"status":"ok","timestamp":1650082679551,"user_tz":180,"elapsed":31448,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"0bf01e23-811b-4a40-df48-10e3243aaf8d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","metadata":{"id":"ebUQXRyyTwDU"},"source":["#biblitoecas utilizadas na etada AED\n","import pandas as pd\n","import numpy as np\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","import warnings \n","warnings.filterwarnings('ignore') #evita que warnings sejam mostrados\n","\n","# ###\n","# from sklearn.preprocessing import LabelEncoder\n","# from scipy import stats\n","\n","# from sklearn.model_selection import train_test_split\n","# from sklearn.preprocessing import LabelEncoder\n","\n","# from sklearn import svm\n","# from sklearn.neighbors import KNeighborsClassifier\n","# from sklearn.model_selection import GridSearchCV\n","# from sklearn.naive_bayes import GaussianNB\n","# from sklearn.tree import DecisionTreeClassifier\n","# from sklearn import tree\n","# import graphviz \n","\n","# from sklearn.metrics import classification_report\n","# from imblearn.under_sampling import RandomUnderSampler"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Z30w6iF1n-Jy"},"source":["### Aquisição dos dados\n","\n","Base de dados disponível [aqui](#)."]},{"cell_type":"code","metadata":{"id":"8WGBT3NPUBIh","colab":{"base_uri":"https://localhost:8080/","height":577},"executionInfo":{"status":"ok","timestamp":1650082700040,"user_tz":180,"elapsed":4830,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"d77dfb0b-0521-48cd-fd4b-3dddc179774c"},"source":["#Leitura dos dados\n","df = pd.read_csv('#', sep=';')\n","\n","#Exemplo de algumas linhas do conjunto de dados \n","df.head(5)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   NR_SEQ_REQUISICAO  NR_SEQ_ITEM  DT_REQUISICAO  \\\n","0            1506266      4178993        2459565   \n","1            1586873      4433946        2459611   \n","2            1556472      4335516        2459597   \n","3             943431      2331936        2459271   \n","4            1107870      2865226        2459361   \n","\n","                                       DS_TIPO_GUIA  DT_NASCIMENTO  \\\n","0                       Guia de solicitac?o SP/SADT      2437002.0   \n","1  Guia de solicitac?o de prorrogac?o de internac?o      2428370.0   \n","2                       Guia de solicitac?o SP/SADT      2437266.0   \n","3                       Guia de solicitac?o SP/SADT      2441608.0   \n","4                       Guia de solicitac?o SP/SADT      2435597.0   \n","\n","   NR_PRODUTO DS_TIPO_PREST_SOLICITANTE                            DS_CBO  \\\n","0           1                   CLINICA        Medico oncologista clinico   \n","1           1                  HOSPITAL     Medico em medicina intensiva    \n","2           1         CLINICA DE IMAGEM  Medico ginecologista e obstetra    \n","3           1                   CLINICA             Medico oftalmologista   \n","4           1     PRESTADOR DE SERVICOS                    Medico clinico   \n","\n","  DS_TIPO_CONSULTA  QT_TEMPO_DOENCA  ... CD_CID  \\\n","0              NaN              NaN  ...    C90   \n","1              NaN              NaN  ...    NaN   \n","2              NaN              NaN  ...    NaN   \n","3              NaN              NaN  ...   H353   \n","4              NaN              NaN  ...    NaN   \n","\n","                                DS_INDICACAO_CLINICA   DS_TIPO_ITEM   CD_ITEM  \\\n","0          Mieloma mult e neopl malig de plasmocitos  Procedimentos  60026251   \n","1  Solicitac?o de prorrogac?o de 2 diarias, refer...  Procedimentos  20203012   \n","2                             PEDIDO MEDICO EM ANEXO  Procedimentos  40901300   \n","3  \\nLAUDO OFTALMOLOGICO \\n\\n \\nPaciente: Patrici...  Procedimentos  30307147   \n","4                                       dor cronica.  Procedimentos  41101227   \n","\n","                                             DS_ITEM  \\\n","0  TAXA DE SALA MANIPULAC?O QUIMIOTERAPICOS ? CAPELA   \n","1  ASSISTENCIA FISIATRICA RESPIRATORIA EM PACIENT...   \n","2  USG - ULTRASSONOGRAFIA - TRANSVAGINAL (UTERO, ...   \n","3  PCT-HM - TRATAMENTO OCULAR QUIMIOTERAPICO COM ...   \n","4  RM - RESSONANCIA MAGNETICA - COLUNA CERVICAL O...   \n","\n","                           DS_CLASSE                          DS_SUBGRUPO  \\\n","0           FORA DO CENTRO CIRURGICO                         TAXA DE SALA   \n","1            REABILITAC?ES - SESS?ES  PROCEDIMENTOS CLINICOS HOSPITALARES   \n","2       ULTRA-SONOGRAFIA DIAGNOSTICA                     ULTRA-SONOGRAFIA   \n","3                       CORPO VITREO                                OLHOS   \n","4  RESSONANCIA MAGNETICA DIAGNOSTICA                RESSONANCIA MAGNETICA   \n","\n","                              DS_GRUPO  QT_SOLICITADA  DS_STATUS_ITEM  \n","0                                TAXAS              1      Autorizado  \n","1                     PROCED. CLINICOS              6      Autorizado  \n","2  PROCED. DIAGNOSTICOS E TERAPEUTICOS              1      Autorizado  \n","3      PROCED. CIRURGICOS E INVASIVOS               1          Negado  \n","4  PROCED. DIAGNOSTICOS E TERAPEUTICOS              2      Autorizado  \n","\n","[5 rows x 31 columns]"],"text/html":["\n","  <div id=\"df-0f49c004-4376-4689-be2e-2e14328c71ac\">\n","    <div class=\"colab-df-container\">\n","      <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>NR_SEQ_REQUISICAO</th>\n","      <th>NR_SEQ_ITEM</th>\n","      <th>DT_REQUISICAO</th>\n","      <th>DS_TIPO_GUIA</th>\n","      <th>DT_NASCIMENTO</th>\n","      <th>NR_PRODUTO</th>\n","      <th>DS_TIPO_PREST_SOLICITANTE</th>\n","      <th>DS_CBO</th>\n","      <th>DS_TIPO_CONSULTA</th>\n","      <th>QT_TEMPO_DOENCA</th>\n","      <th>...</th>\n","      <th>CD_CID</th>\n","      <th>DS_INDICACAO_CLINICA</th>\n","      <th>DS_TIPO_ITEM</th>\n","      <th>CD_ITEM</th>\n","      <th>DS_ITEM</th>\n","      <th>DS_CLASSE</th>\n","      <th>DS_SUBGRUPO</th>\n","      <th>DS_GRUPO</th>\n","      <th>QT_SOLICITADA</th>\n","      <th>DS_STATUS_ITEM</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1506266</td>\n","      <td>4178993</td>\n","      <td>2459565</td>\n","      <td>Guia de solicitac?o SP/SADT</td>\n","      <td>2437002.0</td>\n","      <td>1</td>\n","      <td>CLINICA</td>\n","      <td>Medico oncologista clinico</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>C90</td>\n","      <td>Mieloma mult e neopl malig de plasmocitos</td>\n","      <td>Procedimentos</td>\n","      <td>60026251</td>\n","      <td>TAXA DE SALA MANIPULAC?O QUIMIOTERAPICOS ? CAPELA</td>\n","      <td>FORA DO CENTRO CIRURGICO</td>\n","      <td>TAXA DE SALA</td>\n","      <td>TAXAS</td>\n","      <td>1</td>\n","      <td>Autorizado</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1586873</td>\n","      <td>4433946</td>\n","      <td>2459611</td>\n","      <td>Guia de solicitac?o de prorrogac?o de internac?o</td>\n","      <td>2428370.0</td>\n","      <td>1</td>\n","      <td>HOSPITAL</td>\n","      <td>Medico em medicina intensiva</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>Solicitac?o de prorrogac?o de 2 diarias, refer...</td>\n","      <td>Procedimentos</td>\n","      <td>20203012</td>\n","      <td>ASSISTENCIA FISIATRICA RESPIRATORIA EM PACIENT...</td>\n","      <td>REABILITAC?ES - SESS?ES</td>\n","      <td>PROCEDIMENTOS CLINICOS HOSPITALARES</td>\n","      <td>PROCED. CLINICOS</td>\n","      <td>6</td>\n","      <td>Autorizado</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1556472</td>\n","      <td>4335516</td>\n","      <td>2459597</td>\n","      <td>Guia de solicitac?o SP/SADT</td>\n","      <td>2437266.0</td>\n","      <td>1</td>\n","      <td>CLINICA DE IMAGEM</td>\n","      <td>Medico ginecologista e obstetra</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>PEDIDO MEDICO EM ANEXO</td>\n","      <td>Procedimentos</td>\n","      <td>40901300</td>\n","      <td>USG - ULTRASSONOGRAFIA - TRANSVAGINAL (UTERO, ...</td>\n","      <td>ULTRA-SONOGRAFIA DIAGNOSTICA</td>\n","      <td>ULTRA-SONOGRAFIA</td>\n","      <td>PROCED. DIAGNOSTICOS E TERAPEUTICOS</td>\n","      <td>1</td>\n","      <td>Autorizado</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>943431</td>\n","      <td>2331936</td>\n","      <td>2459271</td>\n","      <td>Guia de solicitac?o SP/SADT</td>\n","      <td>2441608.0</td>\n","      <td>1</td>\n","      <td>CLINICA</td>\n","      <td>Medico oftalmologista</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>H353</td>\n","      <td>\\nLAUDO OFTALMOLOGICO \\n\\n \\nPaciente: Patrici...</td>\n","      <td>Procedimentos</td>\n","      <td>30307147</td>\n","      <td>PCT-HM - TRATAMENTO OCULAR QUIMIOTERAPICO COM ...</td>\n","      <td>CORPO VITREO</td>\n","      <td>OLHOS</td>\n","      <td>PROCED. CIRURGICOS E INVASIVOS</td>\n","      <td>1</td>\n","      <td>Negado</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1107870</td>\n","      <td>2865226</td>\n","      <td>2459361</td>\n","      <td>Guia de solicitac?o SP/SADT</td>\n","      <td>2435597.0</td>\n","      <td>1</td>\n","      <td>PRESTADOR DE SERVICOS</td>\n","      <td>Medico clinico</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>dor cronica.</td>\n","      <td>Procedimentos</td>\n","      <td>41101227</td>\n","      <td>RM - RESSONANCIA MAGNETICA - COLUNA CERVICAL O...</td>\n","      <td>RESSONANCIA MAGNETICA DIAGNOSTICA</td>\n","      <td>RESSONANCIA MAGNETICA</td>\n","      <td>PROCED. DIAGNOSTICOS E TERAPEUTICOS</td>\n","      <td>2</td>\n","      <td>Autorizado</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 31 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f49c004-4376-4689-be2e-2e14328c71ac')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-0f49c004-4376-4689-be2e-2e14328c71ac button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Procedimentos  20203012   \n","\n","                                             DS_ITEM  \\\n","0  TAXA DE SALA MANIPULAC?O QUIMIOTERAPICOS ? CAPELA   \n","1  ASSISTENCIA FISIATRICA RESPIRATORIA EM PACIENT...   \n","\n","                  DS_CLASSE                          DS_SUBGRUPO  \\\n","0  FORA DO CENTRO CIRURGICO                         TAXA DE SALA   \n","1   REABILITAC?ES - SESS?ES  PROCEDIMENTOS CLINICOS HOSPITALARES   \n","\n","           DS_GRUPO  QT_SOLICITADA DS_STATUS_ITEM  \n","0             TAXAS              1     Autorizado  \n","1  PROCED. CLINICOS              6     Autorizado  "],"text/html":["\n","  <div id=\"df-2080ff6a-2d9b-4d4a-8f0a-db10926da299\">\n","    <div class=\"colab-df-container\">\n","      <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>NR_SEQ_REQUISICAO</th>\n","      <th>NR_SEQ_ITEM</th>\n","      <th>DT_REQUISICAO</th>\n","      <th>DS_TIPO_GUIA</th>\n","      <th>DT_NASCIMENTO</th>\n","      <th>NR_PRODUTO</th>\n","      <th>DS_TIPO_PREST_SOLICITANTE</th>\n","      <th>DS_CBO</th>\n","      <th>DS_TIPO_CONSULTA</th>\n","      <th>QT_TEMPO_DOENCA</th>\n","      <th>DS_UNIDADE_TEMPO_DOENCA</th>\n","      <th>DS_TIPO_DOENCA</th>\n","      <th>DS_INDICACAO_ACIDENTE</th>\n","      <th>DS_TIPO_SAIDA</th>\n","      <th>DS_TIPO_INTERNACAO</th>\n","      <th>DS_REGIME_INTERNACAO</th>\n","      <th>DS_CARATER_ATENDIMENTO</th>\n","      <th>DS_TIPO_ACOMODACAO</th>\n","      <th>QT_DIA_SOLICITADO</th>\n","      <th>CD_GUIA_REFERENCIA</th>\n","      <th>DS_TIPO_ATENDIMENTO</th>\n","      <th>CD_CID</th>\n","      <th>DS_INDICACAO_CLINICA</th>\n","      <th>DS_TIPO_ITEM</th>\n","      <th>CD_ITEM</th>\n","      <th>DS_ITEM</th>\n","      <th>DS_CLASSE</th>\n","      <th>DS_SUBGRUPO</th>\n","      <th>DS_GRUPO</th>\n","      <th>QT_SOLICITADA</th>\n","      <th>DS_STATUS_ITEM</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1506266</td>\n","      <td>4178993</td>\n","      <td>2459565</td>\n","      <td>Guia de solicitac?o SP/SADT</td>\n","      <td>2437002.0</td>\n","      <td>1</td>\n","      <td>CLINICA</td>\n","      <td>Medico oncologista clinico</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>N?o acidente</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Eletiva</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Quimioterapia</td>\n","      <td>C90</td>\n","      <td>Mieloma mult e neopl malig de plasmocitos</td>\n","      <td>Procedimentos</td>\n","      <td>60026251</td>\n","      <td>TAXA DE SALA MANIPULAC?O QUIMIOTERAPICOS ? CAPELA</td>\n","      <td>FORA DO CENTRO CIRURGICO</td>\n","      <td>TAXA DE SALA</td>\n","      <td>TAXAS</td>\n","      <td>1</td>\n","      <td>Autorizado</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1586873</td>\n","      <td>4433946</td>\n","      <td>2459611</td>\n","      <td>Guia de solicitac?o de prorrogac?o de internac?o</td>\n","      <td>2428370.0</td>\n","      <td>1</td>\n","      <td>HOSPITAL</td>\n","      <td>Medico em medicina intensiva</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>N?o acidente</td>\n","      <td>NaN</td>\n","      <td>Clinica</td>\n","      <td>Hospitalar</td>\n","      <td>Urgencia/Emergencia</td>\n","      <td>UTI Adulto</td>\n","      <td>2.0</td>\n","      <td>1573390.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Solicitac?o de prorrogac?o de 2 diarias, refer...</td>\n","      <td>Procedimentos</td>\n","      <td>20203012</td>\n","      <td>ASSISTENCIA FISIATRICA RESPIRATORIA EM PACIENT...</td>\n","      <td>REABILITAC?ES - SESS?ES</td>\n","      <td>PROCEDIMENTOS CLINICOS HOSPITALARES</td>\n","      <td>PROCED. CLINICOS</td>\n","      <td>6</td>\n","      <td>Autorizado</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2080ff6a-2d9b-4d4a-8f0a-db10926da299')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-2080ff6a-2d9b-4d4a-8f0a-db10926da299 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":6}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HY3AHP8SUTzR","executionInfo":{"status":"ok","timestamp":1649912623083,"user_tz":180,"elapsed":47,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"7a012d22-988d-4099-eedc-ef1ecf9577f8"},"source":["#Verificação da dimensionalidade do DataFrame\n","df.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(412540, 31)"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3vRBEbwPUdqw","executionInfo":{"status":"ok","timestamp":1649912624170,"user_tz":180,"elapsed":1132,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"058a4596-adae-444e-894f-e3714cf22b00"},"source":["#Informações dos atributos\n","df.info()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 412540 entries, 0 to 412539\n","Data columns (total 31 columns):\n"," #   Column                     Non-Null Count   Dtype  \n","---  ------                     --------------   -----  \n"," 0   NR_SEQ_REQUISICAO          412540 non-null  int64  \n"," 1   NR_SEQ_ITEM                412540 non-null  int64  \n"," 2   DT_REQUISICAO              412540 non-null  int64  \n"," 3   DS_TIPO_GUIA               412540 non-null  object \n"," 4   DT_NASCIMENTO              412529 non-null  float64\n"," 5   NR_PRODUTO                 412540 non-null  int64  \n"," 6   DS_TIPO_PREST_SOLICITANTE  412540 non-null  object \n"," 7   DS_CBO                     412540 non-null  object \n"," 8   DS_TIPO_CONSULTA           15689 non-null   object \n"," 9   QT_TEMPO_DOENCA            281 non-null     float64\n"," 10  DS_UNIDADE_TEMPO_DOENCA    282 non-null     object \n"," 11  DS_TIPO_DOENCA             967 non-null     object \n"," 12  DS_INDICACAO_ACIDENTE      380490 non-null  object \n"," 13  DS_TIPO_SAIDA              0 non-null       float64\n"," 14  DS_TIPO_INTERNACAO         121928 non-null  object \n"," 15  DS_REGIME_INTERNACAO       121929 non-null  object \n"," 16  DS_CARATER_ATENDIMENTO     412540 non-null  object \n"," 17  DS_TIPO_ACOMODACAO         121854 non-null  object \n"," 18  QT_DIA_SOLICITADO          120685 non-null  float64\n"," 19  CD_GUIA_REFERENCIA         70298 non-null   float64\n"," 20  DS_TIPO_ATENDIMENTO        291251 non-null  object \n"," 21  CD_CID                     177523 non-null  object \n"," 22  DS_INDICACAO_CLINICA       325070 non-null  object \n"," 23  DS_TIPO_ITEM               412540 non-null  object \n"," 24  CD_ITEM                    412540 non-null  int64  \n"," 25  DS_ITEM                    412540 non-null  object \n"," 26  DS_CLASSE                  412540 non-null  object \n"," 27  DS_SUBGRUPO                412540 non-null  object \n"," 28  DS_GRUPO                   412540 non-null  object \n"," 29  QT_SOLICITADA              412540 non-null  int64  \n"," 30  DS_STATUS_ITEM             412540 non-null  object \n","dtypes: float64(5), int64(6), object(20)\n","memory usage: 97.6+ MB\n"]}]},{"cell_type":"code","source":["variaveis_categoricas = df.select_dtypes(include=object).columns\n","variaveis_numericas = df.select_dtypes(include=np.number).columns\n","\n","variaveis_categoricas, variaveis_numericas\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8zle8lGsb1uW","executionInfo":{"status":"ok","timestamp":1650082898406,"user_tz":180,"elapsed":315,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"c98a4fd3-66ab-442a-8d3b-45ab1223fc6d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(Index(['DS_TIPO_GUIA', 'DS_TIPO_PREST_SOLICITANTE', 'DS_CBO',\n","        'DS_TIPO_CONSULTA', 'DS_UNIDADE_TEMPO_DOENCA', 'DS_TIPO_DOENCA',\n","        'DS_INDICACAO_ACIDENTE', 'DS_TIPO_INTERNACAO', 'DS_REGIME_INTERNACAO',\n","        'DS_CARATER_ATENDIMENTO', 'DS_TIPO_ACOMODACAO', 'DS_TIPO_ATENDIMENTO',\n","        'CD_CID', 'DS_INDICACAO_CLINICA', 'DS_TIPO_ITEM', 'DS_ITEM',\n","        'DS_CLASSE', 'DS_SUBGRUPO', 'DS_GRUPO', 'DS_STATUS_ITEM'],\n","       dtype='object'),\n"," Index(['NR_SEQ_REQUISICAO', 'NR_SEQ_ITEM', 'DT_REQUISICAO', 'DT_NASCIMENTO',\n","        'NR_PRODUTO', 'QT_TEMPO_DOENCA', 'DS_TIPO_SAIDA', 'QT_DIA_SOLICITADO',\n","        'CD_GUIA_REFERENCIA', 'CD_ITEM', 'QT_SOLICITADA'],\n","       dtype='object'))"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BZf6o92ohsN-","executionInfo":{"status":"ok","timestamp":1649912624180,"user_tz":180,"elapsed":33,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"a3233dd0-750e-4c25-97e9-8a169e5f5a5e"},"source":["#Descrição estatística de cada recurso\n","df[variaveis_numericas].describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       NR_SEQ_REQUISICAO   NR_SEQ_ITEM  DT_REQUISICAO  DT_NASCIMENTO  \\\n","count       4.125400e+05  4.125400e+05   4.125400e+05   4.125290e+05   \n","mean        1.151521e+06  2.626495e+06   2.459381e+06   2.439534e+06   \n","std         2.889745e+05  1.179561e+06   1.533197e+02   6.849753e+03   \n","min         5.796250e+05  8.231270e+05   2.459075e+06   2.421816e+06   \n","25%         9.300360e+05  1.468307e+06   2.459264e+06   2.434627e+06   \n","50%         1.189158e+06  2.760746e+06   2.459402e+06   2.438884e+06   \n","75%         1.385720e+06  3.658920e+06   2.459506e+06   2.443453e+06   \n","max         1.614682e+06  4.525999e+06   2.459622e+06   2.459562e+06   \n","\n","       NR_PRODUTO  QT_TEMPO_DOENCA  DS_TIPO_SAIDA  QT_DIA_SOLICITADO  \\\n","count    412540.0       281.000000            0.0      120685.000000   \n","mean          1.0         8.960854            NaN           2.460372   \n","std           0.0        11.014045            NaN           3.703165   \n","min           1.0         1.000000            NaN           0.000000   \n","25%           1.0         3.000000            NaN           1.000000   \n","50%           1.0         5.000000            NaN           1.000000   \n","75%           1.0        11.000000            NaN           3.000000   \n","max           1.0        92.000000            NaN          55.000000   \n","\n","       CD_GUIA_REFERENCIA       CD_ITEM  QT_SOLICITADA  \n","count        7.029800e+04  4.125400e+05  412540.000000  \n","mean         1.179341e+06  5.946714e+07      59.127765  \n","std          2.497937e+05  2.185481e+08    4056.078153  \n","min          5.797030e+05  4.200000e+01       1.000000  \n","25%          9.922550e+05  2.020305e+07       1.000000  \n","50%          1.186608e+06  4.070601e+07       1.000000  \n","75%          1.395000e+06  4.140148e+07       2.000000  \n","max          1.613675e+06  3.000079e+09  600343.000000  "],"text/html":["\n","  <div id=\"df-3066d619-c629-41b1-bc6a-0af337633368\">\n","    <div class=\"colab-df-container\">\n","      <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: 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<td>412540.