predict-heart-disease.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning - Predição de Doenças Cardíacas.\n",
    "\n",
    "        - Análise Exploratória de Dados;\n",
    "        - dataset 'heart.csv';\n",
    "        - Correlações, Remoção de outilers, duplicates, [...];\n",
    "        - Aplicação de Diferentes Algoritmos de Aprendizagem de Máquina;\n",
    "        - Observação dos Resultados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prelúdio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Manipulação e análise de dados.\n",
    "import pandas as pd\n",
    "\n",
    "# Processamento de grandes, multi-dimensionais arranjos e matrizes;\n",
    "# Coleção de funções matemáticas de alto nível.\n",
    "import numpy as np\n",
    "\n",
    "# Criação de gráficos e visualizações de dados em geral.\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Visualização de dados Python baseada em matplotlib;\n",
    "# Interface de alto nível para desenhar gráficos estatísticos atraentes e informativos.\n",
    "import seaborn as sns\n",
    "\n",
    "# Algoritmos fundamentais para computação científica em Python.\n",
    "from scipy import stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividir matrizes ou matrizes em subconjuntos aleatórios de treinamento e teste.\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Pontuação de classificação de precisão;\n",
    "# Relatório de texto mostrando as principais métricas de classificação;\n",
    "# Calcular a matriz de confusão para avaliar a precisão de uma classificação.\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "# Algoritmos de Machine Learning a serem implementados.\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# Padronização das features removendo a média e dimensionando para a variação da unidade.\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease Dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Consumindo o Dataset.\n",
    "ds = pd.read_csv('/home/jbrun0r/Documentos/scripts/datasets/heart.csv')\n",
    "ds_copy = pd.read_csv('/home/jbrun0r/Documentos/scripts/datasets/heart.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### age: Idade do paciente\n",
    "##### sex: Sexo do paciente\n",
    "    0: F;\n",
    "    1: M.\n",
    "##### cp : tipo de dor no peito no peito\n",
    "    1: angina típica;\n",
    "    2: angina atípica;\n",
    "    3: dor não anginosa;\n",
    "    4: assintomático.\n",
    "##### trtbps : pressão arterial em repouso (em mm Hg)\n",
    "##### chol : colestoral em mg/dl obtido via sensor de IMC(Colesterol sérico em mg/dl.)\n",
    "##### fbs : (glicemia em jejum > 120 mg/dl) (Açúcar no sangue em jejum > 120 mg/dl = 0 | < 120mg/dl = 1.)\n",
    "    1 = verdadeiro;\n",
    "    0 = falso.\n",
    "##### rest_ecg : Resultados eletrocardiográficos em repouso\n",
    "    0: normal;\n",
    "    1: com anormalidade da onda ST-T (inversões da onda T e/ou elevação ou depressão do segmento ST > 0,05 mV);\n",
    "    2: mostrando provável ou definitiva hipertrofia ventricular esquerda pelos critérios de Estes.\n",
    "##### thalach : Frequência cardíaca máxima atingida\n",
    "    0 = menos chance de ataque cardíaco\n",
    "    1 = mais chance de ataque cardíaco\n",
    "##### exng: Angina induzida pelo exercício\n",
    "    1 = sim\n",
    "    0 = não\n",
    "##### Oldpeak: Depressão do segmento ST induzida pelo exercício em relação ao repouso\n",
    "##### slp: Inclinação do segmento ST de pico do exercício.\n",
    "##### caa: número de vasos principais\n",
    "    0-3\n",
    "##### thal: Talassemia: \n",
    "    1 = normal;\n",
    "    2 = problema corrigido; \n",
    "    3 = problema reversível.\n",
    "##### output:\n",
    "    0 = não possui doença cardíaca;\n",
    "    1 = possui doença cardíaca.\n",
    "##### Importante: Angina é o nome dado para a dor no peito causada pela diminuição do fluxo de sangue no coração, o que é chamado de isquemia. Ela não é uma doença, mas está relacionada a outras condições que provocam obstrução nas artérias coronárias, responsáveis por levar sangue ao coração."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
       "      <th>thall</th>\n",
       "      <th>output</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
       "0   63    1   3     145   233    1        0       150     0      2.3    0   \n",
       "1   37    1   2     130   250    0        1       187     0      3.5    0   \n",
       "2   41    0   1     130   204    0        0       172     0      1.4    2   \n",
       "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
       "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
       "\n",
       "   caa  thall  output  \n",
       "0    0      1       1  \n",
       "1    0      2       1  \n",
       "2    0      2       1  \n",
       "3    0      2       1  \n",
       "4    0      2       1  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pré-Processamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trtbps      0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalachh    0\n",
       "exng        0\n",
       "oldpeak     0\n",
       "slp         0\n",
       "caa         0\n",
       "thall       0\n",
       "output      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.isnull().sum()\n",
    "#Abaixo podemos verificar se existem registros nulos em nossa base de dados, \n",
    "# e para a nossa sorte nenhum foi encontrado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          29.0\n",
       "sex           0.0\n",
       "cp            0.0\n",
       "trtbps       94.0\n",
       "chol        126.0\n",
       "fbs           0.0\n",
       "restecg       0.0\n",
       "thalachh     71.0\n",
       "exng          0.0\n",
       "oldpeak       0.0\n",
       "slp           0.0\n",
       "caa           0.0\n",
       "thall         0.0\n",
       "output        0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.min()\n",
    "# Podemos ver que também não possuímos valores negativos nos dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_copy.duplicated().sum()\n",
    "# Podemos ver que há 'uma' linha duplicada e tiraremos para não causar overfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removendo a duplicação\n",
    "ds = ds.drop_duplicates()\n",
    "ds_copy = ds_copy.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>302.0</td>\n",
       "      <td>54.420530</td>\n",
       "      <td>9.047970</td>\n",
       "      <td>29.0</td>\n",
       "      <td>48.00</td>\n",
       "      <td>55.5</td>\n",
       "      <td>61.00</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>302.0</td>\n",
       "      <td>0.682119</td>\n",
       "      <td>0.466426</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>302.0</td>\n",
       "      <td>0.963576</td>\n",
       "      <td>1.032044</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trtbps</th>\n",
       "      <td>302.0</td>\n",
       "      <td>131.602649</td>\n",
       "      <td>17.563394</td>\n",
       "      <td>94.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>