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>1.151521e+06</td>\n","      <td>2.626495e+06</td>\n","      <td>2.459381e+06</td>\n","      <td>2.439534e+06</td>\n","      <td>1.0</td>\n","      <td>8.960854</td>\n","      <td>NaN</td>\n","      <td>2.460372</td>\n","      <td>1.179341e+06</td>\n","      <td>5.946714e+07</td>\n","      <td>59.127765</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>2.889745e+05</td>\n","      <td>1.179561e+06</td>\n","      <td>1.533197e+02</td>\n","      <td>6.849753e+03</td>\n","      <td>0.0</td>\n","      <td>11.014045</td>\n","      <td>NaN</td>\n","      <td>3.703165</td>\n","      <td>2.497937e+05</td>\n","      <td>2.185481e+08</td>\n","      <td>4056.078153</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>5.796250e+05</td>\n","      <td>8.231270e+05</td>\n","      <td>2.459075e+06</td>\n","      <td>2.421816e+06</td>\n","      <td>1.0</td>\n","      <td>1.000000</td>\n","      <td>NaN</td>\n","      <td>0.000000</td>\n","      <td>5.797030e+05</td>\n","      <td>4.200000e+01</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>9.300360e+05</td>\n","      <td>1.468307e+06</td>\n","      <td>2.459264e+06</td>\n","      <td>2.434627e+06</td>\n","      <td>1.0</td>\n","      <td>3.000000</td>\n","      <td>NaN</td>\n","      <td>1.000000</td>\n","      <td>9.922550e+05</td>\n","      <td>2.020305e+07</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>1.189158e+06</td>\n","      <td>2.760746e+06</td>\n","      <td>2.459402e+06</td>\n","      <td>2.438884e+06</td>\n","      <td>1.0</td>\n","      <td>5.000000</td>\n","      <td>NaN</td>\n","      <td>1.000000</td>\n","      <td>1.186608e+06</td>\n","      <td>4.070601e+07</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>1.385720e+06</td>\n","      <td>3.658920e+06</td>\n","      <td>2.459506e+06</td>\n","      <td>2.443453e+06</td>\n","      <td>1.0</td>\n","      <td>11.000000</td>\n","      <td>NaN</td>\n","      <td>3.000000</td>\n","      <td>1.395000e+06</td>\n","      <td>4.140148e+07</td>\n","      <td>2.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1.614682e+06</td>\n","      <td>4.525999e+06</td>\n","      <td>2.459622e+06</td>\n","      <td>2.459562e+06</td>\n","      <td>1.0</td>\n","      <td>92.000000</td>\n","      <td>NaN</td>\n","      <td>55.000000</td>\n","      <td>1.613675e+06</td>\n","      <td>3.000079e+09</td>\n","      <td>600343.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3066d619-c629-41b1-bc6a-0af337633368')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","    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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"ueNal_8vVLWI"},"source":["## Dados incompletos\n","Identificação de valores missing"]},{"cell_type":"code","source":["df.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c68RQMEXLaGr","executionInfo":{"status":"ok","timestamp":1649912624181,"user_tz":180,"elapsed":30,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"4aad43fe-111a-40a5-bc56-09059bd9f73e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(412540, 31)"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"KGGxjtjIVPSr"},"source":["#Verificação de valores missing\n","for coluna in df.columns:\n","    print(\"Qtd. de valores faltantes em {}: {}\".format(coluna, df[coluna].isna().sum()))"],"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#Verificação de valores missing  \n","print('############################################################\\n PORCENTAGEM DE DADOS FALTANTES\\n############################################################\\n')\n","for coluna in df.columns:\n","  qtd_dados_faltantes = df[coluna].isna().sum()                                                             \n","  porcentagem = qtd_dados_faltantes*100/len(df)\n","  print(\"{}: {}\".format(coluna, round(porcentagem, 2)))\n"," "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fncy93pmlDa2","executionInfo":{"status":"ok","timestamp":1650082775100,"user_tz":180,"elapsed":817,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"f1cfd36f-07af-4596-81fc-722eece6d984"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["############################################################\n"," PORCENTAGEM DE DADOS FALTANTES\n","############################################################\n","\n","NR_SEQ_REQUISICAO: 0.0\n","NR_SEQ_ITEM: 0.0\n","DT_REQUISICAO: 0.0\n","DS_TIPO_GUIA: 0.0\n","DT_NASCIMENTO: 0.0\n","NR_PRODUTO: 0.0\n","DS_TIPO_PREST_SOLICITANTE: 0.0\n","DS_CBO: 0.0\n","DS_TIPO_CONSULTA: 96.2\n","QT_TEMPO_DOENCA: 99.93\n","DS_UNIDADE_TEMPO_DOENCA: 99.93\n","DS_TIPO_DOENCA: 99.77\n","DS_INDICACAO_ACIDENTE: 7.77\n","DS_TIPO_SAIDA: 100.0\n","DS_TIPO_INTERNACAO: 70.44\n","DS_REGIME_INTERNACAO: 70.44\n","DS_CARATER_ATENDIMENTO: 0.0\n","DS_TIPO_ACOMODACAO: 70.46\n","QT_DIA_SOLICITADO: 70.75\n","CD_GUIA_REFERENCIA: 82.96\n","DS_TIPO_ATENDIMENTO: 29.4\n","CD_CID: 56.97\n","DS_INDICACAO_CLINICA: 21.2\n","DS_TIPO_ITEM: 0.0\n","CD_ITEM: 0.0\n","DS_ITEM: 0.0\n","DS_CLASSE: 0.0\n","DS_SUBGRUPO: 0.0\n","DS_GRUPO: 0.0\n","QT_SOLICITADA: 0.0\n","DS_STATUS_ITEM: 0.0\n"]}]},{"cell_type":"code","source":["#Verificação de valores missing  \n","print('#### ATRIBUTOS QUE POSSUEM MAIS DA METADE DE DADOS FALTANTES\\n')\n","for coluna in df.columns:\n","  qtd_dados_faltantes = df[coluna].isna().sum()                                                             \n","  if qtd_dados_faltantes > len(df)/2:\n","    print(\"Qtd. de valores faltantes em {}: {}\".format(coluna, qtd_dados_faltantes))\n"," "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Vxyx8VG4Hh0I","executionInfo":{"status":"ok","timestamp":1649912633274,"user_tz":180,"elapsed":779,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"375bef1f-2887-4151-c387-1b9d7fb4b6bc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["#### ATRIBUTOS QUE POSSUEM MAIS DA METADE DE DADOS FALTANTES\n","\n","Qtd. de valores faltantes em DS_TIPO_CONSULTA: 396851\n","Qtd. de valores faltantes em QT_TEMPO_DOENCA: 412259\n","Qtd. de valores faltantes em DS_UNIDADE_TEMPO_DOENCA: 412258\n","Qtd. de valores faltantes em DS_TIPO_DOENCA: 411573\n","Qtd. de valores faltantes em DS_TIPO_SAIDA: 412540\n","Qtd. de valores faltantes em DS_TIPO_INTERNACAO: 290612\n","Qtd. de valores faltantes em DS_REGIME_INTERNACAO: 290611\n","Qtd. de valores faltantes em DS_TIPO_ACOMODACAO: 290686\n","Qtd. de valores faltantes em QT_DIA_SOLICITADO: 291855\n","Qtd. de valores faltantes em CD_GUIA_REFERENCIA: 342242\n","Qtd. de valores faltantes em CD_CID: 235017\n"]}]},{"cell_type":"code","source":["#Verificação de valores missing  \n","print('#### ATRIBUTOS QUE POSSUEM MAIS DE 70% DE DADOS FALTANTES\\n')\n","for coluna in df.columns:\n","  qtd_dados_faltantes = df[coluna].isna().sum()                                                             \n","  if qtd_dados_faltantes > len(df)*0.7:\n","    print(\"Qtd. de valores faltantes em {}: {}\".format(coluna, qtd_dados_faltantes))\n"," "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650082788398,"user_tz":180,"elapsed":754,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"d2456b61-1e1b-4fe0-d9cb-ae35fb9cfcd5","id":"KDQkqolxBlCJ"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["#### ATRIBUTOS QUE POSSUEM MAIS DE 70% DE DADOS FALTANTES\n","\n","Qtd. de valores faltantes em DS_TIPO_CONSULTA: 396851\n","Qtd. de valores faltantes em QT_TEMPO_DOENCA: 412259\n","Qtd. de valores faltantes em DS_UNIDADE_TEMPO_DOENCA: 412258\n","Qtd. de valores faltantes em DS_TIPO_DOENCA: 411573\n","Qtd. de valores faltantes em DS_TIPO_SAIDA: 412540\n","Qtd. de valores faltantes em DS_TIPO_INTERNACAO: 290612\n","Qtd. de valores faltantes em DS_REGIME_INTERNACAO: 290611\n","Qtd. de valores faltantes em DS_TIPO_ACOMODACAO: 290686\n","Qtd. de valores faltantes em QT_DIA_SOLICITADO: 291855\n","Qtd. de valores faltantes em CD_GUIA_REFERENCIA: 342242\n"]}]},{"cell_type":"code","source":["#Verificação de valores missing  \n","print('#### ATRIBUTOS QUE POSSUEM MAIS DE 95% DE DADOS FALTANTES\\n')\n","for coluna in df.columns:\n","  qtd_dados_faltantes = df[coluna].isna().sum()                                                             \n","  if qtd_dados_faltantes > (len(df)*0.95):\n","    print(\"Qtd. de valores faltantes em {}: {}\".format(coluna, qtd_dados_faltantes))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ULtbfmoWKuZe","executionInfo":{"status":"ok","timestamp":1649303833409,"user_tz":180,"elapsed":987,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"7e8ed076-f7cd-45db-90b8-e056a5afc181"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["#### ATRIBUTOS QUE POSSUEM MAIS DE 90% DE DADOS FALTANTES\n","\n","Qtd. de valores faltantes em DS_TIPO_CONSULTA: 396851\n","Qtd. de valores faltantes em QT_TEMPO_DOENCA: 412259\n","Qtd. de valores faltantes em DS_UNIDADE_TEMPO_DOENCA: 412258\n","Qtd. de valores faltantes em DS_TIPO_DOENCA: 411573\n","Qtd. de valores faltantes em DS_TIPO_SAIDA: 412540\n"]}]},{"cell_type":"code","source":["#Verificação de valores missing  \n","print('#### ATRIBUTOS QUE POSSUEM TODOS OS DADOS FALTANTES\\n')\n","for coluna in df.columns:\n","  qtd_dados_faltantes = df[coluna].isna().sum()                                                             \n","  if qtd_dados_faltantes == len(df):\n","    print(\"Qtd. de valores faltantes em {}: {}\".format(coluna, qtd_dados_faltantes))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2efWMueJCnQQ","executionInfo":{"status":"ok","timestamp":1649301711419,"user_tz":180,"elapsed":599,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"aa438cdc-4a3f-42bc-be42-d11e0d99eee6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["#### ATRIBUTOS QUE POSSUEM TODOS OS DADOS FALTANTES\n","\n","Qtd. de valores faltantes em DS_TIPO_SAIDA: 412540\n"]}]},{"cell_type":"markdown","source":["##Dados redundantes\n","Identificação de dados duplicados"],"metadata":{"id":"536G0KIcDLi7"}},{"cell_type":"code","metadata":{"id":"qTpYhxR9WPP7","colab":{"base_uri":"https://localhost:8080/","height":113},"executionInfo":{"status":"ok","timestamp":1649783097550,"user_tz":180,"elapsed":1857,"user":{"displayName":"JEFFERSON CARVALHO SILVA","userId":"18358345737462067048"}},"outputId":"b5e9bcfc-ad33-4d14-e590-0d7061130459"},"source":["#Verifica se tem duplicates\n","df[df.duplicated()]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Empty DataFrame\n","Columns: [NR_SEQ_REQUISICAO, NR_SEQ_ITEM, DT_REQUISICAO, DS_TIPO_GUIA, DT_NASCIMENTO, NR_PRODUTO, DS_TIPO_PREST_SOLICITANTE, DS_CBO, DS_TIPO_CONSULTA, QT_TEMPO_DOENCA, DS_UNIDADE_TEMPO_DOENCA, DS_TIPO_DOENCA, DS_INDICACAO_ACIDENTE, DS_TIPO_SAIDA, DS_TIPO_INTERNACAO, DS_REGIME_INTERNACAO, DS_CARATER_ATENDIMENTO, DS_TIPO_ACOMODACAO, QT_DIA_SOLICITADO, CD_GUIA_REFERENCIA, DS_TIPO_ATENDIMENTO, CD_CID, DS_INDICACAO_CLINICA, DS_TIPO_ITEM, CD_ITEM, DS_ITEM, DS_CLASSE, DS_SUBGRUPO, DS_GRUPO, QT_SOLICITADA, DS_STATUS_ITEM]\n","Index: []"],"text/html":["\n","  <div id=\"df-a126c4c1-87bc-47c4-a991-eb22ae9a1cad\">\n","    <div class=\"colab-df-container\">\n","      <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: 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<th>CD_CID</th>\n","      <th>DS_INDICACAO_CLINICA</th>\n","      <th>DS_TIPO_ITEM</th>\n","      <th>CD_ITEM</th>\n","      <th>DS_ITEM</th>\n","      <th>DS_CLASSE</th>\n","      <th>DS_SUBGRUPO</th>\n","      <th>DS_GRUPO</th>\n","      <th>QT_SOLICITADA</th>\n","      <th>DS_STATUS_ITEM</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a126c4c1-87bc-47c4-a991-eb22ae9a1cad')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 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keep=False)]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113},"id":"-lpggP4sD0rQ","executionInfo":{"status":"ok","timestamp":1649302033688,"user_tz":180,"elapsed":1708,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"fcb7a9ce-9fe4-4645-da32-a53d13e90021"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Empty DataFrame\n","Columns: [NR_SEQ_REQUISICAO, NR_SEQ_ITEM, DT_REQUISICAO, DS_TIPO_GUIA, DT_NASCIMENTO, NR_PRODUTO, DS_TIPO_PREST_SOLICITANTE, DS_CBO, DS_TIPO_CONSULTA, QT_TEMPO_DOENCA, DS_UNIDADE_TEMPO_DOENCA, DS_TIPO_DOENCA, DS_INDICACAO_ACIDENTE, DS_TIPO_SAIDA, DS_TIPO_INTERNACAO, DS_REGIME_INTERNACAO, DS_CARATER_ATENDIMENTO, DS_TIPO_ACOMODACAO, QT_DIA_SOLICITADO, CD_GUIA_REFERENCIA, DS_TIPO_ATENDIMENTO, CD_CID, DS_INDICACAO_CLINICA, DS_TIPO_ITEM, CD_ITEM, DS_ITEM, DS_CLASSE, DS_SUBGRUPO, DS_GRUPO, QT_SOLICITADA, DS_STATUS_ITEM]\n","Index: []"],"text/html":["\n","  <div 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<th>DS_TIPO_SAIDA</th>\n","      <th>DS_TIPO_INTERNACAO</th>\n","      <th>DS_REGIME_INTERNACAO</th>\n","      <th>DS_CARATER_ATENDIMENTO</th>\n","      <th>DS_TIPO_ACOMODACAO</th>\n","      <th>QT_DIA_SOLICITADO</th>\n","      <th>CD_GUIA_REFERENCIA</th>\n","      <th>DS_TIPO_ATENDIMENTO</th>\n","      <th>CD_CID</th>\n","      <th>DS_INDICACAO_CLINICA</th>\n","      <th>DS_TIPO_ITEM</th>\n","      <th>CD_ITEM</th>\n","      <th>DS_ITEM</th>\n","      <th>DS_CLASSE</th>\n","      <th>DS_SUBGRUPO</th>\n","      <th>DS_GRUPO</th>\n","      <th>QT_SOLICITADA</th>\n","      <th>DS_STATUS_ITEM</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cefaedb9-765e-4e49-aa7b-01cfd3dd0aa2')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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da quantidade de exemplos;\n","  * 6 possuem uma quantidade de dados faltantes superior a 95% da quantidade de exemplos;\n","  * 1 tabela possui todos os dados faltantes.\n"],"metadata":{"id":"UIjF97TcTGyQ"}},{"cell_type":"markdown","source":["## Análise aprofundada dos atributos\n","Verificação de inconsistências, redundâncias, outliers e desbalanceamento."],"metadata":{"id":"aqVeWriuR-F7"}},{"cell_type":"markdown","source":["### Atributos númericos"],"metadata":{"id":"ul0j_bLEk-f6"}},{"cell_type":"markdown","source":["#### Visualização gráfica geral de todos os dados"],"metadata":{"id":"4YBYdOWvHKRh"}},{"cell_type":"code","source":["# Análise visual geral de todos os dados\n","plt.style.use('seaborn-whitegrid')\n","df.hist(bins=18,figsize=(20,20))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"sJOozy1iSELD","executionInfo":{"status":"ok","timestamp":1648761468576,"user_tz":180,"elapsed":3098,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"47f53c86-8f0e-41c0-d91f-5629873c8361"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x1440 with 12 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dos valores da coluna NR_PRODUTO\n","pd.unique((df['NR_PRODUTO']))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QZLJz8rYHTcJ","executionInfo":{"status":"ok","timestamp":1649357701433,"user_tz":180,"elapsed":293,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"4338be8d-8c00-4b46-dd54-eda768fcaff4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1])"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["#Verificação dos valores da coluna QT_DIA_SOLICITACAO\n","\n","len(pd.unique((df['QT_DIA_SOLICITADO'])))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gHBGf2xoIXEx","executionInfo":{"status":"ok","timestamp":1649303942799,"user_tz":180,"elapsed":271,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"5b9d9941-c008-4918-f32c-5783fa97a4bb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["37"]},"metadata":{},"execution_count":43}]},{"cell_type":"code","source":["# variaveis_numericas_aux = list(variaveis_numericas)\n","# variaveis_numericas_aux.append('DS_STATUS_ITEM')\n","# variaveis_numericas_aux\n","\n","# sns.pairplot(df[variaveis_numericas_aux], hue='DS_STATUS_ITEM');\n","# plt.show()"],"metadata":{"id":"jhTu9_tyVDQe"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["####**Resultados preliminares**\n","\n","Dessa forma, temos que:\n","* Verificar o significado de NR_SEQ_REQUISICAO e NR_SEQ_ITEM;\n","* DT_REQUISICAO e DT_NASCIMENTO fora do formato DD/MM/AAAA;\n","  * Verificar se, durante a avaliação do modelo, serão apresentados dados fora desse padrão de datas. Se sim, analisar possível necessidade de normalização.\n","* NR_PRODUTO possui apenas um valor em todos os objetos;\n","* Como visto em etapas anteriores:\n","  * QT_TEMPO_DOENCA possui 95% de dados ausentes;\n","  * QT_DIA_SOLICITADO possui mais da metade de dados ausentes;\n","  * CD_GUIA_REFERENCIA possui 80%+ de dados ausentes, além disso, não há informações sobre os dados desse atributo no dicionário de dados;\n","\n","**Nesse sentido, conclui-se previamente que:**\n","1. é necessário verificar, a partir da associação entre QT_TEMPO_DOENCA e CD_CID, se é possível e viavél a substituição dos dados ausentes de QT_TEMPO_DOENCA;\n","2. os atributos NR_PRODUTO, CD_GUIA_REFERENCIA possivelmente podem ser descartados pelos motivos listados anteriormente;\n","3. é necessário verificar o significado de NR_SEQ_REQUISICAO, NR_SEQ_ITEM (manter); e,\n","5. é necessário analisar a corelação entre CD_ITEM, QT_SOLICITADA e DS_STATUS_ITEM para verificação de descarte.\n","\n","**Portanto, nas analises posteriores dessa subseção, NR_PRODUTO e CD_GUIA_REFERENCIA não serão averiguados com outras técnicas.**\n"],"metadata":{"id":"2si5ArKHJcqi"}},{"cell_type":"code","source":["pd.set_option('display.max_columns', None)\n","df.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":280},"id":"KY3PohSRcCeM","executionInfo":{"status":"ok","timestamp":1649359270224,"user_tz":180,"elapsed":286,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"33bb5667-c3f0-4845-9f34-ae48ea50bea4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   NR_SEQ_REQUISICAO  NR_SEQ_ITEM  DT_REQUISICAO  \\\n","0            1506266      4178993        2459565   \n","1            1586873      4433946        2459611   \n","\n","                                       DS_TIPO_GUIA  DT_NASCIMENTO  \\\n","0                       Guia de solicitac?o SP/SADT      2437002.0   \n","1  Guia de solicitac?o de prorrogac?o de internac?o      2428370.0   \n","\n","   NR_PRODUTO DS_TIPO_PREST_SOLICITANTE                         DS_CBO  \\\n","0           1                   CLINICA     Medico oncologista clinico   \n","1           1                  HOSPITAL  Medico em medicina intensiva    \n","\n","  DS_TIPO_CONSULTA  QT_TEMPO_DOENCA DS_UNIDADE_TEMPO_DOENCA DS_TIPO_DOENCA  \\\n","0              NaN              NaN                     NaN            NaN   \n","1              NaN              NaN                     NaN            NaN   \n","\n","  DS_INDICACAO_ACIDENTE  DS_TIPO_SAIDA DS_TIPO_INTERNACAO  \\\n","0          N?o acidente            NaN                NaN   \n","1          N?o acidente            NaN            Clinica   \n","\n","  DS_REGIME_INTERNACAO DS_CARATER_ATENDIMENTO DS_TIPO_ACOMODACAO  \\\n","0                  NaN                Eletiva                NaN   \n","1           Hospitalar    Urgencia/Emergencia         UTI Adulto   \n","\n","   QT_DIA_SOLICITADO  CD_GUIA_REFERENCIA DS_TIPO_ATENDIMENTO CD_CID  \\\n","0                NaN                 NaN       Quimioterapia    C90   \n","1                2.0           1573390.0                 NaN    NaN   \n","\n","                                DS_INDICACAO_CLINICA   DS_TIPO_ITEM   CD_ITEM  \\\n","0          Mieloma mult e neopl malig de plasmocitos  Procedimentos  60026251   \n","1  Solicitac?o de prorrogac?o de 2 diarias, refer...  Procedimentos  20203012   \n","\n","                                             DS_ITEM  \\\n","0  TAXA DE SALA MANIPULAC?O QUIMIOTERAPICOS ? CAPELA   \n","1  ASSISTENCIA FISIATRICA RESPIRATORIA EM PACIENT...   \n","\n","                  DS_CLASSE                          DS_SUBGRUPO  \\\n","0  FORA DO CENTRO CIRURGICO                         TAXA DE SALA   \n","1   REABILITAC?ES - SESS?ES  PROCEDIMENTOS CLINICOS HOSPITALARES   \n","\n","           DS_GRUPO  QT_SOLICITADA DS_STATUS_ITEM  \n","0             TAXAS              1     Autorizado  \n","1  PROCED. CLINICOS              6     Autorizado  "],"text/html":["\n","  <div id=\"df-226619e8-1d54-43ff-be53-710651508250\">\n","    <div class=\"colab-df-container\">\n","      <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>NR_SEQ_REQUISICAO</th>\n","      <th>NR_SEQ_ITEM</th>\n","      <th>DT_REQUISICAO</th>\n","      <th>DS_TIPO_GUIA</th>\n","      <th>DT_NASCIMENTO</th>\n","      <th>NR_PRODUTO</th>\n","      <th>DS_TIPO_PREST_SOLICITANTE</th>\n","      <th>DS_CBO</th>\n","      <th>DS_TIPO_CONSULTA</th>\n","      <th>QT_TEMPO_DOENCA</th>\n","      <th>DS_UNIDADE_TEMPO_DOENCA</th>\n","      <th>DS_TIPO_DOENCA</th>\n","      <th>DS_INDICACAO_ACIDENTE</th>\n","      <th>DS_TIPO_SAIDA</th>\n","      <th>DS_TIPO_INTERNACAO</th>\n","      <th>DS_REGIME_INTERNACAO</th>\n","      <th>DS_CARATER_ATENDIMENTO</th>\n","      <th>DS_TIPO_ACOMODACAO</th>\n","      <th>QT_DIA_SOLICITADO</th>\n","      <th>CD_GUIA_REFERENCIA</th>\n","      <th>DS_TIPO_ATENDIMENTO</th>\n","      <th>CD_CID</th>\n","      <th>DS_INDICACAO_CLINICA</th>\n","      <th>DS_TIPO_ITEM</th>\n","      <th>CD_ITEM</th>\n","      <th>DS_ITEM</th>\n","      <th>DS_CLASSE</th>\n","      <th>DS_SUBGRUPO</th>\n","      <th>DS_GRUPO</th>\n","      <th>QT_SOLICITADA</th>\n","      <th>DS_STATUS_ITEM</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1506266</td>\n","      <td>4178993</td>\n","      <td>2459565</td>\n","      <td>Guia de solicitac?o SP/SADT</td>\n","      <td>2437002.0</td>\n","      <td>1</td>\n","      <td>CLINICA</td>\n","      <td>Medico oncologista clinico</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>N?o acidente</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Eletiva</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Quimioterapia</td>\n","      <td>C90</td>\n","      <td>Mieloma mult e neopl malig de plasmocitos</td>\n","      <td>Procedimentos</td>\n","      <td>60026251</td>\n","      <td>TAXA DE SALA MANIPULAC?O QUIMIOTERAPICOS ? CAPELA</td>\n","      <td>FORA DO CENTRO CIRURGICO</td>\n","      <td>TAXA DE SALA</td>\n","      <td>TAXAS</td>\n","      <td>1</td>\n","      <td>Autorizado</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1586873</td>\n","      <td>4433946</td>\n","      <td>2459611</td>\n","      <td>Guia de solicitac?o de prorrogac?o de internac?o</td>\n","      <td>2428370.0</td>\n","      <td>1</td>\n","      <td>HOSPITAL</td>\n","      <td>Medico em medicina intensiva</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>N?o acidente</td>\n","      <td>NaN</td>\n","      <td>Clinica</td>\n","      <td>Hospitalar</td>\n","      <td>Urgencia/Emergencia</td>\n","      <td>UTI Adulto</td>\n","      <td>2.0</td>\n","      <td>1573390.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Solicitac?o de prorrogac?o de 2 diarias, refer...</td>\n","      <td>Procedimentos</td>\n","      <td>20203012</td>\n","      <td>ASSISTENCIA FISIATRICA RESPIRATORIA EM PACIENT...</td>\n","      <td>REABILITAC?ES - SESS?ES</td>\n","      <td>PROCEDIMENTOS CLINICOS HOSPITALARES</td>\n","      <td>PROCED. 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index=atributo)\n","crosstb"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":708},"id":"F8yZdnV_P-_7","executionInfo":{"status":"ok","timestamp":1649305401050,"user_tz":180,"elapsed":744,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"5dbfad8c-c87c-4f1f-9047-e2d8b14e7a74"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DS_STATUS_ITEM   Autorizado  Negado  Parcialmente autorizado\n","QT_TEMPO_DOENCA                                             \n","1.0                      38       1                        0\n","2.0                      20      10                        0\n","3.0                      30      17                        1\n","4.0                       4       2                        0\n","5.0                      29       0                        0\n","6.0                       2       6                        0\n","7.0                       8       1                        0\n","8.0                       4       4                        0\n","9.0                       2       0                        0\n","10.0                      3       1                        0\n","11.0                     36       3                        0\n","12.0                      3       0                        0\n","13.0                      1       0                        0\n","15.0                      1       0                        0\n","19.0                     40       0                        0\n","30.0                      2       3                        0\n","35.0                      3       0                        0\n","40.0                      1       0                        0\n","47.0                      3       0                        0\n","92.0                      2       0                        0"],"text/html":["\n","  <div id=\"df-d6832439-0340-4ef6-8aab-9ccd6dfce1fe\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style 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<td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4.0</th>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>5.0</th>\n","      <td>29</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>6.0</th>\n","      <td>2</td>\n","      <td>6</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>7.0</th>\n","      <td>8</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8.0</th>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>9.0</th>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>10.0</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>11.0</th>\n","      <td>36</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>12.0</th>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>13.0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15.0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>19.0</th>\n","      <td>40</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>30.0</th>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>35.0</th>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>40.0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>47.0</th>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>92.0</th>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["#### Cruzamento entre status e DT_REQUISICAO"],"metadata":{"id":"d0Ut3OdeTCPG"}},{"cell_type":"code","source":["classe = df['DS_STATUS_ITEM']\n","atributo = df['DT_REQUISICAO']\n","crosstb = pd.crosstab(columns=classe, index=atributo)\n","crosstb"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":455},"executionInfo":{"status":"ok","timestamp":1649914712840,"user_tz":180,"elapsed":430,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"b5bc3a29-9106-498f-87d9-671318bae59f","id":"Rp6Y2pCrTCPb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DS_STATUS_ITEM  Autorizado  Negado  Parcialmente autorizado\n","DT_REQUISICAO                                              \n","2459075                237      27                       13\n","2459076                734      97                        6\n","2459077                304     108                        5\n","2459078                 15      33             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0Ufw0N/37umU/bGIsZmY2BJVJFOplutG8mZmNMGUSRfQy3Wh+PZLGSvqDpDsk3S3ppFS+g6RbJXVJulDSxql8kzTflZZP68dzMTOzCpS56qlNUgfZ2UNtmjTf1x1aXwT2j4jnJG0E3Czpl8Dnge9FxAWSfgQcC5yR/j4dEdPTECHfAo7o/9MyM7NmKZMovpSbXlS3rH5+Pem+2rXblG+UHgHsz7pfdC8A5pMlioPTNMDFwOmS5Ptzm5m1TpnRYxf0tkzSdn1tL2k02e8vpgM/AP4EPBMRa9Iq3aw7M5kCPJL2u0bSKmBr4Im+9mNmZtUoO3rsXpIOk7Rtmt9N0k+B3/a1bUS8HBEzgTaywQV3HkzAaf9zJS2StKinp2ew1ZmZWYE+E4WkbwNnAx8AfiHpZOBa4FZgRtkdRcQzwI3AXsB4SbWzmTbWDQWyApia9jsG2BJ4skFdZ0ZEe0S0T5w4sWwIZmY2AGX6KN4D7B4RL0jaiqxpaNeIeLCvDSVNBP4aEc9I2hT4R7IO6huBw4ALgNnAFWmTK9P879PyG9w/YWbWWmUSxQu10WMj4mlJy8skiWQysCD1U4wCLoqIqyQtAy5IZydLgLPS+mcB50nqAp4CPtSP52JmZhUokyh2lJQfSnyH/HxE9DowYEQsBXZvUH4/WX9FffkLwAdLxGRmZhtImURxcN38d6oIxMzMhqYyl8f+ukxFki6JiA8MPiQzMxtKSl0eW9KOTazLzMyGiGYmCl+dZGY2AjUzUZiZ2QjUzEThIcfNzEagMne4Oyci5pSo6/jBh1OtUaufYuyyq1odRml64c8AxNgtWhxJOaNWPwW8utVhmFmTlbk8drcyFUXEtYOMpVLTp09vdQj9tnz5swDMeO1w+fB99bB8nc2sWJlEsZmk3emlaSkibm9uSNWYN29eq0Pot1rMHR0dfaxpZladMoliCtmP7Bolitq9Jcwq9/Bzozl50bhWh1HK46uz7r9Jm61tcSTlPfzcaHZqdRA2JJVJFF0R4WRgLTXcmrReWr4cgLHTSg+w3HI7MfxeZ9swyiQKs5Ybbk2Hbja0kaTM5bF/u5pJ0sQ0dLiZmb1ClEkU10maL+kJ4I/AfZJ6JP1LxbGZmdkQUCZRfA7YG3hzREyIiK2AtwJ7S/pcpdGZmVnLlUkUHwWOjIgHagXpfhIfAY6uKjAzMxsayiSKjSLiifrCiOgBNmp+SGZmNpSUSRQvDXCZmZmNAGUuj32jpD83KBcwtsnxmJnZEFPmDnejN0QgZmY2NPl+FGZmVsiJwszMCjlRmJlZIScKMzMr5ERhZmaFnCjMzKyQE4WZmRVyojAzs0KVJgpJUyXdKGmZpLslfSaVT5B0naTl6e9WqVySOiR1SVoqaY8q4zMzs75VfUaxBvhCROwC7AkcJ2kX4ATg+oiYAVyf5gHeDcxIj7nAGRXHZ2Zmfag0UUTEoxFxe5p+FrgHmAIcDCxIqy0ADknTBwPnRuYWYLykyVXGaGZmxTZYH4WkacDuwK3ApIh4NC16DJiUpqcAj+Q2605l9XXNlbRI0qKenp7KYjYzsw2UKCSNAy4BPhsR641EGxEBRH/qi4gzI6I9ItonTvQtvM3MqlR5opC0EVmSOD8iLk3Fj9ealNLflal8BTA1t3lbKjMzsxap+qonAWcB90TEd3OLrgRmp+nZwBW58qPT1U97AqtyTVRmZtYCZW5cNBh7k91z+05Jnansn4FTgIskHQs8BByell0NHAh0AauBYyqOz8zM+lBpooiIm8nuhNfIAQ3WD+C4KmMyM7P+8S+zzcyskBOFmZkVcqIwM7NCThRmZlbIicLMzAo5UZiZWSEnCjMzK+REYWZmhZwozMyskBOFmZkVcqIwM7NCThRmZlbIicLMzAo5UZiZWSEnCjMzK+REYWZmhZwozMyskBOFmZkVcqIwM7NCThRmZlbIicLMzAo5UZiZWSEnCjOzOp2dnXR2drLvvvu2OpQhwYnCzKwXEdHqEIaEMa0OwMxsoDo6Oujq6mpqnZ2dnevN77PPPsycObNp9U+fPp158+Y1rb4NwWcUZmZWyGcUZjZsVfHNfJ999vm7so6OjqbvZzip9IxC0tmSVkq6K1c2QdJ1kpanv1ulcknqkNQlaamkPaqMzczMyqm66ekc4F11ZScA10fEDOD6NA/wbmBGeswFzqg4NjMzK6HSRBERNwFP1RUfDCxI0wuAQ3Ll50bmFmC8pMlVxmdmZn1rRWf2pIh4NE0/BkxK01OAR3LrdaeyvyNprqRFkhb19PRUF6mZmbX2qqfILlLu94XKEXFmRLRHRPvEiRMriMzMzGpakSgerzUppb8rU/kKYGpuvbZUZmZmLdSKRHElMDtNzwauyJUfna5+2hNYlWuiMjOzFqn0dxSS/hOYBWwjqRv4GnAKcJGkY4GHgMPT6lcDBwJdwGrgmCpjMzOzcipNFBFxZC+LDmiwbgDHVRmPmZn1n4fwMDOzQk4UZmZWyGM9DVIVo1fWLF++HKhmPJvhOIJlFao6flUeO/Dxsw3LiWII23TTTVsdgg2Qj52NJE4Ug+RvdcObj59Z39xHYWaWs+222643P3myh5xzojAzy7n44ovXm7/wwgtbFMnQ4URhZlandlbhs4mM+yjMzOrUn1W80vmMwszMCjlRmJlZIScKMzMr5ERhZmaFlA3aOnxJ6iEbrnyk2gZ4otVB2ID42A1vI/34bR8RpW4ROuwTxUgnaVFEtLc6Dus/H7vhzcdvHTc9mZlZIScKMzMr5EQx9J3Z6gBswHzshjcfv8R9FGZmVshnFGZmVsiJwszMCjlRDIKkQySFpJ1LrPtZSZsNYB8/lrTLwCL8Wx3TJN01mDpeSdIx/U5u/ouS5le8zzmSTq9yH4Ml6WVJnZLukvSzgbyfG9T5uxLrPDfY/fRR/3hJ/1TlPtJ+Zkn6hwFs9z5JJzRh/wslDehyXyeKwTkSuDn97ctngX79Y0kaHREfj4hlAwnOBuxF4FBJ27Q6kCHm+YiYGRG7Ai8BnyyzkaReR6mOiH5/cFZgPFB5ogBmAf16vpLGRMSVEXFKNSGV40QxQJLGAW8DjgU+lMpmSboqt87p6ZviPOA1wI2SbkzLjpR0Z/p29q3cNs9J+o6kO4C9at8C0reKzvT4o6QH0vr/Ium2VM+ZkpTK3yTpjlTPcbn6x0r6Sdr3Ekn7Vf5iDT9ryK54+Vz9AkkTJV2SXvPbJO2dK79O0t3pLPChWqKRdLmkxWnZ3Fxdx0i6T9IfgL1z5dMk3SBpqaTrJW1X+TPuv98A0yW9V9Kt6b30X5ImAUiaL+k8Sb8FzpM0SdJltfdk7Zt17WxB0rj0XG9P782D63eY/r9+LekKSfdLOkXSUZL+kLZ5bVqvt2M0X9LZ6X/q/vR/CXAK8Nr0v/XttO6X0rZLJZ3U6AWQdIakRem4npQrfzB37NvT/qaRJdbPpf28vbfjLOkcST+SdCvw78qdbeY+AzolPS9pX0lvkfT7dAx+J+l1ad1NJV0g6R5JlwGb5mJs+PnTq4jwYwAP4CjgrDT9O+BNZN8YrsqtczowJ00/CGyTpl8DPAxMJLsnyA3AIWlZAIfn6lgItNft+yLguDQ9IVd+HvDeNL0U2CdNfxu4K01/ATg7Te+c4hjb6tdzKD2A54At0jHbEvgiMD8t+ynwtjS9HXBP7lifmKbflY5j7XhPSH83Be4CtgYm594DGwO/BU5P6/0cmJ2mPwZc3urXpPa6pL9jgCuA/w1sxbqrJz8OfCdNzwcWA5um+QuBz6bp0cCWDercIk1vA3Tl6q2tMwt4Jr12mwArgJPSss8Ap/ZxjOaT/a9ukvbxJLARMK32/5HWeyfZFwWRfZm+qva/VPd6TMg9n4XAbmn+wdyxbwcW5vb/xdz2DY8zcE7a5+g0P6f23sht+16yZL0R2Xt1TCp/B3BJmv486/7XdyP7AtROwedPbw/fuGjgjgROS9MXpPmrel99PW8me/P0AEg6H9gHuBx4Gbiktw0lfZmsCeAHqWi/VLYZMAG4W9JvgPERcVNa5zzg3Wn6bcD3ASLiXkkPATuRJRZLIuLPks4F5gHP5xa9A9glnbgBbKF1Z5fvT9teI+np3DbzJL0/TU8FZgCvZv33wIVkxwFgL+DQNH0e8O/NfG6DsKmkzjT9G+As4HXAhZImkyW8B3LrXxkRtdduf+BogIh4GVhVV7eAb0jaB1gLTAEmAY/VrXdbRDwKIOlPwLWp/E6gdnbc2zEC+EVEvAi8KGll2ke9d6bHkjQ/juyY3VS33uHpDHEMWfLahf79HxUd55+l1+nvSJpB9uVvv4j4q6RXAwtSeZAlD8g+UzoAImKppFpsRZ8/DTlRDICkCWRv/DdICrJvFEH2LSvfnDd2ANW/UPAGeQfwQbKDiqSxwA/JzjgeUdbhOpB9WmOnArcDP8mVjQL2jIgX8ivmPpSoK59F9sG1V0SslrSQ4XuMno+ImfkCSd8HvhsRV6bnOj+3+C/9qPsosm+4b0offg/S+HV6MTe9Nje/lnWfZ0XHKL/9yzT+DBTwzYj4j96ClbQD2ZnmmyPiaUnn5OJdw7rPgYEe64avXUp4FwGfqCVM4F+BGyPi/amJa+EA99kr91EMzGHAeRGxfURMi4ipZN+kRpF9k9lE0njggNw2zwKbp+k/APtK2kbSaLKzkV8X7VDS9sAPgA/mvqXV3oRPpDfQYQAR8QzwjKS3peVH5ar6TW1e0k5kp+Z/7N/Tf2WIiKfI/imPzRVfC3y6NiOp9sH5W+DwVPZOsiYZyJqunk5JYmdgz1R+K9l7YGtJG5F9Aaj5Hanfi+xY/aZpT6r5tiRrAgKYXbDe9WRNVUgaLWnLBvWsTEliP2D7QcTU2zHqTf5/E+BXwMdqZyGSpkjatm6bLcg+zFcp65d5d27Zg2RN0QAfKNjPQI7z2cBPIiK/bv4YzMmV3wR8OD2HXcman2AAnz9OFANzJHBZXdklZAf9IrJ26ItYd+oKWZvnNZJuTN8ETgBuBO4AFkfEFX3scw5Z2/blqSPr6pQQ/m/a36+A23LrHwP8IDUV5L/u/hAYJelOsnbjOelU3Br7Dll7ds08oD11QC5j3ZU/JwHvVHYZ8gfJmkyeBa4Bxki6h6zT9BaA9B6YD/yeLMnck9vHp4FjUlPBR8na34eq+cDPJC2meEjuz5A1k95J1ndRf8n3+WSv651kTVT3DiKm3o5RQxHxJPDb1LH77Yi4lqyf4/cpnotZ/wOeiLiD7P/73rTub3OLTwJOk7SI7Kyl5ufA+2ud2fTzOKcvi4eRJbFah3Y7WZPVNyUtYf0zpDOAcem993Wy152BfP54CA+zJpC0CfByRKyRtBdwRn0zjdlw5T4Ks+bYDrhI0iiy3xh8osXxmDWNzyjMzKyQ+yjMzKyQE4WZmRVyojAzs0JOFGZmVsiJwoYlrRvy+m5lg8x9IV1xhKTNJJ2fG/Ts5twQDo3q+kqqZ2mq863KBrDrlNQlaVXuuvXaYHadki5I08fklr+U9tupbNC6+ZK+WLe//KBxf7fvgjhrA0TemtZ9WFJPbt/TUt135so60rbnSFotafNcfacqG1Ldo+RaIV8ea8PV34aTSL+a/SnZr2W/RvbDpccj4g1p+euAvzaqJP3m4SBgj4h4MX1obhwR70/LZ5EN5HZQbpvXkw3b8nZJr4qIn5CG+VA29MR+EfFEmp/f2xPobd99PfGIeGvafg7Z8C2fytVJfv91uoCDgf+Xkur+rPtFr1mvfEZhw15ErATmAp9S9kk5mdwHYET8seDX55OBJ2rLI+KJiPjvPnZ5JNkgbteSffAO1ED2PRgXAEek6VlkvyZeU+H+bIRworARISLuJ/uWvy3ZeDjHKxuj/2Rlo2r25lpgqrL7QvxQ0r4ldncE2Yfuf1LuplXN3HcZN+aanvL31LgPmChpK7K4L2jS/myEc6KwESciOoEdyYZingDclpqLGq37HNkAbnOBHrIhs+f0VncaW+eJiHiYbKC73ZWNJtxrOL2H2b9998N+kd2JbmZEfK9u2aVkY5K9laE92KANIe6jsBFB0o5kA7CthL8lgEuBSyWtBQ5k/YH3/iYN674QWJgGgZtNdvOYRo4Edk59EZD1i3yAbHDGRp4ka2LK25zsBjz93WX75RsAAAEQSURBVHczXEg2ONyCiFirXoZHN8vzGYUNe5ImAj8iuwtYSNo7Na8gaWOykUof6mXb19U1Tc0sWHcU2VDib0jDy08j66Moan66CXhf7WojSYcCd0TEy/3Zd7NExEPAV8hGETYrxWcUNlzV7ra2EVmH7HnAd9Oy1wJnpI7tUcAv6P2ugeOA7yu7f8gasiuD5vay7tuBFXUdzjeR3YNkcu5GMn+T7ix2OnCzsptcrSS7ZWh/990fN0qqDW+9NCKOroup1xvymDXiQQHNzKyQm57MzKyQm57sFUHS1mRXKdU7IN3hbMiQdBmwQ13x8RHxq1bEY+amJzMzK+SmJzMzK+REYWZmhZwozMyskBOFmZkV+v/9VqBrLqAhsAAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### Atributos simbolicos/ textuais"],"metadata":{"id":"Nz5QLI1wlGkY"}},{"cell_type":"code","source":["pd.set_option('display.max_columns', None)\n","df.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":280},"id":"Np5d5ZwwfSfx","executionInfo":{"status":"ok","timestamp":1649302471969,"user_tz":180,"elapsed":306,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"e46a2986-2469-47cb-d582-ae62a59e188f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   NR_SEQ_REQUISICAO  NR_SEQ_ITEM  DT_REQUISICAO  \\\n","0            1506266      4178993        2459565   \n","1            1586873      4433946        2459611   \n","\n","                                       DS_TIPO_GUIA  DT_NASCIMENTO  \\\n","0                       Guia de solicitac?o SP/SADT      2437002.0   \n","1  Guia de solicitac?o de prorrogac?o de internac?o      2428370.0   \n","\n","   NR_PRODUTO DS_TIPO_PREST_SOLICITANTE                         DS_CBO  \\\n","0           1                   CLINICA     Medico oncologista clinico   \n","1           1                  HOSPITAL  Medico em medicina intensiva    \n","\n","  DS_TIPO_CONSULTA  QT_TEMPO_DOENCA DS_UNIDADE_TEMPO_DOENCA DS_TIPO_DOENCA  \\\n","0              NaN              NaN                     NaN            NaN   \n","1              NaN              NaN                     NaN            NaN   \n","\n","  DS_INDICACAO_ACIDENTE  DS_TIPO_SAIDA DS_TIPO_INTERNACAO  \\\n","0          N?o acidente            NaN                NaN   \n","1          N?o acidente            NaN            Clinica   \n","\n","  DS_REGIME_INTERNACAO DS_CARATER_ATENDIMENTO DS_TIPO_ACOMODACAO  \\\n","0                  NaN                Eletiva                NaN   \n","1           Hospitalar    Urgencia/Emergencia         UTI Adulto   \n","\n","   QT_DIA_SOLICITADO  CD_GUIA_REFERENCIA DS_TIPO_ATENDIMENTO CD_CID  \\\n","0                NaN                 NaN       Quimioterapia    C90   \n","1                2.0           1573390.0                 NaN    NaN   \n","\n","                                DS_INDICACAO_CLINICA   DS_TIPO_ITEM   CD_ITEM  \\\n","0          Mieloma mult e neopl malig de plasmocitos  Procedimentos  60026251   \n","1  Solicitac?o de prorrogac?o de 2 diarias, refer...  Procedimentos  20203012   \n","\n","                                             DS_ITEM  \\\n","0  TAXA DE SALA MANIPULAC?O QUIMIOTERAPICOS ? CAPELA   \n","1  ASSISTENCIA FISIATRICA RESPIRATORIA EM PACIENT...   \n","\n","                  DS_CLASSE                          DS_SUBGRUPO  \\\n","0  FORA DO CENTRO CIRURGICO                         TAXA DE SALA   \n","1   REABILITAC?ES - SESS?ES  PROCEDIMENTOS CLINICOS HOSPITALARES   \n","\n","           DS_GRUPO  QT_SOLICITADA DS_STATUS_ITEM  \n","0             TAXAS              1     Autorizado  \n","1  PROCED. CLINICOS              6     Autorizado  "],"text/html":["\n","  <div id=\"df-84fa1f1d-1a54-490f-9ffc-82b2579ee549\">\n","    <div class=\"colab-df-container\">\n","      <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>NR_SEQ_REQUISICAO</th>\n","      <th>NR_SEQ_ITEM</th>\n","      <th>DT_REQUISICAO</th>\n","      <th>DS_TIPO_GUIA</th>\n","      <th>DT_NASCIMENTO</th>\n","      <th>NR_PRODUTO</th>\n","      <th>DS_TIPO_PREST_SOLICITANTE</th>\n","      <th>DS_CBO</th>\n","      <th>DS_TIPO_CONSULTA</th>\n","      <th>QT_TEMPO_DOENCA</th>\n","      <th>DS_UNIDADE_TEMPO_DOENCA</th>\n","      <th>DS_TIPO_DOENCA</th>\n","      <th>DS_INDICACAO_ACIDENTE</th>\n","      <th>DS_TIPO_SAIDA</th>\n","      <th>DS_TIPO_INTERNACAO</th>\n","      <th>DS_REGIME_INTERNACAO</th>\n","      <th>DS_CARATER_ATENDIMENTO</th>\n","      <th>DS_TIPO_ACOMODACAO</th>\n","      <th>QT_DIA_SOLICITADO</th>\n","      <th>CD_GUIA_REFERENCIA</th>\n","      <th>DS_TIPO_ATENDIMENTO</th>\n","      <th>CD_CID</th>\n","      <th>DS_INDICACAO_CLINICA</th>\n","      <th>DS_TIPO_ITEM</th>\n","      <th>CD_ITEM</th>\n","      <th>DS_ITEM</th>\n","      <th>DS_CLASSE</th>\n","      <th>DS_SUBGRUPO</th>\n","      <th>DS_GRUPO</th>\n","      <th>QT_SOLICITADA</th>\n","      <th>DS_STATUS_ITEM</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1506266</td>\n","      <td>4178993</td>\n","      <td>2459565</td>\n","      <td>Guia de solicitac?o SP/SADT</td>\n","      <td>2437002.0</td>\n","      <td>1</td>\n","      <td>CLINICA</td>\n","      <td>Medico oncologista clinico</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>N?o acidente</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Eletiva</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Quimioterapia</td>\n","      <td>C90</td>\n","      <td>Mieloma mult e neopl malig de plasmocitos</td>\n","      <td>Procedimentos</td>\n","      <td>60026251</td>\n","      <td>TAXA DE SALA MANIPULAC?O QUIMIOTERAPICOS ? CAPELA</td>\n","      <td>FORA DO CENTRO CIRURGICO</td>\n","      <td>TAXA DE SALA</td>\n","      <td>TAXAS</td>\n","      <td>1</td>\n","      <td>Autorizado</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1586873</td>\n","      <td>4433946</td>\n","      <td>2459611</td>\n","      <td>Guia de solicitac?o de prorrogac?o de internac?o</td>\n","      <td>2428370.0</td>\n","      <td>1</td>\n","      <td>HOSPITAL</td>\n","      <td>Medico em medicina intensiva</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>N?o acidente</td>\n","      <td>NaN</td>\n","      <td>Clinica</td>\n","      <td>Hospitalar</td>\n","      <td>Urgencia/Emergencia</td>\n","      <td>UTI Adulto</td>\n","      <td>2.0</td>\n","      <td>1573390.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Solicitac?o de prorrogac?o de 2 diarias, refer...</td>\n","      <td>Procedimentos</td>\n","      <td>20203012</td>\n","      <td>ASSISTENCIA FISIATRICA RESPIRATORIA EM PACIENT...</td>\n","      <td>REABILITAC?ES - SESS?ES</td>\n","      <td>PROCEDIMENTOS CLINICOS HOSPITALARES</td>\n","      <td>PROCED. CLINICOS</td>\n","      <td>6</td>\n","      <td>Autorizado</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84fa1f1d-1a54-490f-9ffc-82b2579ee549')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-84fa1f1d-1a54-490f-9ffc-82b2579ee549 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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DS_TIPO_PREST_SOLICITANTE"],"metadata":{"id":"lNo5ZWH5i5-S"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648761469795,"user_tz":180,"elapsed":608,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"6fa1d9d1-289f-466d-8114-75b97ff15a5f","id":"j7noPG1Ai5-S"},"source":["df.DS_TIPO_PREST_SOLICITANTE.value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_TIPO_PREST_SOLICITANTE)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9cfae390>"]},"metadata":{},"execution_count":16},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["#### DS_CBO"],"metadata":{"id":"phPZYjbHiGRX"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":414},"executionInfo":{"status":"ok","timestamp":1649699608155,"user_tz":180,"elapsed":5658,"user":{"displayName":"André Ribeiro Miranda","userId":"04420503819125685723"}},"outputId":"d6e900bc-89be-4fd4-df09-81c5d54bd177","id":"MXdkfpQ4iGRY"},"source":["df.DS_CBO.value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_CBO)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f9703c76a10>"]},"metadata":{},"execution_count":10},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["#### DS_TIPO_CONSULTA"],"metadata":{"id":"nJ-oCVu9iGay"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648761475333,"user_tz":180,"elapsed":483,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"d9925c1d-613d-4db7-8ec9-c25b06aba284","id":"xQGhDDIdiGaz"},"source":["# df.DS_TIPO_PREST_SOLICITANTE.value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_TIPO_CONSULTA)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9ef28290>"]},"metadata":{},"execution_count":18},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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DS_UNIDADE_TEMPO_DOENCA"],"metadata":{"id":"nRzKQx1VjG7r"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648761475790,"user_tz":180,"elapsed":468,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"26299d98-7d9d-401e-82b8-70c7c7466c8c","id":"EPzisz-CjG7s"},"source":["# df.DS_TIPO_PREST_SOLICITANTE.value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_UNIDADE_TEMPO_DOENCA)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9e97de10>"]},"metadata":{},"execution_count":19},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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DS_TIPO_DOENCA"],"metadata":{"id":"vDLpwssljzjI"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648761476359,"user_tz":180,"elapsed":584,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"5ee8cc0e-67b1-4ce6-fb44-dc07c20c0504","id":"LnJvkpItjzjJ"},"source":["# df.DS_TIPO_DOENCA.value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_TIPO_DOENCA)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9f026190>"]},"metadata":{},"execution_count":20},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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DS_INDICACAO_ACIDENTE"],"metadata":{"id":"Cz5xkxPrj5m0"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648761476941,"user_tz":180,"elapsed":597,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"9ecf86f5-477b-4ee8-c16a-9d329535808e","id":"HgSJwggKj5m1"},"source":["# df.DS_INDICACAO_ACIDENTE.value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_INDICACAO_ACIDENTE)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9f10c450>"]},"metadata":{},"execution_count":21},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["#### DS_TIPO_INTERNACAO "],"metadata":{"id":"qk8VNiVfkAs5"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648761477364,"user_tz":180,"elapsed":437,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"aa473f2f-990c-4f95-be6b-a09ac310b443","id":"BxOj-e7VkAs5"},"source":["# df.'DS_TIPO_INTERNACAO', .value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_TIPO_INTERNACAO)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9e94e110>"]},"metadata":{},"execution_count":22},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["#### DS_REGIME_INTERNACAO"],"metadata":{"id":"kv7JGFOckbnN"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648761477680,"user_tz":180,"elapsed":329,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"3657f4a8-e02c-43cc-c581-7813f0b97d9c","id":"ATepfx8LkbnN"},"source":["# df.'DS_REGIME_INTERNACAO', .value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_REGIME_INTERNACAO)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9f0f5210>"]},"metadata":{},"execution_count":23},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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DS_CARATER_ATENDIMENTO"],"metadata":{"id":"SL20CZgikiYr"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":438},"executionInfo":{"status":"ok","timestamp":1648761478445,"user_tz":180,"elapsed":780,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"d258854c-e68e-459d-9711-c5442d602319","id":"BkQP4I5fkiYt"},"source":["# df.'DS_CARATER_ATENDIMENTO', .value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_CARATER_ATENDIMENTO)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9ef16b50>"]},"metadata":{},"execution_count":24},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["#### DS_CARATER_ATENDIMENTO"],"metadata":{"id":"gmFfeCqzkoaQ"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648761479028,"user_tz":180,"elapsed":596,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"81818c2b-c377-4f2d-f4d0-bc758eb5a7f3","id":"kI4T2OIgkoaR"},"source":["# df.'DS_TIPO_ACOMODACAO', .value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_TIPO_ACOMODACAO)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9ef5ed90>"]},"metadata":{},"execution_count":25},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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CM2fOYMOGDVAoFGjXrh2+/PJLdOnSpcU+j5dddnY2TExMpHFhHR0dvPXWW82erpOTE+RyOc6fP4/c3FxMmzYNkydPlo6FhITAwsICt27dwqJFi1BSUgILCwuUlZVJcTT03Tx27Bg2bNgAXV1duLq6qqRd33je47Kzs7FgwQLk5ubi9ddfh6bm/3/wZHFxMb766ivcuHEDZWVlsLe3x6JFi9T+Qcm2bdtw7NgxKBQKmJmZYeXKlTA1NUVwcDBu3ryJ4uJiZGZmYt++fQgJCamz3aZuvw6ov534uM2bNyM2Nha6urrQ0NDAd999B0NDwyeOgU6cOBFnz55FaWkp1q9fj7CwMFy+fBl6enrYsmULTE1Nn2oCtb7x2br6qJqamvV+FlOmTEG/fv1w6dIl5OTkYOTIkZg/f36D1yqTyXDx4kX89NNPOHbsmPQjw8rKSjg6OmLv3r0oKSnBihUr8OjRI5SVlWHixImYOnVqwxcliJqYhYWFKC4uVtlnZ2cn0tPTn3heXfuqrV69WmzcuFEIIcTUqVNFXFycEEKIa9euicGDB4vc3FwhhBDLli0TdnZ2QgghEhISxNixY6U4am7X/Ds9PV0KU238+PFi//79Qgghbt68Kezs7MQff/yh5l0ganrDhw8XI0aMEGPGjBFjxoyRvr8WFhZi9+7dQgghkpKSxJAhQ6QwkydPFidOnBBCCFFYWCjkcrnIzs4WQgiRnZ0thg4dKgoLC0V6erqwsLCQylV1WB8fH6FQKERWVpawtLQUGzZsEEIIcfnyZTF06FDp3JplY8OGDSIwMFAIUVXOevXqJS5evCgdX7hwoZTfgoICUVlZKYQQIjc3VwwdOlQUFBQ00R0jalnNVR8SvQwer7PGjBkjLC0tRUJCghBCCB8fH/Gvf/1LCFFVHw0ePFjcuHFD/Pbbb8LNzU0oFAohhJDqhOp6KjIyUghRVb8MHTpUlJWV1WrHPV5+8vLyhFKpFEIIcfv2bZX6a/jw4eLGjRu18l9X21AIIVauXCmWLVsmhFCtYxtKg6gxmqMuqe4H5ebmioEDB4rs7GyVvlFaWpqYOHGiKCoqEkIIkZqaKoYNGyaEEOLUqVNi1KhRoqioSCiVSuHn5yfWrVtXZ96ry1FpaalwdHQUV69erVW2GmpD1uzHPX7u/v37xdy5c1XSIWpqz1qXVYf9+uuvhRBVfScrKyvx/fffCyGEiIuLE97e3kKIhstbTQ8ePBAWFhaisLCwzrwqlUqRl5cn/e3n5ydCQ0OFEEKEh4cLa2trkZaWJoRouF9I1ByuXbsmhg8fLpRKpYiOjhbTp08XQggRFBSkMp7n7+8vlZuGxgomT54s/vGPf4iKigohRN31QM0649y5c2LChAnStoWFhVQeDx8+LKytraU23bZt24Svr68QQoiff/5ZfPLJJ6KsrEwIUVUHvvfee0KIqnJla2srMjMzhRBCBAQESOMl4eHhYvbs2VJ6Dx48EPb29uLmzZtCiKo6bPz48c96O//n1PX5ZmdnC1dXV+Hi4iL8/f1FVFRUg98HdT2p3VXzf3t6erqwtraWzq/Zrxg7dqyIiIgQQgiRnJwsevXqJX3H6vtu5ubmCjs7O3H79m0hRNV3sTo/T/N/+7PPPhPBwcFCCCHu3bsnrK2tpTG4xYsXS/0phUIh5s2bJ/bt21fv/aj5XY6KihJLliyR+ml79uwRn3/+uRCiqiwPGzZM5doaarep26+rr51YU35+vujfv7949OiREEKIoqIiUVFRodYY6MmTJ4UQQmzfvl30799fXL16VQhRNbdQX3muqa5+aH3js49/txr6LGqOxz58+FDY2dmJu3fv1nutNeMvKSlRmcM4fvy4mDJlinR+9f+z4uJiMXLkSHHr1q06r60aVx7Sc/Msy5WrVVRUIDY2FmFhYQCAsWPHIjw8HO7u7rhw4QIcHR3RoUMHAMB7772HH374oVF5LS4uxrVr1zBu3DgAwFtvvYXevXvj0qVLcHJyalTcRI1R32NLqx+HYW1tjZycHJSVlUFXV1flnOTkZGRkZKg8ykNDQwNpaWkwMTGBrq5urceburm5QVNTE2ZmZjA2NoaLiwsAoG/fvsjOzpbSiY6OxqFDh1BRUYGSkhKVX8N26dIFNjY2dV5PXl4eFi9ejLS0NGhpaaGwsBB3796t85e2RK+KxtSHRC+Tx+usmu9Ti4+Ph7+/P4CqFVDDhg1DYmIiPD09UVlZiYCAANjb22P48OFSmFatWmHMmDEAqlZa6Onp4c6dO9DX128wH+np6fD19UV2dja0tbXx4MED5ObmwtTUtMmu9XmkQVRTU9QlHTp0wMSJE7FlyxaVNuDZs2dx7949TJo0SdpXWVmJBw8eID4+Hu7u7lK5mzhxItasWdNgOrq6upg1axY2bNig8v5DAA22IR935swZhIaGoqSkBJWVlc9wxURP71nqsurzq/toffv2xaNHj6Ry1q9fP2m1YkPlrXqMQx1KpRLffvstzpw5A6VSicLCQujp6UnH33nnHenRwQ31C99++2210yRS18GDByGXy6GhoQFXV1esWrUK2dnZAKAynjd+/HjpnYhPGivw8PBo8FUrV65cwTfffIPCwkJoaGjgv//9r8rx6vLYt29fAJDanP369cOPP/4IADhx4gSuX7+OCRMmAACEEHj48KEUxzvvvANzc3MAgJWVFc6fP19nXi5fvoxevXpJK+PGjRuHFStWoLi4+IntWKpbx44dERcXh0uXLuGXX35BSEgIYmJi8J///KdZ0qvZ7qr+3/7GG2/A0NAQWVlZ0kpVoGpMOTU1FXK5HEDVOF3NeqS+7+bly5fRp08fdO/eHUDV+Pb69esBPN3/7cTERCxZsgQA0LlzZ5WnoZw4cQIpKSnS+yFLS0thZmam1j04ceIErly5grFjxwKAtBq32rvvvot27dpJ2w2129Tt16nTTjQwMMCbb76JBQsWYMiQIXB0dIS+vv4Tx0DbtGkjrdzs27cvOnXqJK0A7du3b73l+UnUGZ8FnvxZVI/HGhgYoEePHrh37x46d+5c57XW1Lp1a7i4uCA2NhYffvghIiMjpbZLaWkpli9fjhs3bkBDQwM5OTm4fv26yvf3cZw8pCbXrl07FBQUSI+uqaysRHFxsco/kKd14sQJFBUVSUtplUolHjx4gPv37zcYTktLC0IIabvmMnGiV0l1RVT9qIHKyspalZMQAjKZrM4XHGdkZKB169a1BqJqxqGlpVVnOr/++iv27t2LsLAwtGvXDocOHcL+/fulcG3atKk338uXL4eTkxM2b94MDQ0NjBgxguWUXhnNUR8SveoMDAwQFxeHxMREnD9/HuvXr0dkZGSj4vz888/h7+8PFxcXKJVKWFlZPXNd8+uvv0od3eZKg6im5q5LPv74Y4wcOVIaPK02dOhQrFu37qniWrFiBS5evAgA+Ne//qXyThcvLy/85z//QVJSkrQvKSmpwTZkTb///ju++uorHDx4EJ07d8bFixelxzcRvage7ztVb2tqaqoMpKpT3tq3bw8zMzOkpKRgyJAhtY4fOnQIv/zyC/bs2QN9fX2EhISoTJbUfLRwQ/1CoqZWXl6O2NhY6OjoSO/kqqioqPU6occ9aaygoXGG8vJy+Pj44Pvvv5d++Pz4a4hqlsea9VXN8imEwLhx4+Dj41NnOo+PlygUigaviZqWtrY2bG1tYWtri3HjxmHw4MEoKChoMMyECRNQXl6Otm3bIjQ0tNZxddpdjfnc1flu1qWp/m8LIbBlyxZ07tz5mcLOnDkT48ePr/N4zXqmKdpt6rYTtbS0sH//fly8eBEJCQnw8vLCjh07njgG+ni5r7ld3+faUFu3mjrjs8CTP4u6vmf1XWuvXr1Uwo4dOxZr1qyBh4cHLly4ILUxNmzYAFNTU3z99dfQ1tbGtGnTnthn1WzwKNEzGDRoEPbt2ydt79u3D1ZWVmjduvUzxxkeHo6lS5fixIkTOHHiBE6dOgUvLy9ERETAzs4Op0+fxh9//AGg6hdN1Tp37oz09HQUFhZCCIG4uLg649fX10dpaanUQNDX10fv3r2lwarbt2/j+vXrXA1FLzUbGxukpaUhISFB2peSkqIywf4sHj58CH19fRgbG6O8vBzh4eFqhy0qKsLrr78ODQ0NnDt3DmlpaY3KC9GLpDnqQ6JXhYODg9T5y83NxenTpzFw4EDk5eXh0aNHGDp0KObPnw8DAwOkp6cDqBpoOnToEICqzmRpaan0y9ya2rZti+LiYmm7qKgIb7zxBoCqNmV5eflT57e8vBybN29GVlZWnZOHTZEGUV2auy4xMDDARx99hK1bt0r7Bg8ejLNnz+LmzZvSvpSUFABVZfeHH35AcXExhBA4ePAgBg0aBKDqfTXR0dGIjo6uVTa1tLQwd+5clffFNNSG1NfXVynHxcXFaNWqFUxNTaFUKqUn0hC1pPrqsqfRUHl73KxZs/D1119LqxYB4Oeff0ZKSgqKiopgYmICfX19FBUVITY2tt40m6tfSFSX48ePo1u3bjhz5ow0pvftt99K422nTp1CXl4eACAiIkIqQ08zVvD4mF55eTkqKyulVYF1TRKpw8nJCdHR0cjKygJQtdLqypUrTwxXXQ6rWVtb4/r167h9+zaAqvco9unTh6sOG+HKlSvIyMiQtn/77TcYGRnB0NCwwXAHDhxAdHR0vd+Jpmh36evrw8LCQuq3pKSkIDU1FUDD301ra2tcvXpV+uHHgQMHpGNP83974MCBUpsqPT0d8fHx0jEnJyds27ZNmhjLy8uT+lpP4uTkhNDQUBQWFkrXcv369TrPfVK7TZ1+nbpjjcXFxcjLy4OdnR3mzJkDCwsL3Lx5s1nquobauk/yeB/1WT6L+q71cba2tiguLsaGDRvg4uIifX+LiorQqVMnaGtrIzU1VeVHffXhykNqcgEBAVi9ejU8PDygqakJc3Pzp/7Vak3Z2dm4cOGCtFS7moeHBxYtWoRZs2ZhxowZeP/996Gvr6/yiw0zMzN89NFH8PLyQocOHTBgwIA6C5WxsTE8PDzg4eEBIyMjhIWFYf369Vi6dCl27doFbW1trFu3jqtFqMXNmTNH5dcn1Y/0UIeRkRG2bNmCwMBArFmzBhUVFejcuTNCQkIalaehQ4ciJiYGI0aMgImJCWxtbfHrr7+qFdbX1xcrVqxAcHAw3n77bchkskblhehF0tT1IdGrZMmSJVi6dCk8PDwAAPPnz0fPnj3x22+/4YsvvkBlZSUUCgXeffddWFtbIzMzE8bGxrh+/Tp27NgBoOqXk3X92nPatGn48MMPoaenh927d0vtRSMjIwwdOhTGxsZq5bGoqAhyuRwKhQIVFRWwtbVFWFgYDAwMap37rGkQPcnzqEsmT56M7777Ttru2rUrAgMDERAQgNLSUlRUVOCdd96BpaUlhg0bhhs3bsDb2xtA1ePdZs6cqVY6bm5u2L59O/78808ADbchZTIZunXrhtGjR6N79+4ICgqCm5sb3N3dYWJigmHDhqk14EHUnOqry55GQ+Xtcd7e3tDT08OcOXNQWloKTU1N9OrVC35+fvD09MTx48fh5uaG9u3bo3///vWuJmioX8jH61NTCw8Pl8pINRsbGyiVSly4cAG2traYN28esrOz8dZbb0mPAn6asYK6xvTmzJmD8ePHw9jYGCNGjHimvA8YMABz587FzJkzpfagm5sb+vXr12A4BwcHfPvttxgzZgzs7OywZMkSrFu3DvPnz0dlZSXatWuHwMDAZ8oTVcnPz5ce/aqjo4PWrVvj3//+NzQ1G7dOqqnaXevWrcOiRYuwfft2WFhYSI8W1dfXr/e72b59e6xcuRIzZsyAnp4eXF1dpWNP8387ICAACxYsQGxsLN544w3Y29tLxxYvXozAwEDpMcKtWrXC4sWL1VqJ6OnpiYKCAkyePBlA1cq5999/v9aqN6CqHddQu02dfp26Y43FxcWYPXs2SktLIYRAnz594OrqCl1d3WYZA31Wj/dRn+WzqO9a6+Lp6YlNmzaprLycOXMmFixYgIMHD6Jbt24YMGDAE/OtIfjTIiIiIiIieoFlZGRg3LhxSExMbOmsEBEREVETCA4ORklJCRYuXNjSWSGi54T9upcLH1tKRERERERERERERERERAC48pCIiIiIiIiIiIiIiIiI/sKVh0REREREREREREREREQEgJOHRERERERERERERERERPQXTh4SEREREREREREREREREQBOHhIRERERERERERERERHRXzh5SEREREREREREREREREQAOHlIRERERET0XDg5OcHS0hI2NjawtbWFt7c39u7dC6VSCQDIysrC7NmzYW9vj/79+2P06NGIiIioN76kpCTY2NjAxsYG1tbWkMlk0raNjQ0yMzMxZcoUHDhwAACQmJiIXr16ScdHjBiB8PBwKb7y8nL885//hKOjIywtLeHq6oodO3ZACKH2Nfr7+6NPnz7Iycmpdezs2bOYNGkSbGxsMHDgQEyePBnHjx+XjmdlZcHX1xf29vawtrbG+PHjcfLkSZU4ZDIZHBwcUFlZKe2rqKiAg4MDZDKZyrknT57E+PHjYW1tDXt7e/j6+iIrK0s6HhERgd69e0v3w8nJCYsWLcLdu3dr5T0xMREymQzbtm2rdSwnJweLFy/GkCFDYGNjAzc3NwQFBaGkpEQ6RwgBZ2dnuLu71wovhMCOHTvg6uoKS0tLODo64p///CfKy8vrusVERERERETNjpOHREREREREz0lISAiSk5Nx8uRJfPLJJ9i+fTsCAgIAAH5+fujUqRNOnjyJxMRErFu3Du3bt683LltbWyQnJyM5ORmxsbEAgJ9//lna99prr9UK07FjRyQnJ+PixYvw8/PDF198gVu3bgEA5syZg/j4eGzbtg0XL17EunXrsH//fqxevVqtayspKcHRo0dhYGCAmJgYlWNHjhyBj48PPD09cebMGZw/fx5z5syRJgcLCgrwwQcfQEdHB7GxsUhISMDUqVPh6+uLI0eOqMRlaGiIM2fOSNtnzpyBoaFhrfR8fX3x97//HQkJCYiNjYWOjg4++OADibhPDQAACM1JREFUFBYWSudZW1sjOTkZSUlJ2LVrF3R1deHl5YXU1FSV+KKiomBsbIzo6GiV/QUFBfD29kZZWRnCwsKQnJyMnTt34uHDh7h375503s8//4y8vDykp6cjJSVFJY5Vq1Zh//79WLt2LS5evIjt27cjPj4ePj4+at13IiIiIiKipsbJQyIiIiIioufMwMAAzs7O2LhxIyIjI5GamoorV67Ay8sLbdq0gba2Nvr06YNhw4Y1S/oaGhpwcXGBoaEhbt26hfj4eJw7dw7BwcGwsLCAtrY2rK2tERgYiD179iAtLe2JcR47dgyGhoaYNWsWoqKipP1CCHz99deYNWsWJkyYAAMDA2hqasLOzg6rVq0CAOzatQtt2rTB6tWrYWpqCj09PYwePRozZszA2rVrVVY/yuVylfijo6Ph6empkt7atWsxc+ZMeHh4QE9PD6ampli9ejXatGmDXbt21cq7lpYW3nzzTSxfvhx2dnbYvHmzdKykpARHjhzB0qVLkZaWhl9//VU6tnPnTrRt2xaBgYF44403AADm5uZYsmQJevXqJZ0XGRkJJycnDBs2TCXv//3vfxEaGor169fDxsYG2tra6NmzJ4KDg3H27FnEx8c/8b4TERERERE1NU4eEhERERERtRBLS0t06tQJSUlJsLKywooVKxAXF4fMzMxmTVepVOLHH39EUVERLCwscO7cOVhZWcHc3FzlPCsrK3Tq1EmtSazIyEiMGjUKo0aNwp07d3DlyhUAwJ07d3D//n2MGDGi3rDnz5+Hq6srNDVVu6gjR45EZmamyqNEXVxckJSUhIcPH6KwsBBJSUlwdnaWjt+5cweZmZlwc3NTiUtTUxOurq44f/58g9fxt7/9DUlJSdL2sWPH0LZtW7i5uWHIkCEqk3/x8fH429/+VivfNT169AhHjx7FmDFj4OHhgbi4OOmRpPHx8ejUqRMsLS1Vwpibm8Pa2vqJeSUiIiIiImoOnDwkIiIiIiJqQR07dkRhYSE2bdoEW1tbbNmyBc7OzpDL5bUecdlYOTk5sLW1xcCBA7F582asW7cO3bt3R35+PkxNTesMY2pqivz8/AbjzczMRGJiIjw8PNChQwc4ODhIk2wFBQXSddanvvSrw9RMX1dXF8OHD8fhw4dx+PBhODk5QVdXVyWu+tJT51qqP49qUVFRGDlyJLS0tDB69GjExcWhoqJCurb67lu1Y8eOQUdHB4MHD4ajoyMqKytx+vTpBq9b3bwSERERERE1B04eEhERERERtaDs7GwYGRnByMgI8+fPR1xcHM6dO4fevXvj008/VXlkZ2N17NgRSUlJuHDhAqKjozFq1CgAgImJCXJzc+sMk5ubCxMTkwbjjY6ORo8ePdC7d28AgIeHB2JjY1FRUQFjY2MAVROX9akv/eowj6fv6emJqKioWo8srXluXempcy3VnwcA3L9/X5oUBQBnZ2eUlZVJk3/Gxsb13rdq1ZOP2tra0NXVhaurKyIjIxu8bnXzSkRERERE1Bw4eUhERERERNRCUlJSkJ2djf79+6vsb9euHaZNm4acnBxp5V5zGjRoEC5fvoz79++r7K/eN3DgwAbDR0VFIT09HYMHD8bgwYPx1VdfIT8/H6dPn0b37t1hbm6OY8eO1RvewcEBP/74I5RKpcr+H374Aebm5ujWrZvKfltbW+Tm5uLBgwe17l337t3RqVMnHDlyRGW/UqnEsWPHnngtP/30E2xtbQFUTYoqlUrMnDkTgwcPhouLC8rLy6XJv/ryXS0rKwsJCQmIiYmR7s3Ro0dx5swZ5OXlYeDAgbh//36tFab379/HpUuX4ODg0GBeiYiIiIiImgMnD4mIiIiIiJ6z4uJinDx5Ep9//jnGjBkDmUyGwMBApKamorKyEsXFxdi7dy+6dOnyXFafDRo0CA4ODpg9ezZu3rwJhUKBS5cuwc/PD++//z66du1ab9jk5GSkp6fjwIEDiIqKQlRUFGJjYzF69GhER0dDQ0MD/v7+2LJlC8LDw1FcXAylUomkpCR88cUXAICpU6eiqKgIAQEByM3NRVlZGWJjYxESEoIFCxZAQ0NDJU0NDQ2EhIRg69atdR5buHAhtm7dikOHDqGsrAy5ubkICAhAcXExpk6dWusaFAoF0tPTsXLlSly4cAGffvopgKr3OH722WfSdUVFRSEoKAinT59Gfn4+PvroI/z5559YuHAhfv/9dwBVKxe/+uorXL9+HdHR0ejatSuOHDkihT969CjMzMwQFxeHbt26wdvbG/Pnz8elS5egUChw8+ZNzJ49G4MGDcKgQYMa8akSERERERE9G+2WzgAREREREdH/ihkzZkBLSwuampp466238NFHH8Hb2xsAUFpais8++wy5ubnQ1dWFlZUVtm7d+tzyFhwcjKCgIHz88cfIz8+HmZkZJkyYgI8//rjBcJGRkXB2doZMJlPZ//e//x0ffPABCgoK4ObmhjZt2iAkJASrVq2Crq4uevbsienTpwOoenxnaGgo1q9fj1GjRqG8vBw9evTAunXr4OLiUme6PXv2rDdP7u7u0NHRwdatW/HFF19AR0cHQ4YMwd69e1UmYy9dugQbGxsIIWBiYgI7OzscPHgQPXr0wKVLl5CZmYlJkyahXbt2UhhnZ2d06dIFcXFxmDx5Mvbu3YuNGzdi4sSJKCkpgZmZGUaPHo0uXbogMjISkyZNqvVeQ29vb0RGRmLKlClYunQpduzYAT8/P2RnZ8PExASjRo2Cj49Pg/ediIiIiIiouWiIpnyBBhERERERERERERERERG9tPjYUiIiIiIiIiIiIiIiIiICwMeWEhERERERvdBiYmKwbNmyWvtfe+01xMXFPbd82NjY1Ll/+/btsLW1fW75ICIiIiIioubFx5YSEREREREREREREREREQA+tpSIiIiIiIiIiIiIiIiI/sLJQyIiIiIiIiIiIiIiIiICwMlDIiIiIiIiIiIiIiIiIvoLJw+JiIiIiIiIiIiIiIiICADw/wCvo+JPgxGerQAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"markdown","source":["#### DS_CARATER_ATENDIMENTO"],"metadata":{"id":"mcQe_qwDksjD"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648761479658,"user_tz":180,"elapsed":647,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"bfc1ff52-f729-4b73-a614-26f0b7a39346","id":"1a_Cyk1EksjD"},"source":["# df.'DS_TIPO_ATENDIMENTO', .value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_TIPO_ATENDIMENTO)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9f07e5d0>"]},"metadata":{},"execution_count":26},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["#### DS_TIPO_ATENDIMENTO"],"metadata":{"id":"fVdDyJetkz-L"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648761480269,"user_tz":180,"elapsed":635,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"31d955a0-80a3-47b6-ed8a-dc24a94f7471","id":"H7f-3-Ijkz-M"},"source":["# df.DS_TIPO_ATENDIMENTO.value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_TIPO_ATENDIMENTO)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9f129790>"]},"metadata":{},"execution_count":27},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["#### CD_CID"],"metadata":{"id":"rN6PGfhak34P"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648761480722,"user_tz":180,"elapsed":470,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"8d42b711-dd62-423a-b96f-4345c67dbbca","id":"jTwBuXJRk34Q"},"source":["df.CD_CID.value_counts()\n","# sns.set(rc = {'figure.figsize':(31,8)})\n","# sns.countplot(df.CD_CID)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Z00     15377\n","H40     10580\n","B342     9484\n","Z10      9357\n","C50      6375\n","        ...  \n","Z039        1\n","I714        1\n","D232        1\n","S221        1\n","D61         1\n","Name: CD_CID, Length: 1902, dtype: int64"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","source":["#### DS_INDICACAO_CLINICA"],"metadata":{"id":"UjQKhmLkldWn"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648761480723,"user_tz":180,"elapsed":28,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"9fa95911-5215-48e9-d2af-2e152efff67b","id":"-uR2GBrkldWo"},"source":["# Verificação das características das categorias 'type'\n","df.DS_INDICACAO_CLINICA.value_counts()\n","# sns.set(rc = {'figure.figsize':(31,8)})\n","# sns.countplot(df.DS_INDICACAO_CLINICA)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["EM ANEXO                                                7856\n","Em anexo                                                2089\n","GLAUCOMA                                                1702\n","em anexo                                                1635\n","CA DE MAMA                                              1273\n","                                                        ... \n","Isolamento por Covid                                       1\n","LESAO MANGUITO ROTADOR + discopatia lombar                 1\n","DOR LOCAL INTENSA COM LIMITACAO DE MOVIMENTOS              1\n","TETRAPARESTESIA DE INICIO RECENTE                          1\n","RM OMBRO DIREITO E ESQUERDO , TENDINOPATIA  OMBROS.        1\n","Name: DS_INDICACAO_CLINICA, Length: 76280, dtype: int64"]},"metadata":{},"execution_count":29}]},{"cell_type":"markdown","source":["#### DS_TIPO_ITEM"],"metadata":{"id":"50BOqN6ql0ON"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"FjqUGthRl0OP","executionInfo":{"status":"ok","timestamp":1648761481173,"user_tz":180,"elapsed":468,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"c2da5e85-249e-43f7-e1df-c24fd35f323d"},"source":["# Verificação das características das categorias 'type'\n","# df.DS_TIPO_ITEM.value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_TIPO_ITEM)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9f177050>"]},"metadata":{},"execution_count":30},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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sns.set(rc = {'figure.figsize':(31,8)})\n","# sns.countplot(df.DS_ITEM)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FhJn3S66l4a5","executionInfo":{"status":"ok","timestamp":1648761481175,"user_tz":180,"elapsed":25,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"c3f51043-2b5a-455f-b3e5-cacc902e2bc5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["ENFERMARIA                                                                                                     8170\n","PAQUIMETRIA ULTRASSONICA - MONOCULAR                                                                           7554\n","PORTE 3 (CIRURGIA MEDIA)                                                                                       7451\n","MAPEAMENTO DE RETINA (OFTALMOSCOPIA INDIRETA) - MONOCULAR                                                      6845\n","USG - ULTRASSONOGRAFIA - ABDOME TOTAL (ABDOME SUPERIOR, RINS, BEXIGA, AORTA, VEIA CAVA INFERIOR E ADRENAIS)    6839\n","                                                                                                               ... \n","FRESA ESFERICA CORTANTE 2,0X080MM - UN                                                                            1\n","STENT EPIC VASCULAR NITINOL OTW 06X040X120CM - UN                                                                 1\n","EQUIPO P/BOMBA IRRIGACAO AR 2VIAS - UN                                                                            1\n","DRENAGEM CAVITARIA POR LAPAROSCOPIA                                                                               1\n","OMCILOM A ORABASE 1,0MG 10G - GRS                                                                                 1\n","Name: DS_ITEM, Length: 7685, dtype: int64"]},"metadata":{},"execution_count":31}]},{"cell_type":"markdown","source":["####DS_CLASSE"],"metadata":{"id":"0hq6T7rbl8_x"}},{"cell_type":"code","source":["df.DS_CLASSE.value_counts()\n","# sns.set(rc = {'figure.figsize':(31,8)})\n","# sns.countplot(df.DS_CLASSE)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"P0o6yllEl8_y","executionInfo":{"status":"ok","timestamp":1648761481176,"user_tz":180,"elapsed":23,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"c6207b08-7dc9-487e-b04f-d6537b5ebd53"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["PROCEDIMENTOS                                                                                 43439\n","ULTRA-SONOGRAFIA DIAGNOSTICA                                                                  36399\n","BIOQUIMICA                                                                                    27423\n","Material de consumo                                                                           27094\n","EM CENTRO CIRURGICO                                                                           21605\n","                                                                                              ...  \n","MALHA CIRURGICA IMPLANTAVEL                                                                       1\n","SISTEMA POSTERIOR DE COLUNA PARA FIXAC?O EM LAMINA, PEDICULO, APOFISE OU MACICO ARTICULAR.        1\n","CABECA METALICA PARA ARTROPLASTIA DE QUADRIL                                                      1\n","COMPRESSA ABSORVENTE PARA NEUROCIRURGIA                                                           1\n","CORTICOSTEROIDES                                                                                  1\n","Name: DS_CLASSE, Length: 501, dtype: int64"]},"metadata":{},"execution_count":32}]},{"cell_type":"markdown","source":["####DS_SUBGRUPO"],"metadata":{"id":"WEQasMLYl9LP"}},{"cell_type":"code","source":["df.DS_SUBGRUPO.value_counts()\n","# sns.set(rc = {'figure.figsize':(74,8)})\n","# sns.countplot(df.DS_SUBGRUPO)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tezGDUPVl9LQ","executionInfo":{"status":"ok","timestamp":1648761481178,"user_tz":180,"elapsed":22,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"93180807-67e4-4261-bec7-f9d9472dfed1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["MEDICINA LABORATORIAL                                                                      62140\n","Material Hospitalar                                                                        51344\n","ULTRA-SONOGRAFIA                                                                           36518\n","EXAMES ESPECIFICOS                                                                         33907\n","TAXA DE SALA                                                                               30245\n","                                                                                           ...  \n","SISTEMA CARDIOVASCULAR+SISTEMA RESPIRATORIO                                                    3\n","PRODUTOS DERMATOLOGICOS E MEMBRANAS MUCOSAS+SISTEMA GENITO URINARIO E HORMONIOS SEXUAIS        2\n","SISTEMA DIGESTIVO, METABOLISMO E NUTRICAO+SISTEMA GENITO URINARIO E HORMONIOS SEXUAIS          2\n","SISTEMA DIGESTIVO+METABOLISMO E NUTRICAO                                                       1\n","SANGUE E SISTEMA HEMATOPOETICO+SISTEMA NERVOSO                                                 1\n","Name: DS_SUBGRUPO, Length: 74, dtype: int64"]},"metadata":{},"execution_count":33}]},{"cell_type":"markdown","source":["####DS_GRUPO"],"metadata":{"id":"3CC4Firul9U4"}},{"cell_type":"code","source":["# df.DS_GRUPO.value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_GRUPO)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":435},"id":"ft_o1pWnl9U4","executionInfo":{"status":"ok","timestamp":1648761481833,"user_tz":180,"elapsed":675,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"6ea3ddac-3fd5-470e-e8c8-f27811ddfb13"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9f0d3290>"]},"metadata":{},"execution_count":34},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["####DS_STATUS_ITEM"],"metadata":{"id":"F9i9mzC_l9f4"}},{"cell_type":"code","source":["# df.DS_STATUS_ITEM.value_counts()\n","sns.set(rc = {'figure.figsize':(31,8)})\n","sns.countplot(df.DS_STATUS_ITEM)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":435},"id":"-2VCNsNVl9f6","executionInfo":{"status":"ok","timestamp":1648761482476,"user_tz":180,"elapsed":656,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"6aa16065-3845-4f61-ee47-c31e3bc1843c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9e99ee90>"]},"metadata":{},"execution_count":35},{"output_type":"display_data","data":{"text/plain":["<Figure size 2232x576 with 1 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Análise de correlação\n","**Qual é a correlação entre cada atributo?** "],"metadata":{"id":"Ne06F8qwSQfq"}},{"cell_type":"code","source":["correlacao = df.corr()\n","correlacao"],"metadata":{"id":"lihnU5TMjyw8","colab":{"base_uri":"https://localhost:8080/","height":438},"executionInfo":{"status":"ok","timestamp":1648763749030,"user_tz":180,"elapsed":721,"user":{"displayName":"André Ribeiro Miranda","userId":"04420503819125685723"}},"outputId":"189d6254-54ff-4693-bd5e-f1c87c903ca9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                    NR_SEQ_REQUISICAO  NR_SEQ_ITEM  DT_REQUISICAO  \\\n","NR_SEQ_REQUISICAO            1.000000     0.644921       0.999843   \n","NR_SEQ_ITEM                  0.644921     1.000000       0.644845   \n","DT_REQUISICAO                0.999843     0.644845       1.000000   \n","DT_NASCIMENTO               -0.049581     0.026050      -0.049534   \n","NR_PRODUTO                        NaN          NaN            NaN   \n","QT_TEMPO_DOENCA              0.333073     0.202291       0.338338   \n","DS_TIPO_SAIDA                     NaN          NaN            NaN   \n","QT_DIA_SOLICITADO           -0.020638     0.013269      -0.019249   \n","CD_GUIA_REFERENCIA           0.981034     0.508212       0.980812   \n","CD_ITEM                      0.016366    -0.114341       0.016398   \n","QT_SOLICITADA                0.000297    -0.001349       0.000321   \n","\n","                    DT_NASCIMENTO  NR_PRODUTO  QT_TEMPO_DOENCA  DS_TIPO_SAIDA  \\\n","NR_SEQ_REQUISICAO       -0.049581         NaN         0.333073            NaN   \n","NR_SEQ_ITEM              0.026050         NaN         0.202291            NaN   \n","DT_REQUISICAO           -0.049534         NaN         0.338338            NaN   \n","DT_NASCIMENTO            1.000000         NaN        -0.176836            NaN   \n","NR_PRODUTO                    NaN         NaN              NaN            NaN   \n","QT_TEMPO_DOENCA         -0.176836         NaN         1.000000            NaN   \n","DS_TIPO_SAIDA                 NaN         NaN              NaN            NaN   \n","QT_DIA_SOLICITADO       -0.104517         NaN         0.814799            NaN   \n","CD_GUIA_REFERENCIA      -0.052870         NaN        -0.542600            NaN   \n","CD_ITEM                 -0.031217         NaN         0.059790            NaN   \n","QT_SOLICITADA            0.004499         NaN         0.055368            NaN   \n","\n","                    QT_DIA_SOLICITADO  CD_GUIA_REFERENCIA   CD_ITEM  \\\n","NR_SEQ_REQUISICAO           -0.020638            0.981034  0.016366   \n","NR_SEQ_ITEM                  0.013269            0.508212 -0.114341   \n","DT_REQUISICAO               -0.019249            0.980812  0.016398   \n","DT_NASCIMENTO               -0.104517           -0.052870 -0.031217   \n","NR_PRODUTO                        NaN                 NaN       NaN   \n","QT_TEMPO_DOENCA              0.814799           -0.542600  0.059790   \n","DS_TIPO_SAIDA                     NaN                 NaN       NaN   \n","QT_DIA_SOLICITADO            1.000000           -0.092297  0.012142   \n","CD_GUIA_REFERENCIA          -0.092297            1.000000 -0.017494   \n","CD_ITEM                      0.012142           -0.017494  1.000000   \n","QT_SOLICITADA                0.003571           -0.002502  0.004045   \n","\n","                    QT_SOLICITADA  \n","NR_SEQ_REQUISICAO        0.000297  \n","NR_SEQ_ITEM             -0.001349  \n","DT_REQUISICAO            0.000321  \n","DT_NASCIMENTO            0.004499  \n","NR_PRODUTO                    NaN  \n","QT_TEMPO_DOENCA          0.055368  \n","DS_TIPO_SAIDA                 NaN  \n","QT_DIA_SOLICITADO        0.003571  \n","CD_GUIA_REFERENCIA      -0.002502  \n","CD_ITEM                  0.004045  \n","QT_SOLICITADA            1.000000  "],"text/html":["\n","  <div id=\"df-5b665942-1912-467f-a588-b86f74aaa6be\">\n","    <div class=\"colab-df-container\">\n","      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<td>NaN</td>\n","      <td>0.333073</td>\n","      <td>NaN</td>\n","      <td>-0.020638</td>\n","      <td>0.981034</td>\n","      <td>0.016366</td>\n","      <td>0.000297</td>\n","    </tr>\n","    <tr>\n","      <th>NR_SEQ_ITEM</th>\n","      <td>0.644921</td>\n","      <td>1.000000</td>\n","      <td>0.644845</td>\n","      <td>0.026050</td>\n","      <td>NaN</td>\n","      <td>0.202291</td>\n","      <td>NaN</td>\n","      <td>0.013269</td>\n","      <td>0.508212</td>\n","      <td>-0.114341</td>\n","      <td>-0.001349</td>\n","    </tr>\n","    <tr>\n","      <th>DT_REQUISICAO</th>\n","      <td>0.999843</td>\n","      <td>0.644845</td>\n","      <td>1.000000</td>\n","      <td>-0.049534</td>\n","      <td>NaN</td>\n","      <td>0.338338</td>\n","      <td>NaN</td>\n","      <td>-0.019249</td>\n","      <td>0.980812</td>\n","      <td>0.016398</td>\n","      <td>0.000321</td>\n","    </tr>\n","    <tr>\n","      <th>DT_NASCIMENTO</th>\n","      <td>-0.049581</td>\n","      <td>0.026050</td>\n","      <td>-0.049534</td>\n","      <td>1.000000</td>\n","      <td>NaN</td>\n","      <td>-0.176836</td>\n","      <td>NaN</td>\n","      <td>-0.104517</td>\n","      <td>-0.052870</td>\n","      <td>-0.031217</td>\n","      <td>0.004499</td>\n","    </tr>\n","    <tr>\n","      <th>NR_PRODUTO</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>QT_TEMPO_DOENCA</th>\n","      <td>0.333073</td>\n","      <td>0.202291</td>\n","      <td>0.338338</td>\n","      <td>-0.176836</td>\n","      <td>NaN</td>\n","      <td>1.000000</td>\n","      <td>NaN</td>\n","      <td>0.814799</td>\n","      <td>-0.542600</td>\n","      <td>0.059790</td>\n","      <td>0.055368</td>\n","    </tr>\n","    <tr>\n","      <th>DS_TIPO_SAIDA</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>QT_DIA_SOLICITADO</th>\n","      <td>-0.020638</td>\n","      <td>0.013269</td>\n","      <td>-0.019249</td>\n","      <td>-0.104517</td>\n","      <td>NaN</td>\n","      <td>0.814799</td>\n","      <td>NaN</td>\n","      <td>1.000000</td>\n","      <td>-0.092297</td>\n","      <td>0.012142</td>\n","      <td>0.003571</td>\n","    </tr>\n","    <tr>\n","      <th>CD_GUIA_REFERENCIA</th>\n","      <td>0.981034</td>\n","      <td>0.508212</td>\n","      <td>0.980812</td>\n","      <td>-0.052870</td>\n","      <td>NaN</td>\n","      <td>-0.542600</td>\n","      <td>NaN</td>\n","      <td>-0.092297</td>\n","      <td>1.000000</td>\n","      <td>-0.017494</td>\n","      <td>-0.002502</td>\n","    </tr>\n","    <tr>\n","      <th>CD_ITEM</th>\n","      <td>0.016366</td>\n","      <td>-0.114341</td>\n","      <td>0.016398</td>\n","      <td>-0.031217</td>\n","      <td>NaN</td>\n","      <td>0.059790</td>\n","      <td>NaN</td>\n","      <td>0.012142</td>\n","      <td>-0.017494</td>\n","      <td>1.000000</td>\n","      <td>0.004045</td>\n","    </tr>\n","    <tr>\n","      <th>QT_SOLICITADA</th>\n","      <td>0.000297</td>\n","      <td>-0.001349</td>\n","      <td>0.000321</td>\n","      <td>0.004499</td>\n","      <td>NaN</td>\n","      <td>0.055368</td>\n","      <td>NaN</td>\n","      <td>0.003571</td>\n","      <td>-0.002502</td>\n","      <td>0.004045</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5b665942-1912-467f-a588-b86f74aaa6be')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","             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n=10))\n","plt.show()"],"metadata":{"id":"1gb_QDXur5Xm","colab":{"base_uri":"https://localhost:8080/","height":603},"executionInfo":{"status":"ok","timestamp":1648761483353,"user_tz":180,"elapsed":910,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"d2deb241-93e7-4c24-d5a7-d607ee7c4a0a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 936x576 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Verificação de balanceamento de DS_STATUS_ITEM\n"," "],"metadata":{"id":"UmhgF9FtSxhD"}},{"cell_type":"code","source":["sns.countplot(x=\"Status antes remover\", data=df)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":280},"id":"nGZhNFT3xgXl","executionInfo":{"status":"ok","timestamp":1649783742106,"user_tz":180,"elapsed":1031,"user":{"displayName":"JEFFERSON CARVALHO SILVA","userId":"18358345737462067048"}},"outputId":"7d8d8551-ce5b-4c56-b16e-ad0eddad65a5"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# Substituindo \"Parcialmente autorizado\" por \"Autorizado\" em \"DF_STATUS_ITEM\"\n","df[\"DS_STATUS_ITEM\"].replace(['Parcialmente autorizado'], 'Autorizado', inplace = True)"],"metadata":{"id":"I9avEgJ5xc4B"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sns.countplot(x=\"DS_STATUS_ITEM\", data=df)\n","plt.show()"],"metadata":{"id":"HPbbR4FYS-SZ","colab":{"base_uri":"https://localhost:8080/","height":280},"executionInfo":{"status":"ok","timestamp":1649783792299,"user_tz":180,"elapsed":610,"user":{"displayName":"JEFFERSON CARVALHO SILVA","userId":"18358345737462067048"}},"outputId":"12a4ea3b-55e6-4d2c-eb31-2a1e97d74bbb"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["df[\"DS_STATUS_ITEM\"].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cnJv0aNRvLzO","executionInfo":{"status":"ok","timestamp":1649783772401,"user_tz":180,"elapsed":413,"user":{"displayName":"JEFFERSON CARVALHO SILVA","userId":"18358345737462067048"}},"outputId":"889e6fa8-bec0-4dc7-d85b-1000b16ec1af"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['Autorizado', 'Negado'], dtype=object)"]},"metadata":{},"execution_count":73}]},{"cell_type":"markdown","source":["# Criação do Modelo"],"metadata":{"id":"h5fmwBtMod2-"}},{"cell_type":"markdown","source":["Esta seção engloba:\n","* Aplicação de diferentes algortimos de claficação;\n","* Teste de escalonamento e normalização de dados;\n","* Verificaão de diferentes encoders;\n","\n","Além disso, engloba também a construção do modelo definitivo e geração do arquivo para submissão no Kaggle.\n"],"metadata":{"id":"CjnIIYtPomAN"}},{"cell_type":"markdown","metadata":{"id":"HpV07vGyLbtf"},"source":["## Pré-processamento"]},{"cell_type":"code","source":["import pandas as pd \n","import numpy as np\n"],"metadata":{"id":"iMSG134WpHzi"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BTHGuqSSK7Lv"},"outputs":[],"source":["def gera_porcentagem_faltantes(df):\n","  for coluna in df.columns:\n","    qtd_dados_faltantes = df[coluna].isna().sum()                                                             \n","    porcentagem = qtd_dados_faltantes*100/len(df)\n","    print(\"{}: {}\".format(coluna, round(porcentagem, 2)))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pOXII9bZEniK"},"outputs":[],"source":["df = pd.read_csv('/content/drive/MyDrive/Trabalho Mineração de Dados [UFG]/0. base de dados/classificacao_binaria_train.csv', sep=';')\n","df[\"DS_STATUS_ITEM\"].replace(['Parcialmente autorizado'], 'Autorizado', inplace = True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Zfhv7iRpFEeh"},"outputs":[],"source":["# Atributos definidos com base na análise exploratoria dos dados\n","\n","df = df[['DT_NASCIMENTO', 'DS_TIPO_PREST_SOLICITANTE', 'DS_CARATER_ATENDIMENTO', 'CD_CID', 'DS_SUBGRUPO', 'DS_CLASSE', 'DS_TIPO_GUIA', 'DS_GRUPO', 'DS_INDICACAO_ACIDENTE', \n","         'DS_CBO','NR_SEQ_REQUISICAO', 'DS_STATUS_ITEM']]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"m7_k5uzJFFyX"},"outputs":[],"source":["df = df.dropna()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1650064491387,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"CIZ-62s0FMZB","outputId":"07c0e60f-5cc0-4ec0-cb1a-0289ec48d0c4"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(177495, 12)"]},"metadata":{},"execution_count":189}],"source":["df.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1650064491388,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"SuUifMoNG4UU","outputId":"07eb13b1-70cb-468f-a514-f289614699b5"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Autorizado    126398\n","Negado         51097\n","Name: DS_STATUS_ITEM, dtype: int64"]},"metadata":{},"execution_count":190}],"source":["df['DS_STATUS_ITEM'].value_counts()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"du7DhVA5G6ST"},"outputs":[],"source":["from imblearn.under_sampling import RandomUnderSampler\n","rus = RandomUnderSampler(random_state=0)\n","X_resampled, y_resampled = rus.fit_resample(df.drop(columns=['DS_STATUS_ITEM']), df['DS_STATUS_ITEM'])\n","df = pd.concat([X_resampled, y_resampled], axis='columns')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":48,"status":"ok","timestamp":1650064492586,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"28Nk7go4G7f7","outputId":"74211b21-7299-4a9e-b0af-4943e3db623d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Autorizado    51097\n","Negado        51097\n","Name: DS_STATUS_ITEM, dtype: int64"]},"metadata":{},"execution_count":192}],"source":["df['DS_STATUS_ITEM'].value_counts()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"x_MgBpRWG-OZ"},"outputs":[],"source":["dict_status = {'Autorizado':0, 'Negado':1}\n","df['DS_STATUS_ITEM'] = df['DS_STATUS_ITEM'].map(dict_status)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":44,"status":"ok","timestamp":1650064492589,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"0ABoVb2CHs4P","outputId":"5796dc35-4db1-421b-81d8-d7b2a54ad438"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DT_NASCIMENTO                float64\n","DS_TIPO_PREST_SOLICITANTE     object\n","DS_CARATER_ATENDIMENTO        object\n","CD_CID                        object\n","DS_SUBGRUPO                   object\n","DS_CLASSE                     object\n","DS_TIPO_GUIA                  object\n","DS_GRUPO                      object\n","DS_INDICACAO_ACIDENTE         object\n","DS_CBO                        object\n","NR_SEQ_REQUISICAO              int64\n","DS_STATUS_ITEM                 int64\n","dtype: object"]},"metadata":{},"execution_count":194}],"source":["df.dtypes"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kk7PwWQeHy2S"},"outputs":[],"source":["df['DT_NASCIMENTO'] = df['DT_NASCIMENTO'].apply(\"int64\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":39,"status":"ok","timestamp":1650064492591,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"uwtFjwerK3WV","outputId":"28bfce5a-d274-4fe8-92c7-d8f3baa3f7f1"},"outputs":[{"output_type":"stream","name":"stdout","text":["#### PORCENTAGEM DE DADOS FALTANTES\n","\n","DT_NASCIMENTO: 0.0\n","DS_TIPO_PREST_SOLICITANTE: 0.0\n","DS_CARATER_ATENDIMENTO: 0.0\n","CD_CID: 0.0\n","DS_SUBGRUPO: 0.0\n","DS_CLASSE: 0.0\n","DS_TIPO_GUIA: 0.0\n","DS_GRUPO: 0.0\n","DS_INDICACAO_ACIDENTE: 0.0\n","DS_CBO: 0.0\n","NR_SEQ_REQUISICAO: 0.0\n","DS_STATUS_ITEM: 0.0\n"]}],"source":["#Verificação de valores missing  \n","print('#### PORCENTAGEM DE DADOS FALTANTES\\n')\n","\n","gera_porcentagem_faltantes(df)"]},{"cell_type":"markdown","metadata":{"id":"zT9jp3sALYQP"},"source":["## Encodings \n","Executar um metódo por vez."]},{"cell_type":"markdown","metadata":{"id":"3KVPTprIburm"},"source":["###Target Enconding"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":25,"status":"ok","timestamp":1650064494227,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"Yc9UANwbcGbr","outputId":"b5ee3a57-dc60-458c-8dc6-9d65ee7fe5b0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   DT_NASCIMENTO DS_TIPO_PREST_SOLICITANTE DS_CARATER_ATENDIMENTO CD_CID  \\\n","0        2429082                  HOSPITAL    Urgencia/Emergencia   I509   \n","\n","  DS_SUBGRUPO DS_CLASSE                                      DS_TIPO_GUIA  \\\n","0     DIARIAS   DIARIAS  Guia de solicitac?o de prorrogac?o de internac?o   \n","\n","  DS_GRUPO DS_INDICACAO_ACIDENTE          DS_CBO  NR_SEQ_REQUISICAO  \\\n","0  DIARIAS          N?o acidente  Medico clinico            1458911   \n","\n","   DS_STATUS_ITEM  \n","0               0  "],"text/html":["\n","  <div id=\"df-80777983-7c70-45e5-b7f2-f8ef4cb40f0e\">\n","    <div class=\"colab-df-container\">\n","      <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>DT_NASCIMENTO</th>\n","      <th>DS_TIPO_PREST_SOLICITANTE</th>\n","      <th>DS_CARATER_ATENDIMENTO</th>\n","      <th>CD_CID</th>\n","      <th>DS_SUBGRUPO</th>\n","      <th>DS_CLASSE</th>\n","      <th>DS_TIPO_GUIA</th>\n","      <th>DS_GRUPO</th>\n","      <th>DS_INDICACAO_ACIDENTE</th>\n","      <th>DS_CBO</th>\n","      <th>NR_SEQ_REQUISICAO</th>\n","      <th>DS_STATUS_ITEM</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2429082</td>\n","      <td>HOSPITAL</td>\n","      <td>Urgencia/Emergencia</td>\n","      <td>I509</td>\n","      <td>DIARIAS</td>\n","      <td>DIARIAS</td>\n","      <td>Guia de solicitac?o de prorrogac?o de internac?o</td>\n","      <td>DIARIAS</td>\n","      <td>N?o acidente</td>\n","      <td>Medico clinico</td>\n","      <td>1458911</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-80777983-7c70-45e5-b7f2-f8ef4cb40f0e')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":197}],"source":["df.head(1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Udm1nXqNb8Jy"},"outputs":[],"source":["# https://towardsdatascience.com/all-about-categorical-variable-encoding-305f3361fd02\n","\n","mean_encode = df.groupby('DS_TIPO_PREST_SOLICITANTE')['DS_STATUS_ITEM'].mean()\n","df.loc[:, 'DS_TIPO_PREST_SOLICITANTE'] = df['DS_TIPO_PREST_SOLICITANTE'].map(mean_encode)\n","\n","mean_encode = df.groupby('DT_NASCIMENTO')['DS_STATUS_ITEM'].mean()\n","df.loc[:, 'DT_NASCIMENTO'] = df['DT_NASCIMENTO'].map(mean_encode)\n","\n","mean_encode = df.groupby('DS_CARATER_ATENDIMENTO')['DS_STATUS_ITEM'].mean()\n","df.loc[:, 'DS_CARATER_ATENDIMENTO'] = df['DS_CARATER_ATENDIMENTO'].map(mean_encode)\n","\n","mean_encode = df.groupby('CD_CID')['DS_STATUS_ITEM'].mean()\n","df.loc[:, 'CD_CID'] = df['CD_CID'].map(mean_encode)\n","\n","mean_encode = df.groupby('DS_SUBGRUPO')['DS_STATUS_ITEM'].mean()\n","df.loc[:, 'DS_SUBGRUPO'] = df['DS_SUBGRUPO'].map(mean_encode)\n","\n","mean_encode = df.groupby('DS_CLASSE')['DS_STATUS_ITEM'].mean()\n","df.loc[:, 'DS_CLASSE'] = df['DS_CLASSE'].map(mean_encode)\n","\n","mean_encode = df.groupby('DS_TIPO_GUIA')['DS_STATUS_ITEM'].mean()\n","df.loc[:, 'DS_TIPO_GUIA'] = df['DS_TIPO_GUIA'].map(mean_encode)\n","\n","mean_encode = df.groupby('DS_GRUPO')['DS_STATUS_ITEM'].mean()\n","df.loc[:, 'DS_GRUPO'] = df['DS_GRUPO'].map(mean_encode)\n","\n","mean_encode = df.groupby('DS_INDICACAO_ACIDENTE')['DS_STATUS_ITEM'].mean()\n","df.loc[:, 'DS_INDICACAO_ACIDENTE'] = df['DS_INDICACAO_ACIDENTE'].map(mean_encode)\n","\n","mean_encode = df.groupby('DS_CBO')['DS_STATUS_ITEM'].mean()\n","df.loc[:, 'DS_CBO'] = df['DS_CBO'].map(mean_encode)\n","\n","mean_encode = df.groupby('NR_SEQ_REQUISICAO')['DS_STATUS_ITEM'].mean()\n","df.loc[:, 'NR_SEQ_REQUISICAO'] = df['NR_SEQ_REQUISICAO'].map(mean_encode)\n","\n","# mean_encode = df.groupby('DT_REQUISICAO')['DS_STATUS_ITEM'].mean()\n","# df.loc[:, 'DT_REQUISICAO'] = df['DT_REQUISICAO'].map(mean_encode)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1650064494694,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"4SOQXqbbfFDG","outputId":"b61b50c9-4a19-4fb3-89ac-57ccf5eb5969"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["        DT_NASCIMENTO  DS_TIPO_PREST_SOLICITANTE  DS_CARATER_ATENDIMENTO  \\\n","0            0.636364                   0.609573                0.623313   \n","1            0.000000                   0.490390                0.462760   \n","2            0.250000                   0.609573                0.623313   \n","3            0.500000                   0.609573                0.462760   \n","4            0.595506                   0.609573                0.462760   \n","...               ...                        ...                     ...   \n","102189       0.800000                   0.433879                0.462760   \n","102190       0.600000                   0.490390                0.462760   \n","102191       0.666667                   0.433879                0.462760   \n","102192       1.000000                   0.609573                0.623313   \n","102193       0.410959                   0.433879                0.462760   \n","\n","          CD_CID  DS_SUBGRUPO  DS_CLASSE  DS_TIPO_GUIA  DS_GRUPO  \\\n","0       0.384615     0.528302   0.528302      0.672809  0.528302   \n","1       0.428571     0.531234   0.374195      0.462082  0.513471   \n","2       0.642869     0.531234   0.544776      0.462082  0.513471   \n","3       0.333333     0.528302   0.528302      0.564332  0.528302   \n","4       0.307960     0.387223   0.281682      0.462082  0.433423   \n","...          ...          ...        ...           ...       ...   \n","102189  0.687270     0.499729   0.499729      0.462082  0.513471   \n","102190  0.418969     0.531234   0.682373      0.462082  0.513471   \n","102191  0.530120     0.541985   0.516393      0.462082  0.523978   \n","102192  1.000000     0.531234   0.682373      0.672809  0.513471   \n","102193  0.307960     0.304348   0.458333      0.462082  0.353062   \n","\n","        DS_INDICACAO_ACIDENTE    DS_CBO  NR_SEQ_REQUISICAO  DS_STATUS_ITEM  \n","0                    0.485784  0.517373                0.0               0  \n","1                    0.575487  0.517373                0.0               0  \n","2                    0.485784  0.517373                0.0               0  \n","3                    0.575487  0.517373                0.5               0  \n","4                    0.485784  0.517373                0.0               0  \n","...                       ...       ...                ...             ...  \n","102189               0.485784  0.504591                1.0               1  \n","102190               0.485784  0.517373                1.0               1  \n","102191               0.485784  0.373737                1.0               1  \n","102192               0.575487  0.517373                1.0               1  \n","102193               0.575487  0.299464                0.5               1  \n","\n","[102194 rows x 12 columns]"],"text/html":["\n","  <div id=\"df-7087193a-e764-4ad5-825f-11a1b8851c22\">\n","    <div class=\"colab-df-container\">\n","      <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>DT_NASCIMENTO</th>\n","      <th>DS_TIPO_PREST_SOLICITANTE</th>\n","      <th>DS_CARATER_ATENDIMENTO</th>\n","      <th>CD_CID</th>\n","      <th>DS_SUBGRUPO</th>\n","      <th>DS_CLASSE</th>\n","      <th>DS_TIPO_GUIA</th>\n","      <th>DS_GRUPO</th>\n","      <th>DS_INDICACAO_ACIDENTE</th>\n","      <th>DS_CBO</th>\n","      <th>NR_SEQ_REQUISICAO</th>\n","      <th>DS_STATUS_ITEM</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.636364</td>\n","      <td>0.609573</td>\n","      <td>0.623313</td>\n","      <td>0.384615</td>\n","      <td>0.528302</td>\n","      <td>0.528302</td>\n","      <td>0.672809</td>\n","      <td>0.528302</td>\n","      <td>0.485784</td>\n","      <td>0.517373</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.000000</td>\n","      <td>0.490390</td>\n","      <td>0.462760</td>\n","      <td>0.428571</td>\n","      <td>0.531234</td>\n","      <td>0.374195</td>\n","      <td>0.462082</td>\n","      <td>0.513471</td>\n","      <td>0.575487</td>\n","      <td>0.517373</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.250000</td>\n","      <td>0.609573</td>\n","      <td>0.623313</td>\n","      <td>0.642869</td>\n","      <td>0.531234</td>\n","      <td>0.544776</td>\n","      <td>0.462082</td>\n","      <td>0.513471</td>\n","      <td>0.485784</td>\n","      <td>0.517373</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.500000</td>\n","      <td>0.609573</td>\n","      <td>0.462760</td>\n","      <td>0.333333</td>\n","      <td>0.528302</td>\n","      <td>0.528302</td>\n","      <td>0.564332</td>\n","      <td>0.528302</td>\n","      <td>0.575487</td>\n","      <td>0.517373</td>\n","      <td>0.5</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.595506</td>\n","      <td>0.609573</td>\n","      <td>0.462760</td>\n","      <td>0.307960</td>\n","      <td>0.387223</td>\n","      <td>0.281682</td>\n","      <td>0.462082</td>\n","      <td>0.433423</td>\n","      <td>0.485784</td>\n","      <td>0.517373</td>\n","      <td>0.0</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","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>102189</th>\n","      <td>0.800000</td>\n","      <td>0.433879</td>\n","      <td>0.462760</td>\n","      <td>0.687270</td>\n","      <td>0.499729</td>\n","      <td>0.499729</td>\n","      <td>0.462082</td>\n","      <td>0.513471</td>\n","      <td>0.485784</td>\n","      <td>0.504591</td>\n","      <td>1.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>102190</th>\n","      <td>0.600000</td>\n","      <td>0.490390</td>\n","      <td>0.462760</td>\n","      <td>0.418969</td>\n","      <td>0.531234</td>\n","      <td>0.682373</td>\n","      <td>0.462082</td>\n","      <td>0.513471</td>\n","      <td>0.485784</td>\n","      <td>0.517373</td>\n","      <td>1.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>102191</th>\n","      <td>0.666667</td>\n","      <td>0.433879</td>\n","      <td>0.462760</td>\n","      <td>0.530120</td>\n","      <td>0.541985</td>\n","      <td>0.516393</td>\n","      <td>0.462082</td>\n","      <td>0.523978</td>\n","      <td>0.485784</td>\n","      <td>0.373737</td>\n","      <td>1.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>102192</th>\n","      <td>1.000000</td>\n","      <td>0.609573</td>\n","      <td>0.623313</td>\n","      <td>1.000000</td>\n","      <td>0.531234</td>\n","      <td>0.682373</td>\n","      <td>0.672809</td>\n","      <td>0.513471</td>\n","      <td>0.575487</td>\n","      <td>0.517373</td>\n","      <td>1.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>102193</th>\n","      <td>0.410959</td>\n","      <td>0.433879</td>\n","      <td>0.462760</td>\n","      <td>0.307960</td>\n","      <td>0.304348</td>\n","      <td>0.458333</td>\n","      <td>0.462082</td>\n","      <td>0.353062</td>\n","      <td>0.575487</td>\n","      <td>0.299464</td>\n","      <td>0.5</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>102194 rows × 12 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7087193a-e764-4ad5-825f-11a1b8851c22')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      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DIAGNOSTICOS E TERAPEUTICOS                Outros  Medico clinico   \n","2  PROCED. DIAGNOSTICOS E TERAPEUTICOS          N?o acidente  Medico clinico   \n","\n","   NR_SEQ_REQUISICAO  DT_REQUISICAO  DS_STATUS_ITEM  \n","0            1458911        2459542               0  \n","1             642753        2459108               0  \n","2            1402326        2459513               0  "],"text/html":["\n","  <div id=\"df-0f3abd67-94f1-4c88-b942-f8d0d2039f6f\">\n","    <div class=\"colab-df-container\">\n","      <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>DT_NASCIMENTO</th>\n","      <th>DS_TIPO_PREST_SOLICITANTE</th>\n","      <th>DS_CARATER_ATENDIMENTO</th>\n","      <th>CD_CID</th>\n","      <th>DS_SUBGRUPO</th>\n","      <th>DS_CLASSE</th>\n","      <th>DS_TIPO_GUIA</th>\n","      <th>DS_GRUPO</th>\n","      <th>DS_INDICACAO_ACIDENTE</th>\n","      <th>DS_CBO</th>\n","      <th>NR_SEQ_REQUISICAO</th>\n","      <th>DT_REQUISICAO</th>\n","      <th>DS_STATUS_ITEM</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2429082</td>\n","      <td>HOSPITAL</td>\n","      <td>Urgencia/Emergencia</td>\n","      <td>I509</td>\n","      <td>DIARIAS</td>\n","      <td>DIARIAS</td>\n","      <td>Guia de solicitac?o de prorrogac?o de internac?o</td>\n","      <td>DIARIAS</td>\n","      <td>N?o acidente</td>\n","      <td>Medico clinico</td>\n","      <td>1458911</td>\n","      <td>2459542</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2438342</td>\n","      <td>LABORATORIO</td>\n","      <td>Eletiva</td>\n","      <td>Z008</td>\n","      <td>MEDICINA LABORATORIAL</td>\n","      <td>ENDOCRINOLOGIA LABORATORIAL</td>\n","      <td>Guia de solicitac?o SP/SADT</td>\n","      <td>PROCED. DIAGNOSTICOS E TERAPEUTICOS</td>\n","      <td>Outros</td>\n","      <td>Medico clinico</td>\n","      <td>642753</td>\n","      <td>2459108</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2444117</td>\n","      <td>HOSPITAL</td>\n","      <td>Urgencia/Emergencia</td>\n","      <td>Z10</td>\n","      <td>MEDICINA LABORATORIAL</td>\n","      <td>BIOQUIMICA</td>\n","      <td>Guia de solicitac?o SP/SADT</td>\n","      <td>PROCED. DIAGNOSTICOS E TERAPEUTICOS</td>\n","      <td>N?o acidente</td>\n","      <td>Medico clinico</td>\n","      <td>1402326</td>\n","      <td>2459513</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f3abd67-94f1-4c88-b942-f8d0d2039f6f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-0f3abd67-94f1-4c88-b942-f8d0d2039f6f button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Marques","userId":"04292042536301413808"},"user_tz":180},"id":"fiRY4njrayWe","colab":{"base_uri":"https://localhost:8080/","height":488},"outputId":"ad9929c4-7c79-4036-a3c2-098b0f4003de"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["         DT_NASCIMENTO  DS_TIPO_PREST_SOLICITANTE  DS_CARATER_ATENDIMENTO  \\\n","0             1.750000                   1.561301                1.654721   \n","1             0.000000                   0.962283                0.861364   \n","2             0.333333                   1.561301                1.654721   \n","3             1.000000                   1.561301                0.861364   \n","4             1.472222                   1.561301                0.861364   \n","...                ...                        ...                     ...   \n","102189        4.000000                   0.766406                0.861364   \n","102190        1.500000                   0.962283                0.861364   \n","102191        2.000000                   0.766406                0.861364   \n","102192  1000000.000000                   1.561301                1.654721   \n","102193        0.697674                   0.766406                0.861364   \n","\n","                CD_CID  DS_SUBGRUPO  DS_CLASSE  DS_TIPO_GUIA  DS_GRUPO  \\\n","0             0.625000     1.120000   1.120000      2.056316  1.120000   \n","1             0.750000     1.133260   0.597943      0.859018  1.055376   \n","2             1.800092     1.133260   1.196721      0.859018  1.055376   \n","3             0.500000     1.120000   1.120000      1.295327  1.120000   \n","4             0.445002     0.631915   0.392141      0.859018  0.764985   \n","...                ...          ...        ...           ...       ...   \n","102189        2.197647     0.998917   0.998917      0.859018  1.055376   \n","102190        0.721080     1.133260   2.148346      0.859018  1.055376   \n","102191        1.128205     1.183333   1.067797      0.859018  1.100743   \n","102192  1000000.000000     1.133260   2.148346      2.056316  1.055376   \n","102193        0.445002     0.437500   0.846154      0.859018  0.545743   \n","\n","        DS_INDICACAO_ACIDENTE    DS_CBO  NR_SEQ_REQUISICAO  DT_REQUISICAO  \\\n","0                    0.944708  1.071995                0.0       0.588235   \n","1                    1.355641  1.071995                0.0       0.453125   \n","2                    0.944708  1.071995                0.0       1.281250   \n","3                    1.355641  1.071995                1.0       1.376404   \n","4                    0.944708  1.071995                0.0       0.468354   \n","...                       ...       ...                ...            ...   \n","102189               0.944708  1.018533          1000000.0       3.552941   \n","102190               0.944708  1.071995          1000000.0       0.470817   \n","102191               0.944708  0.596774          1000000.0       0.761905   \n","102192               1.355641  1.071995          1000000.0       2.049587   \n","102193               1.355641  0.427479                1.0       2.732673   \n","\n","        DS_STATUS_ITEM  \n","0                    0  \n","1                    0  \n","2                    0  \n","3                    0  \n","4                    0  \n","...                ...  \n","102189               1  \n","102190               1  \n","102191               1  \n","102192               1  \n","102193               1  \n","\n","[102194 rows x 13 columns]"],"text/html":["\n","  <div id=\"df-c4865842-620d-445a-b262-0fd7fa632eed\">\n","    <div class=\"colab-df-container\">\n","      <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>DT_NASCIMENTO</th>\n","      <th>DS_TIPO_PREST_SOLICITANTE</th>\n","      <th>DS_CARATER_ATENDIMENTO</th>\n","      <th>CD_CID</th>\n","      <th>DS_SUBGRUPO</th>\n","      <th>DS_CLASSE</th>\n","      <th>DS_TIPO_GUIA</th>\n","      <th>DS_GRUPO</th>\n","      <th>DS_INDICACAO_ACIDENTE</th>\n","      <th>DS_CBO</th>\n","      <th>NR_SEQ_REQUISICAO</th>\n","      <th>DT_REQUISICAO</th>\n","      <th>DS_STATUS_ITEM</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.750000</td>\n","      <td>1.561301</td>\n","      <td>1.654721</td>\n","      <td>0.625000</td>\n","      <td>1.120000</td>\n","      <td>1.120000</td>\n","      <td>2.056316</td>\n","      <td>1.120000</td>\n","      <td>0.944708</td>\n","      <td>1.071995</td>\n","      <td>0.0</td>\n","      <td>0.588235</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.000000</td>\n","      <td>0.962283</td>\n","      <td>0.861364</td>\n","      <td>0.750000</td>\n","      <td>1.133260</td>\n","      <td>0.597943</td>\n","      <td>0.859018</td>\n","      <td>1.055376</td>\n","      <td>1.355641</td>\n","      <td>1.071995</td>\n","      <td>0.0</td>\n","      <td>0.453125</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.333333</td>\n","      <td>1.561301</td>\n","      <td>1.654721</td>\n","      <td>1.800092</td>\n","      <td>1.133260</td>\n","      <td>1.196721</td>\n","      <td>0.859018</td>\n","      <td>1.055376</td>\n","      <td>0.944708</td>\n","      <td>1.071995</td>\n","      <td>0.0</td>\n","      <td>1.281250</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.000000</td>\n","      <td>1.561301</td>\n","      <td>0.861364</td>\n","      <td>0.500000</td>\n","      <td>1.120000</td>\n","      <td>1.120000</td>\n","      <td>1.295327</td>\n","      <td>1.120000</td>\n","      <td>1.355641</td>\n","      <td>1.071995</td>\n","      <td>1.0</td>\n","      <td>1.376404</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.472222</td>\n","      <td>1.561301</td>\n","      <td>0.861364</td>\n","      <td>0.445002</td>\n","      <td>0.631915</td>\n","      <td>0.392141</td>\n","      <td>0.859018</td>\n","      <td>0.764985</td>\n","      <td>0.944708</td>\n","      <td>1.071995</td>\n","      <td>0.0</td>\n","      <td>0.468354</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","      <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>102189</th>\n","      <td>4.000000</td>\n","      <td>0.766406</td>\n","      <td>0.861364</td>\n","      <td>2.197647</td>\n","      <td>0.998917</td>\n","      <td>0.998917</td>\n","      <td>0.859018</td>\n","      <td>1.055376</td>\n","      <td>0.944708</td>\n","      <td>1.018533</td>\n","      <td>1000000.0</td>\n","      <td>3.552941</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>102190</th>\n","      <td>1.500000</td>\n","      <td>0.962283</td>\n","      <td>0.861364</td>\n","      <td>0.721080</td>\n","      <td>1.133260</td>\n","      <td>2.148346</td>\n","      <td>0.859018</td>\n","      <td>1.055376</td>\n","      <td>0.944708</td>\n","      <td>1.071995</td>\n","      <td>1000000.0</td>\n","      <td>0.470817</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>102191</th>\n","      <td>2.000000</td>\n","      <td>0.766406</td>\n","      <td>0.861364</td>\n","      <td>1.128205</td>\n","      <td>1.183333</td>\n","      <td>1.067797</td>\n","      <td>0.859018</td>\n","      <td>1.100743</td>\n","      <td>0.944708</td>\n","      <td>0.596774</td>\n","      <td>1000000.0</td>\n","      <td>0.761905</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>102192</th>\n","      <td>1000000.000000</td>\n","      <td>1.561301</td>\n","      <td>1.654721</td>\n","      <td>1000000.000000</td>\n","      <td>1.133260</td>\n","      <td>2.148346</td>\n","      <td>2.056316</td>\n","      <td>1.055376</td>\n","      <td>1.355641</td>\n","      <td>1.071995</td>\n","      <td>1000000.0</td>\n","      <td>2.049587</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>102193</th>\n","      <td>0.697674</td>\n","      <td>0.766406</td>\n","      <td>0.861364</td>\n","      <td>0.445002</td>\n","      <td>0.437500</td>\n","      <td>0.846154</td>\n","      <td>0.859018</td>\n","      <td>0.545743</td>\n","      <td>1.355641</td>\n","      <td>0.427479</td>\n","      <td>1.0</td>\n","      <td>2.732673</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>102194 rows × 13 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4865842-620d-445a-b262-0fd7fa632eed')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-c4865842-620d-445a-b262-0fd7fa632eed');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":267}],"source":["# https://medium.com/analytics-vidhya/categorical-variable-encoding-techniques-17e607fe42f9#:~:text=like%20Logistic%20Regression.-,Count%20or%20frequency%20encoding,-First%20step%20is\n","# https://towardsdatascience.com/all-about-categorical-variable-encoding-305f3361fd02\n","\n","for name, values in df.iteritems():\n","  if name != 'DS_STATUS_ITEM':\n","    pr_df = df.groupby(name)['DS_STATUS_ITEM'].mean()\n","    pr_df = pd.DataFrame(pr_df)\n","    pr_df = pr_df.rename(columns = {'DS_STATUS_ITEM': 'Good'})\n","    pr_df['Bad'] = 1-pr_df.Good\n","    pr_df['Bad'] = np.where(pr_df['Bad'] == 0, 0.000001, pr_df['Bad'])\n","    pr_df['PR'] = pr_df.Good/pr_df.Bad\n","\n","    df.loc[:, name] = df[name].map(pr_df['PR'])\n","\n","df"]},{"cell_type":"markdown","metadata":{"id":"nrlHBrTuz3OU"},"source":["### Label Encoder"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LEe6hXNBz2iq"},"outputs":[],"source":["# from collections import defaultdict\n","# from sklearn.preprocessing import LabelEncoder\n","\n","# df = pd.DataFrame(X_data)\n","                  \n","# d = defaultdict(LabelEncoder)\n","\n","# # Encoding the variable\n","# fit = df.apply(lambda x: d[x.name].fit_transform(x))\n","\n","# # Inverse the encoded\n","# fit.apply(lambda x: d[x.name].inverse_transform(x))\n","\n","# # Using the dictionary to label future data\n","# X_data = df.apply(lambda x: d[x.name].transform(x))\n","\n","# X_data = X_data.iloc[:, 0:12].values"]},{"cell_type":"markdown","metadata":{"id":"1hTTuCqRaXd1"},"source":["##Pré-treino"]},{"cell_type":"markdown","metadata":{"id":"cOSLRdDEsWFx"},"source":["### Divisão entre previsores e classe"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":485,"status":"ok","timestamp":1650064496909,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"AebORElCWYmK","outputId":"d430f678-f0dc-42bf-da7f-a7d7ed136fd8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(102194, 12)"]},"metadata":{},"execution_count":200}],"source":["df.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1650064497310,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"JeE1xaPHB_vo","outputId":"93400515-de66-4502-ce7b-afae921fab6d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.63636364, 0.60957337, 0.62331252, 0.38461538, 0.52830189,\n","       0.52830189, 0.67280869, 0.52830189, 0.48578397, 0.51737326,\n","       0.        ])"]},"metadata":{},"execution_count":201}],"source":["X_data = df.iloc[:, 0:11].values\n","X_data[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"usDHmfBTC64Y"},"outputs":[],"source":["y_data= df.iloc[:, 11].values"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1650064497312,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"2_XXNWDVDDRE","outputId":"f6188cae-e240-4202-976e-9cd4f7bedafa"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(102194,)"]},"metadata":{},"execution_count":203}],"source":["y_data.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1650064497313,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"1ZIBFk39Y-el","outputId":"346249c6-2bd7-4622-db94-9841e9edec39"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0"]},"metadata":{},"execution_count":204}],"source":["y_data[0]"]},{"cell_type":"markdown","metadata":{"id":"LXSsHxkKQO43"},"source":["### Normalização"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":411,"status":"ok","timestamp":1650064171235,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"Y7TFWI80Q4xB","outputId":"c4f738cc-171c-4a62-da83-91c9ef7bb003"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 4.33734563e-01,  1.39847834e+00,  1.81968555e+00, ...,\n","        -4.31357441e-01,  2.11038169e-01, -1.05103531e+00],\n","       [-1.59036006e+00, -1.22657797e-01, -5.49545498e-01, ...,\n","         2.29050823e+00,  2.11038169e-01, -1.05103531e+00],\n","       [-7.95180032e-01,  1.39847834e+00,  1.81968555e+00, ...,\n","        -4.31357441e-01,  2.11038169e-01, -1.05103531e+00],\n","       ...,\n","       [ 5.30120021e-01, -8.43900061e-01, -5.49545498e-01, ...,\n","        -4.31357441e-01, -1.53375018e+00,  1.05103531e+00],\n","       [ 1.59036006e+00,  1.39847834e+00,  1.81968555e+00, ...,\n","         2.29050823e+00,  2.11038169e-01,  1.05103531e+00],\n","       [-2.83214806e-01, -8.43900061e-01, -5.49545498e-01, ...,\n","         2.29050823e+00, -2.43596816e+00,  1.16688360e-16]])"]},"metadata":{},"execution_count":148}],"source":["from sklearn.preprocessing import StandardScaler\n","scaler_data = StandardScaler()\n","X_data = scaler_data.fit_transform(X_data)\n","X_data"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":295,"status":"ok","timestamp":1650064172090,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"YMKdQfQMZBgw","outputId":"2c6a7317-a156-407f-fee0-636aa68cdc02"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(102194, 11)"]},"metadata":{},"execution_count":149}],"source":["X_data.shape"]},{"cell_type":"markdown","metadata":{"id":"kf5MpvOhYoKp"},"source":["###Divisão train e test"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":394,"status":"ok","timestamp":1650064500718,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"NGPwsH3OYnUs","outputId":"4dfc89f9-3184-466e-8e44-b928e540d1b0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["((30659, 11), (30659,))"]},"metadata":{},"execution_count":205}],"source":["from sklearn.model_selection import train_test_split\n","# Splitting the data into train and test\n","X_data_treinamento, X_data_teste, y_data_treinamento, y_data_teste = train_test_split(X_data, y_data, test_size = 0.3, random_state = 0)\n","X_data_teste.shape, y_data_teste.shape"]},{"cell_type":"markdown","metadata":{"id":"4LsWgEEZYDIL"},"source":["### Redução da Dimensionalidade"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"arYyTmd_XG1Z"},"outputs":[],"source":["from sklearn.decomposition import PCA\n","\n","pca = PCA(n_components=6)\n","X_data_treinamento = pca.fit_transform(X_data_treinamento)\n","X_data_teste = pca.transform(X_data_teste)"]},{"cell_type":"markdown","metadata":{"id":"OF5pIeps-D_c"},"source":["## Etapa de Treinamento"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1Pr01wnzBCrV"},"outputs":[],"source":["from sklearn.model_selection import GridSearchCV\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.svm import SVC\n","from sklearn.neural_network import MLPClassifier"]},{"cell_type":"markdown","metadata":{"id":"ASzfDqq0HRym"},"source":["###DecisionTreeClassifier com análise de Overfiting"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2602,"status":"ok","timestamp":1650064203135,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"qte-TTr3HarP","outputId":"5bc9ba84-aafd-48d5-bf22-f0995eb2aad9"},"outputs":[{"output_type":"stream","name":"stdout","text":[">1, train: 0.962, test: 0.962\n",">2, train: 0.962, test: 0.962\n",">3, train: 0.965, test: 0.964\n",">4, train: 0.965, test: 0.964\n",">5, train: 0.965, test: 0.964\n",">6, train: 0.966, test: 0.965\n",">7, train: 0.968, test: 0.966\n",">8, train: 0.969, test: 0.966\n",">9, train: 0.970, test: 0.967\n",">10, train: 0.972, test: 0.966\n",">11, train: 0.973, test: 0.965\n",">12, train: 0.975, test: 0.965\n",">13, train: 0.977, test: 0.964\n",">14, train: 0.979, test: 0.964\n",">15, train: 0.981, test: 0.963\n",">16, train: 0.982, test: 0.961\n",">17, train: 0.983, test: 0.962\n",">18, train: 0.984, test: 0.961\n",">19, train: 0.985, test: 0.961\n",">20, train: 0.986, test: 0.960\n",">21, train: 0.987, test: 0.960\n",">22, train: 0.987, test: 0.959\n",">23, train: 0.987, test: 0.959\n",">24, train: 0.988, test: 0.959\n"]}],"source":["# https://machinelearningmastery.com/overfitting-machine-learning-models/\n","\n","# evaluate knn performance on train and test sets with different numbers of neighbors\n","from sklearn.datasets import make_classification\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import accuracy_score\n","from sklearn.neighbors import KNeighborsClassifier\n","from matplotlib import pyplot\n","\n","train_scores = []\n","test_scores = []\n","values = [i for i in range(1, 25)]\n","\n","# evaluate a decision tree for each depth\n","for i in values:\n","\t# configure the model\n","\tmodel = DecisionTreeClassifier(max_depth=i)\n","\t# fit model on the training dataset\n","\tmodel.fit(X_data_treinamento, y_data_treinamento)\n","\t# evaluate on the train dataset\n","\ttrain_yhat = model.predict(X_data_treinamento)\n","\ttrain_acc = accuracy_score(y_data_treinamento, train_yhat)\n","\ttrain_scores.append(train_acc)\n","\t# evaluate on the test dataset\n","\ttest_yhat = model.predict(X_data_teste)\n","\ttest_acc = accuracy_score(y_data_teste, test_yhat)\n","\ttest_scores.append(test_acc)\n","\t# summarize progress\n","\tprint('>%d, train: %.3f, test: %.3f' % (i, train_acc, test_acc))"]},{"cell_type":"code","source":["# plot of train and test scores vs tree depth\n","pyplot.plot(values, train_scores, '-o', label='Train')\n","pyplot.plot(values, test_scores, '-o', label='Test')\n","pyplot.legend()\n","pyplot.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":347},"id":"Ubz00ninQpgf","executionInfo":{"status":"ok","timestamp":1650064203505,"user_tz":180,"elapsed":53,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"ca1ed910-972c-4a3f-8d00-a6b963d0238e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 576x396 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"D_7NZfh0RTEB"},"source":["###RandomForest"]},{"cell_type":"markdown","metadata":{"id":"goQDQ9D9BwHS"},"source":["#### Tuning dos parâmetros com GridSearch"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"B3CqFyHpBJiY"},"outputs":[],"source":["# parametros = {'criterion': ['gini', 'entropy'],\n","#               'n_estimators': [10, 40, 100, 150],\n","#               'min_samples_split': [2, 5, 10],\n","#               'min_samples_leaf': [1, 5, 10]}\n","\n","# grid_search = GridSearchCV(estimator=RandomForestClassifier(), param_grid=parametros)\n","# grid_search.fit(X_data, y_data)\n","# melhores_parametros = grid_search.best_params_\n","# melhor_resultado = grid_search.best_score_\n","# print(melhores_parametros)\n","# print(melhor_resultado)"]},{"cell_type":"markdown","source":["**Parametros de acordo com o metódo encoder aplicado.**\n","\n","Target Enconding:\n","\n","* n_estimators=150, criterion = 'entropy', min_samples_split = 10, min_samples_leaf= 10\n","\n","Prob Encoder \n","* 'criterion': 'entropy', 'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 150\n"],"metadata":{"id":"hx0leWJZQUzZ"}},{"cell_type":"markdown","source":["#### Treinamento"],"metadata":{"id":"k9TbOg0vHS1m"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"vzlCCcQ7ZVGJ"},"outputs":[],"source":["from sklearn.ensemble import RandomForestClassifier"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7185,"status":"ok","timestamp":1650064519487,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"mdsn8CwxZXGS","outputId":"95932ac6-a692-42c4-fbd3-454f68fd73e1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestClassifier(criterion='entropy', min_samples_leaf=10,\n","                       min_samples_split=10, n_estimators=150)"]},"metadata":{},"execution_count":206}],"source":["random_forest = RandomForestClassifier(n_estimators=150, criterion = 'entropy', min_samples_split = 10, min_samples_leaf= 10)\n","random_forest.fit(X_data_treinamento, y_data_treinamento)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":470,"status":"ok","timestamp":1650064519930,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"8bJ8eWaNZYYy","outputId":"cee14a9e-9fea-4a53-b63a-54e4e3c904c8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 0, 0, ..., 1, 1, 0])"]},"metadata":{},"execution_count":207}],"source":["previsoes = random_forest.predict(X_data_teste)\n","previsoes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1650064519932,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"R7Axb3CKZyug","outputId":"44bfb890-7a31-4fca-d23e-da17e0ab7f9b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9674157669852246"]},"metadata":{},"execution_count":208}],"source":["from sklearn.metrics import accuracy_score\n","accuracy_score(y_data_teste, previsoes)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":361},"executionInfo":{"elapsed":1167,"status":"ok","timestamp":1650064521089,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"aQrpg_FdqDhq","outputId":"3a1eec45-b5ac-4e91-b176-6c045fa5ef70"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9674157669852246"]},"metadata":{},"execution_count":209},{"output_type":"display_data","data":{"text/plain":["<Figure size 576x396 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["from yellowbrick.classifier import ConfusionMatrix\n","cm = ConfusionMatrix(random_forest)\n","cm.fit(X_data_treinamento, y_data_treinamento)\n","cm.score(X_data_teste, y_data_teste)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":308,"status":"ok","timestamp":1650064524314,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"tVN-TFHmqUjh","outputId":"0956c5a5-e23f-49b3-da33-e807a93d7b91"},"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.96      0.97      0.97     15219\n","           1       0.97      0.96      0.97     15440\n","\n","    accuracy                           0.97     30659\n","   macro avg       0.97      0.97      0.97     30659\n","weighted avg       0.97      0.97      0.97     30659\n","\n"]}],"source":["from sklearn.metrics import accuracy_score, classification_report\n","print(classification_report(y_data_teste, previsoes))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1405,"status":"ok","timestamp":1650064212964,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"QfLz4npD01c_","outputId":"d08a581b-f1ad-4dcd-bc43-4d525bea94d2"},"outputs":[{"output_type":"stream","name":"stdout","text":["Precision (micro): 0.966829\n","Recall (micro):    0.966829\n","F1 score (micro):  0.966829\n","\n","Precision (macro): 0.966843\n","Recall (macro):    0.966863\n","F1 score (macro):  0.966829\n","\n","Precision (weighted): 0.966879\n","Recall (weighted):    0.966829\n","F1 score (weighted):  0.966829\n"]}],"source":["# https://simonhessner.de/why-are-precision-recall-and-f1-score-equal-when-using-micro-averaging-in-a-multi-class-problem/\n","\n","from sklearn.metrics import precision_score, recall_score, f1_score\n","\n","# These values are the same as in the table above\n","labels      = y_data_teste\n","predicitons = previsoes\n","print(\"Precision (micro): %f\" % precision_score(labels, predicitons, average='micro'))\n","print(\"Recall (micro):    %f\" % recall_score(labels, predicitons, average='micro'))\n","print(\"F1 score (micro):  %f\" % f1_score(labels, predicitons, average='micro'), end='\\n\\n')\n","print(\"Precision (macro): %f\" % precision_score(labels, predicitons, average='macro'))\n","print(\"Recall (macro):    %f\" % recall_score(labels, predicitons, average='macro'))\n","print(\"F1 score (macro):  %f\" % f1_score(labels, predicitons, average='macro'), end='\\n\\n')\n","print(\"Precision (weighted): %f\" % precision_score(labels, predicitons, average='weighted'))\n","print(\"Recall (weighted):    %f\" % recall_score(labels, predicitons, average='weighted'))\n","print(\"F1 score (weighted):  %f\" % f1_score(labels, predicitons, average='weighted'))"]},{"cell_type":"markdown","metadata":{"id":"YvaRztnDp6jb"},"source":["### Redes Neurais"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"svm3Arqfp8iL"},"outputs":[],"source":["from sklearn.neural_network import MLPClassifier"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1650064530800,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"MZ5MDx9-rorv","outputId":"333bc0b1-6d1d-4986-ccd2-fd5ac1274eb8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["((30659, 11), (30659,))"]},"metadata":{},"execution_count":212}],"source":["X_data_teste.shape, y_data_teste.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1650064530801,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"RSYVXUcirw2u","outputId":"c5b20a55-b8b5-453d-c8c0-ba94cfaa92d3"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["4.5"]},"metadata":{},"execution_count":213}],"source":["(8 + 1) / 2"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14348,"status":"ok","timestamp":1650064545136,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"1fW2zuHMr2ah","outputId":"499a78f8-43e8-414e-d505-fd81df2293c9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Iteration 1, loss = 0.65849380\n","Iteration 2, loss = 0.40438824\n","Iteration 3, loss = 0.23473069\n","Iteration 4, loss = 0.09877307\n","Iteration 5, loss = 0.08780822\n","Iteration 6, loss = 0.08577335\n","Iteration 7, loss = 0.08482526\n","Iteration 8, loss = 0.08435553\n","Iteration 9, loss = 0.08415752\n","Iteration 10, loss = 0.08389552\n","Iteration 11, loss = 0.08376025\n","Iteration 12, loss = 0.08361405\n","Iteration 13, loss = 0.08354521\n","Iteration 14, loss = 0.08348521\n","Iteration 15, loss = 0.08338427\n","Iteration 16, loss = 0.08332415\n","Iteration 17, loss = 0.08324514\n","Iteration 18, loss = 0.08322546\n","Iteration 19, loss = 0.08319738\n","Iteration 20, loss = 0.08311714\n","Iteration 21, loss = 0.08316696\n","Iteration 22, loss = 0.08322387\n","Iteration 23, loss = 0.08307798\n","Iteration 24, loss = 0.08311266\n","Iteration 25, loss = 0.08295441\n","Iteration 26, loss = 0.08298869\n","Iteration 27, loss = 0.08298419\n","Iteration 28, loss = 0.08297735\n","Iteration 29, loss = 0.08300617\n","Iteration 30, loss = 0.08291471\n","Iteration 31, loss = 0.08292042\n","Iteration 32, loss = 0.08296231\n","Iteration 33, loss = 0.08291400\n","Iteration 34, loss = 0.08284070\n","Iteration 35, loss = 0.08292794\n","Iteration 36, loss = 0.08281559\n","Iteration 37, loss = 0.08290777\n","Iteration 38, loss = 0.08281908\n","Iteration 39, loss = 0.08287503\n","Iteration 40, loss = 0.08285585\n","Iteration 41, loss = 0.08290441\n","Iteration 42, loss = 0.08284256\n","Iteration 43, loss = 0.08274131\n","Iteration 44, loss = 0.08278206\n","Iteration 45, loss = 0.08273565\n","Iteration 46, loss = 0.08274015\n","Iteration 47, loss = 0.08272636\n","Iteration 48, loss = 0.08274541\n","Iteration 49, loss = 0.08261889\n","Iteration 50, loss = 0.08264806\n","Iteration 51, loss = 0.08280423\n","Iteration 52, loss = 0.08267797\n","Iteration 53, loss = 0.08254831\n","Iteration 54, loss = 0.08264459\n","Iteration 55, loss = 0.08270782\n","Iteration 56, loss = 0.08271010\n","Iteration 57, loss = 0.08274642\n","Iteration 58, loss = 0.08264079\n","Iteration 59, loss = 0.08268388\n","Iteration 60, loss = 0.08268931\n","Iteration 61, loss = 0.08273867\n","Iteration 62, loss = 0.08273197\n","Iteration 63, loss = 0.08261314\n","Iteration 64, loss = 0.08267474\n","Training loss did not improve more than tol=0.000010 for 10 consecutive epochs. Stopping.\n"]},{"output_type":"execute_result","data":{"text/plain":["MLPClassifier(hidden_layer_sizes=(5, 5), max_iter=1000, tol=1e-05, verbose=True)"]},"metadata":{},"execution_count":214}],"source":["rede_neural = MLPClassifier(max_iter=1000, verbose=True, tol=0.0000100,\n","                                   solver = 'adam', activation = 'relu',\n","                                   hidden_layer_sizes = (5,5))\n","rede_neural.fit(X_data_treinamento, y_data_treinamento)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":36,"status":"ok","timestamp":1650064545137,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"JozBrnqvAjjp","outputId":"0bbf2d0a-f627-4cc1-edf8-6098fd21800b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 0, 0, ..., 1, 1, 0])"]},"metadata":{},"execution_count":215}],"source":["previsoes = rede_neural.predict(X_data_teste)\n","previsoes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":30,"status":"ok","timestamp":1650064545139,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"RDxHogPYsRd1","outputId":"9bdf38e4-1b7b-43fa-ef77-d729ec1aa58d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9637952966502495"]},"metadata":{},"execution_count":216}],"source":["from sklearn.metrics import accuracy_score\n","accuracy_score(y_data_teste, previsoes)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":597,"status":"ok","timestamp":1650064545708,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"0hbYRcyTsVuH","outputId":"9ec81439-c472-4ec9-9733-bd29a8fb9881"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9637952966502495"]},"metadata":{},"execution_count":217},{"output_type":"display_data","data":{"text/plain":["<Figure size 576x396 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["from yellowbrick.classifier import ConfusionMatrix\n","cm = ConfusionMatrix(rede_neural)\n","cm.fit(X_data_treinamento, y_data_treinamento)\n","cm.score(X_data_teste, y_data_teste)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1650064545709,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"jOXOAJ1bsVuH","outputId":"dcc5962b-d822-4697-c2a6-44c4181d37e9"},"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.97      0.96      0.96     15219\n","           1       0.96      0.97      0.96     15440\n","\n","    accuracy                           0.96     30659\n","   macro avg       0.96      0.96      0.96     30659\n","weighted avg       0.96      0.96      0.96     30659\n","\n"]}],"source":["print(classification_report(y_data_teste, previsoes))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1650064545710,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"PjU3jXL51ntI","outputId":"bc73057e-85b6-4bd1-caf8-b73d2dd69b0b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Precision (micro): 0.963795\n","Recall (micro):    0.963795\n","F1 score (micro):  0.963795\n","\n","Precision (macro): 0.963933\n","Recall (macro):    0.963742\n","F1 score (macro):  0.963788\n","\n","Precision (weighted): 0.963887\n","Recall (weighted):    0.963795\n","F1 score (weighted):  0.963792\n"]}],"source":["from sklearn.metrics import precision_score, recall_score, f1_score\n","\n","# These values are the same as in the table above\n","labels      = y_data_teste\n","predicitons = previsoes\n","print(\"Precision (micro): %f\" % precision_score(labels, predicitons, average='micro'))\n","print(\"Recall (micro):    %f\" % recall_score(labels, predicitons, average='micro'))\n","print(\"F1 score (micro):  %f\" % f1_score(labels, predicitons, average='micro'), end='\\n\\n')\n","print(\"Precision (macro): %f\" % precision_score(labels, predicitons, average='macro'))\n","print(\"Recall (macro):    %f\" % recall_score(labels, predicitons, average='macro'))\n","print(\"F1 score (macro):  %f\" % f1_score(labels, predicitons, average='macro'), end='\\n\\n')\n","print(\"Precision (weighted): %f\" % precision_score(labels, predicitons, average='weighted'))\n","print(\"Recall (weighted):    %f\" % recall_score(labels, predicitons, average='weighted'))\n","print(\"F1 score (weighted):  %f\" % f1_score(labels, predicitons, average='weighted'))"]},{"cell_type":"markdown","metadata":{"id":"sogASE5gs1Pk"},"source":["### KNN"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7147,"status":"ok","timestamp":1650064257021,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"bU5i5w2S8p5j","outputId":"a3a875e3-69b1-4067-b709-5c6151e4b5fe"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9630451091033628"]},"metadata":{},"execution_count":171}],"source":["knn_model = KNeighborsClassifier(n_neighbors=10)\n","knn_model.fit(X_data_treinamento, y_data_treinamento)\n","\n","previsoes = knn_model.predict(X_data_teste)\n","\n","from sklearn.metrics import accuracy_score, classification_report\n","accuracy_score(y_data_teste, previsoes)"]},{"cell_type":"code","source":["previsoes"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VbADygLmUWct","executionInfo":{"status":"ok","timestamp":1650064257023,"user_tz":180,"elapsed":44,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"dc076422-8f75-43b2-ee2f-9e65deca7600"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 0, 0, ..., 1, 1, 0])"]},"metadata":{},"execution_count":172}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12878,"status":"ok","timestamp":1650064269863,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"cThOIxgB9qPm","outputId":"28ca2ce6-939a-42ce-f369-2f7855b38fdd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9630451091033628"]},"metadata":{},"execution_count":173},{"output_type":"display_data","data":{"text/plain":["<Figure size 576x396 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["from yellowbrick.classifier import ConfusionMatrix\n","cm = ConfusionMatrix(knn_model)\n","cm.fit(X_data_treinamento, y_data_treinamento)\n","cm.score(X_data_teste, y_data_teste)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":52,"status":"ok","timestamp":1650064269865,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"hVRBhKRy95hu","outputId":"40f4a118-d597-41f5-c39b-4e5f32a694c2"},"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.96      0.97      0.96     15219\n","           1       0.97      0.96      0.96     15440\n","\n","    accuracy                           0.96     30659\n","   macro avg       0.96      0.96      0.96     30659\n","weighted avg       0.96      0.96      0.96     30659\n","\n"]}],"source":["print(classification_report(y_data_teste, previsoes))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":435,"status":"ok","timestamp":1650064270257,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"cMeOBKwc95hv","outputId":"24b781f3-564a-41c6-9676-6bd044514369"},"outputs":[{"output_type":"stream","name":"stdout","text":["Precision (micro): 0.963045\n","Recall (micro):    0.963045\n","F1 score (micro):  0.963045\n","\n","Precision (macro): 0.963094\n","Recall (macro):    0.963095\n","F1 score (macro):  0.963045\n","\n","Precision (weighted): 0.963143\n","Recall (weighted):    0.963045\n","F1 score (weighted):  0.963045\n"]}],"source":["from sklearn.metrics import precision_score, recall_score, f1_score\n","\n","# These values are the same as in the table above\n","labels      = y_data_teste\n","predicitons = previsoes\n","print(\"Precision (micro): %f\" % precision_score(labels, predicitons, average='micro'))\n","print(\"Recall (micro):    %f\" % recall_score(labels, predicitons, average='micro'))\n","print(\"F1 score (micro):  %f\" % f1_score(labels, predicitons, average='micro'), end='\\n\\n')\n","print(\"Precision (macro): %f\" % precision_score(labels, predicitons, average='macro'))\n","print(\"Recall (macro):    %f\" % recall_score(labels, predicitons, average='macro'))\n","print(\"F1 score (macro):  %f\" % f1_score(labels, predicitons, average='macro'), end='\\n\\n')\n","print(\"Precision (weighted): %f\" % precision_score(labels, predicitons, average='weighted'))\n","print(\"Recall (weighted):    %f\" % recall_score(labels, predicitons, average='weighted'))\n","print(\"F1 score (weighted):  %f\" % f1_score(labels, predicitons, average='weighted'))"]},{"cell_type":"markdown","metadata":{"id":"AvHSFJxq-aJb"},"source":["### SVM"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":31470,"status":"ok","timestamp":1650064301714,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"mNUdeQPC-dwc","outputId":"6422f0cf-ea8c-4a5b-ffe7-c618271cee4d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9639257640497081"]},"metadata":{},"execution_count":177}],"source":["from sklearn.svm import SVC\n","svm_model = SVC(kernel='linear', random_state=1)\n","svm_model.fit(X_data_treinamento, y_data_treinamento)\n","\n","previsoes = svm_model.predict(X_data_teste)\n","\n","from sklearn.metrics import accuracy_score, classification_report\n","accuracy_score(y_data_teste, previsoes)"]},{"cell_type":"code","source":["previsoes"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QfS07LutUZPs","executionInfo":{"status":"ok","timestamp":1650064301717,"user_tz":180,"elapsed":57,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"7a1da232-5c02-4140-e594-4db0ae880157"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 0, 0, ..., 1, 1, 0])"]},"metadata":{},"execution_count":178}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10835,"status":"ok","timestamp":1650064312506,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"S-80W6vI-ywP","outputId":"60a679b9-4677-4e6e-b5d2-a708b99b7622"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9639257640497081"]},"metadata":{},"execution_count":179},{"output_type":"display_data","data":{"text/plain":["<Figure size 576x396 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["from yellowbrick.classifier import ConfusionMatrix\n","cm = ConfusionMatrix(svm_model)\n","cm.fit(X_data_treinamento, y_data_treinamento)\n","cm.score(X_data_teste, y_data_teste)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":837,"status":"ok","timestamp":1650064312511,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"},"user_tz":180},"id":"2J8bmvP0_GEI","outputId":"4231a35a-078d-42e6-8819-8ade946c001d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Precision (micro): 0.963926\n","Recall (micro):    0.963926\n","F1 score (micro):  0.963926\n","\n","Precision (macro): 0.964028\n","Recall (macro):    0.963881\n","F1 score (macro):  0.963919\n","\n","Precision (weighted): 0.963990\n","Recall (weighted):    0.963926\n","F1 score (weighted):  0.963923\n"]}],"source":["from sklearn.metrics import precision_score, recall_score, f1_score\n","\n","# These values are the same as in the table above\n","labels      = y_data_teste\n","predicitons = previsoes\n","print(\"Precision (micro): %f\" % precision_score(labels, predicitons, average='micro'))\n","print(\"Recall (micro):    %f\" % recall_score(labels, predicitons, average='micro'))\n","print(\"F1 score (micro):  %f\" % f1_score(labels, predicitons, average='micro'), end='\\n\\n')\n","print(\"Precision (macro): %f\" % precision_score(labels, predicitons, average='macro'))\n","print(\"Recall (macro):    %f\" % recall_score(labels, predicitons, average='macro'))\n","print(\"F1 score (macro):  %f\" % f1_score(labels, predicitons, average='macro'), end='\\n\\n')\n","print(\"Precision (weighted): %f\" % precision_score(labels, predicitons, average='weighted'))\n","print(\"Recall (weighted):    %f\" % recall_score(labels, predicitons, average='weighted'))\n","print(\"F1 score (weighted):  %f\" % f1_score(labels, predicitons, average='weighted'))"]},{"cell_type":"markdown","metadata":{"id":"-vaNfAEf16P7"},"source":["##Validação Cruzada"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"CBP_opet198X","outputId":"0a22776b-5797-4444-b053-411e4b2b4516"},"outputs":[{"name":"stdout","output_type":"stream","text":["0\n","1\n"]}],"source":["from sklearn.model_selection import cross_val_score, KFold\n","\n","resultados_arvore = []\n","resultados_random_forest = []\n","resultados_knn = []\n","resultados_logistica = []\n","resultados_svm = []\n","resultados_rede_neural = []\n","\n","for i in range(30):\n","  print(i)\n","  kfold = KFold(n_splits=10, shuffle=True, random_state=i)\n","\n","  rede_neural = MLPClassifier(activation = 'relu', batch_size = 10, solver = 'adam')\n","  scores = cross_val_score(rede_neural, X_data, y_data, cv = kfold)\n","  resultados_rede_neural.append(scores.mean())\n","\n","  random_forest = RandomForestClassifier(criterion = 'entropy', min_samples_leaf = 1, min_samples_split=5, n_estimators = 10)\n","  scores = cross_val_score(random_forest, X_data, y_data, cv = kfold)\n","  resultados_random_forest.append(scores.mean())\n","\n","  arvore = DecisionTreeClassifier(criterion='entropy', min_samples_leaf=1, min_samples_split=5, splitter='best')\n","  scores = cross_val_score(arvore, X_data, y_data, cv = kfold)\n","  resultados_arvore.append(scores.mean())\n","\n","  knn = KNeighborsClassifier()\n","  scores = cross_val_score(knn, X_data, y_data, cv = kfold)\n","  resultados_knn.append(scores.mean())\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6ARiHpQY4D11"},"outputs":[],"source":["resultados = pd.DataFrame({'Arvore': resultados_arvore, 'Random forest': resultados_random_forest,\n","                           'KNN': resultados_knn, 'Logistica': resultados_logistica,\n","                           'SVM': resultados_svm, 'Rede neural': resultados_rede_neural})\n","\n","resultados.to_csv('/content/drive/MyDrive/Trabalho Mineração de Dados [UFG]/resultados_validacao_cruzada.csv')\n","resultados.to_csv('/content/resultados_validacao_cruzada.csv')\n","files.download('resultados_validacao_cruzada.csv')\n","!cp resultados_validacao_cruzada.csv \"/content/drive/My Drive/resultados_validacao_cruzada.csv\""]},{"cell_type":"code","source":["pd.read_csv('/content/resultados_validacao_cruzada.csv')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"a8u6UAXE70LT","executionInfo":{"status":"ok","timestamp":1650088459996,"user_tz":180,"elapsed":339,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"fdbf49b3-b00d-4695-9631-f6c23888322d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Unnamed: 0    Arvore  Random forest       KNN  Rede neural\n","0           0  0.957307       0.960624  0.962503     0.965106\n","1           1  0.956817       0.960800  0.961201     0.964851\n","2           2  0.957493       0.961240  0.961465     0.965106\n","3           3  0.957307       0.961534  0.961465     0.965928"],"text/html":["\n","  <div id=\"df-5352bb32-d96b-4cf5-b646-fe1749e4da35\">\n","    <div class=\"colab-df-container\">\n","      <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>Unnamed: 0</th>\n","      <th>Arvore</th>\n","      <th>Random forest</th>\n","      <th>KNN</th>\n","      <th>Rede neural</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0.957307</td>\n","      <td>0.960624</td>\n","      <td>0.962503</td>\n","      <td>0.965106</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>0.956817</td>\n","      <td>0.960800</td>\n","      <td>0.961201</td>\n","      <td>0.964851</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>0.957493</td>\n","      <td>0.961240</td>\n","      <td>0.961465</td>\n","      <td>0.965106</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>0.957307</td>\n","      <td>0.961534</td>\n","      <td>0.961465</td>\n","      <td>0.965928</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5352bb32-d96b-4cf5-b646-fe1749e4da35')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 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SESS?ES   \n","103133  ANTIEMETICOS E ANTINAUSEANTES   \n","103134                TAXA DE SERVICO   \n","103135                        Protese   \n","\n","                                            DS_TIPO_GUIA  \\\n","0                            Guia de solicitac?o SP/SADT   \n","1                            Guia de solicitac?o SP/SADT   \n","2                            Guia de solicitac?o SP/SADT   \n","3                            Guia de solicitac?o SP/SADT   \n","4                            Guia de solicitac?o SP/SADT   \n","...                                                  ...   \n","103131  Guia de solicitac?o de prorrogac?o de internac?o   \n","103132  Guia de solicitac?o de prorrogac?o de internac?o   \n","103133                       Guia de solicitac?o SP/SADT   \n","103134                    Guia de solicitac?o internac?o   \n","103135  Guia de solicitac?o de prorrogac?o de internac?o   \n","\n","                                   DS_GRUPO DS_INDICACAO_ACIDENTE  \\\n","0       PROCED. DIAGNOSTICOS E TERAPEUTICOS          N?o acidente   \n","1       PROCED. DIAGNOSTICOS E TERAPEUTICOS          N?o acidente   \n","2       PROCED. DIAGNOSTICOS E TERAPEUTICOS          N?o acidente   \n","3       PROCED. DIAGNOSTICOS E TERAPEUTICOS          N?o acidente   \n","4       PROCED. DIAGNOSTICOS E TERAPEUTICOS                   NaN   \n","...                                     ...                   ...   \n","103131                              DIARIAS                   NaN   \n","103132                     PROCED. CLINICOS                Outros   \n","103133                           Brasindice          N?o acidente   \n","103134                                TAXAS          N?o acidente   \n","103135                               Simpro                   NaN   \n","\n","                                          DS_CBO  NR_SEQ_REQUISICAO  \n","0                          Medico oftalmologista             640397  \n","1       Medico endocrinologista e metabologista              897240  \n","2                          Medico oftalmologista             636323  \n","3               Medico ginecologista e obstetra             1425891  \n","4                         Medico reumatologista             1300316  \n","...                                          ...                ...  \n","103131                     Medico cardiologista             1002337  \n","103132                            Medico clinico            1229314  \n","103133     Medico ortopedista e traumatologista             1502406  \n","103134                    Medico infectologista             1034981  \n","103135                            Medico clinico             774495  \n","\n","[103136 rows x 11 columns]"],"text/html":["\n","  <div id=\"df-29c8da3d-3b4e-4ca1-bc5f-f340b891a9c3\">\n","    <div class=\"colab-df-container\">\n","      <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>DT_NASCIMENTO</th>\n","      <th>DS_TIPO_PREST_SOLICITANTE</th>\n","      <th>DS_CARATER_ATENDIMENTO</th>\n","      <th>CD_CID</th>\n","      <th>DS_SUBGRUPO</th>\n","      <th>DS_CLASSE</th>\n","      <th>DS_TIPO_GUIA</th>\n","      <th>DS_GRUPO</th>\n","      <th>DS_INDICACAO_ACIDENTE</th>\n","      <th>DS_CBO</th>\n","      <th>NR_SEQ_REQUISICAO</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2451529.0</td>\n","      <td>CLINICA</td>\n","      <td>Eletiva</td>\n","      <td>H40</td>\n","      <td>EXAMES ESPECIFICOS</td>\n","      <td>PROCEDIMENTOS</td>\n","      <td>Guia de solicitac?o SP/SADT</td>\n","      <td>PROCED. 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":222}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bkazsf6tgtKW","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650064583088,"user_tz":180,"elapsed":4307,"user":{"displayName":"Wendel Marques","userId":"04292042536301413808"}},"outputId":"62053b5f-66af-4ef9-ab9b-a7fcc9090400"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["103136"]},"metadata":{},"execution_count":221}],"source":["import pandas as pd \n","import numpy as np\n","import pickle\n","from sklearn.metrics import accuracy_score\n","from sklearn.metrics import precision_score, recall_score, f1_score\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# Balancemanto\n","from imblearn.under_sampling import RandomUnderSampler\n","# Escaonamento dos dados\n","from sklearn.preprocessing import StandardScaler\n","# Divisao Train e Test\n","from sklearn.model_selection import train_test_split\n","# Redução da Dimensionalidade\n","from sklearn.decomposition import PCA\n","\n","#RandomForest\n","from sklearn.ensemble import RandomForestClassifier\n","\n","################### PRÉ-PROCESSAMENTO ###################\n","#########################################################\n","\n","df_test = pd.read_csv('/content/drive/MyDrive/Trabalho Mineração de Dados [UFG]/0. base de dados/classificacao_binaria_test.csv', sep=';')\n","df_test = df_test[['DT_NASCIMENTO', 'DS_TIPO_PREST_SOLICITANTE', 'DS_CARATER_ATENDIMENTO', 'CD_CID', 'DS_SUBGRUPO', 'DS_CLASSE', 'DS_TIPO_GUIA', 'DS_GRUPO', 'DS_INDICACAO_ACIDENTE', \n","         'DS_CBO','NR_SEQ_REQUISICAO']]\n","\n","df_test['indices'] = df_test.index\n","indices = df_test['indices'] \n","######################## ENCODER ########################\n","#########################################################\n","import category_encoders as ce\n","encoder = ce.TargetEncoder(['DT_NASCIMENTO', 'DS_TIPO_PREST_SOLICITANTE', 'DS_CARATER_ATENDIMENTO', 'CD_CID', 'DS_SUBGRUPO', 'DS_CLASSE', 'DS_TIPO_GUIA', 'DS_GRUPO', 'DS_INDICACAO_ACIDENTE', \n","         'DS_CBO','NR_SEQ_REQUISICAO', 'DS_STATUS_ITEM'])\n","\n","\n","X_data = df.iloc[:, 0:11].values\n","y_data= df.iloc[:, 11].values\n","encoder.fit(X_data, y_data)\n","X_data = encoder.transform(df_test.iloc[:, 0:11].values)\n","\n","##################### PRÉ-TREINO ########################\n","#########################################################\n"," \n","# Escalonamento dos dados\n","from sklearn.preprocessing import StandardScaler\n","scaler_data = StandardScaler()\n","X_data = scaler_data.fit_transform(X_data)\n","\n","# # Redução da Dimensionalidade\n","# pca = PCA(n_components=6)\n","# X_data = pca.fit_transform(X_data)\n","\n","##################### PREVISOES #########################\n","#########################################################\n","\n","previsoes = random_forest.predict(X_data)\n","\n","####################### PÓS-TREINO ##########################\n","#############################################################\n","# Salva Model\n","# arvore = pickle.load(open('arvore_finalizado.sav', 'rb'))\n","\n","# Salva Previsões\n","scores = {'Id':indices.values,\n","          'Predicted': previsoes}\n","\n","previsoes_df = pd.DataFrame(scores)\n","previsoes_df['Id'] = previsoes_df['Id'] + 1\n","previsoes_df.to_csv(\"Previsoes_Auditorias_Planos_Saúde.csv\", index = False)\n","\n","len(X_data)"]},{"cell_type":"markdown","source":["#Pipeline para construção do modelo final\n","Esta seção descreve a melhor configuração obtida das dezenas de testes realizados."],"metadata":{"id":"3afA33sey0Dl"}},{"cell_type":"markdown","source":["###**O modelo foi construido a partir das seguintes etapas:**\n","* Atributos utilizados:`['DT_NASCIMENTO', 'DS_TIPO_PREST_SOLICITANTE', 'DS_CARATER_ATENDIMENTO','CD_CID', 'DS_SUBGRUPO', 'DS_CLASSE', 'DS_TIPO_GUIA', 'DS_GRUPO',  'DS_INDICACAO_ACIDENTE', 'DS_CBO','NR_SEQ_REQUISICAO', 'DS_STATUS_ITEM']` \n","* Pré-processamento:\n","  * Feauture Selection;\n","  * Remoção de valores faltantes;\n","  * Undersampling;\n","* Método de Encoder:\n","  * [Target Enconding](http://contrib.scikit-learn.org/category_encoders/targetencoder.html)\n","* Escalonamento dos dados\n","  * StandardScale\n","* Redução de dimensionalidade\n"," * Foi utilizado o PCA até certo momento, mas não foi utilizado no teste que gerou o melhor resultado. \n","* Divisão dos dados para treino\n"," * 70% para treino e 30% para teste\n","* Algoritmo de classificação:\n","  * Random Florest\n","  * Tuning dos parâmetros com GridSearch gerou os seguintes parâmetros:\n","    * n_estimators=150, criterion = 'entropy', min_samples_split = 10, min_samples_leaf= 10\n","\n","\n","</br>\n","\n","\n","Submissões disponíveis em:\n","\n","* https://drive.google.com/drive/u/1/folders/1flIBanwQ_SUOzslckTblXki1P5rsJGuh\n"],"metadata":{"id":"P6ioVyJty8-s"}},{"cell_type":"markdown","source":["# Alguns artigos de internet consultados"],"metadata":{"id":"_OZS-U0uRFZE"}},{"cell_type":"markdown","metadata":{"id":"WWAFprRfGDR1"},"source":["* [A Complete Guide to Categorical Data Encoding](https://analyticsindiamag.com/a-complete-guide-to-categorical-data-encoding/#:~:text=Encoding%20categorical%20data%20is%20a,provided%20to%20the%20different%20models.&text=In%20the%20field%20of%20data,preparation%20is%20a%20mandatory%20task.)\n","\n","* [Categorical Variable Encoding Techniques](https://medium.com/analytics-vidhya/categorical-variable-encoding-techniques-17e607fe42f9#:~:text=like%20Logistic%20Regression.-,Count%20or%20frequency%20encoding,-First%20step%20is)\n","\n","* [Métodos de Seleção de Atributos para Mineração de Dados](https://www.ic.unicamp.br/~wainer/cursos/2s2008/ia/Palestra_IC_Selecao_atributos_010908.pdf)\n","\n","* [Overview ofFeature Selection Techniques](https://machinelearningmastery.com/wp-content/uploads/2019/11/Overview-of-Feature-Selection-Techniques3.png)\n","\n","* [How to Develop an Encoder-Decoder Model for Sequence-to-Sequence Prediction in Keras](https://machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/)\n","\n","* [How to Identify Overfitting Machine Learning Models in Scikit-Learn](https://machinelearningmastery.com/overfitting-machine-learning-models/)\n","\n","* [3 Techniques to Avoid Overfitting of Decision Trees](https://towardsdatascience.com/3-techniques-to-avoid-overfitting-of-decision-trees-1e7d3d985a09)\n","\n","* [Overfitting e métodos de regularização](https://forum.ailab.unb.br/t/overfitting-e-metodos-de-regularizacao/158)\n","\n","* [Does Random Forest overfit?](https://mljar.com/blog/random-forest-overfitting)\n","\n","* [Overfitting e Regularização – Parte 1](https://www.deeplearningbook.com.br/overfitting-e-regularizacao-parte-1/)\n","\n","* [How to Choose a Feature Selection Method For Machine Learning](https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/)\n","* [Categorical Variable Encoding Techniques](https://medium.com/analytics-vidhya/categorical-variable-encoding-techniques-17e607fe42f9#:~:text=like%20Logistic%20Regression.-,Count%20or%20frequency%20encoding,-First%20step%20is)\n","* [Why are precision, recall and F1 score equal when using micro averaging in a multi-class problem?\n","](https://simonhessner.de/why-are-precision-recall-and-f1-score-equal-when-using-micro-averaging-in-a-multi-class-problem/)\n","* [ALGORITMO KNN PARA CLASSIFICAÇÃO](https://inferir.com.br/artigos/algoritimo-knn-para-classificacao/)\n","\n","* [PRatioEncoder - Documentação](https://feature-engine.readthedocs.io/en/1.0.x/encoding/PRatioEncoder.html)\n","\n","* [Which Encoder Is The Best For Random Forest Accuracy? Float Or Ordinal?](https://towardsdatascience.com/which-encoder-is-the-best-for-random-forest-accuracy-float-or-ordinal-ea7ba7ee697d)\n","\n","* [Random Forest Encoder](https://towardsdatascience.com/random-forest-encoder-e7c8b5b9278e)\n","\n","* [feature_engine Documentation](https://readthedocs.org/projects/feature-engine/downloads/pdf/latest/)\n","\n","* [What measure of training error to report for Random Forests?](https://stats.stackexchange.com/questions/162353/what-measure-of-training-error-to-report-for-random-forests)\n","\n","* [Random forest is overfitting](https://stats.stackexchange.com/questions/45731/random-forest-is-overfitting)\n","\n","* [Understanding Random Forest](https://www.analyticsvidhya.com/blog/2021/06/understanding-random-forest/#:~:text=Random%20forest%20is%20a%20Supervised,average%20in%20case%20of%20regression.)\n","\n","* [Understanding Random Forest](https://towardsdatascience.com/understanding-random-forest-58381e0602d2)\n","\n","* [O que é bootstrapping?](https://support.minitab.com/pt-br/minitab/20/help-and-how-to/probability-distributions-random-data-and-resampling-analyses/supporting-topics/resampling-analyses/what-is-bootstrapping/)\n","\n","* [Engenharia de Features: Transformando dados categóricos em dados numéricos](https://medium.com/data-hackers/engenharia-de-features-transformando-dados-categ%C3%B3ricos-em-dados-num%C3%A9ricos-e5d3991df715)\n","\n","* [Random Forest: como funciona um dos algoritmos mais populares de ML](https://medium.com/cinthiabpessanha/random-forest-como-funciona-um-dos-algoritmos-mais-populares-de-ml-cc1b8a58b3b4)\n","\n","* [Machine Learning: Conceitos e Modelos — Parte I: Aprendizado Supervisionado](https://tatianaesc.medium.com/machine-learning-conceitos-e-modelos-f0373bf4f445#:~:text=O%20Na%C3%AFve%20Bayes%20(Bayes%20Ing%C3%AAnuo,n%C3%BAmero%20de%20atributos%20(caracter%C3%ADsticas).)\n","\n","* [Como selecionar atributos para resolver a maldição da dimensionalidade](https://medium.com/@fabiolenine/como-selecionar-atributos-para-resolver-a-maldi%C3%A7%C3%A3o-da-dimensionalidade-5c810bc8449f)\n","\n","* [RareLabelEncoder - API Reference](https://feature-engine.readthedocs.io/en/1.0.x/encoding/RareLabelEncoder.html#feature_engine.encoding.RareLabelEncoder)\n","\n"]}]}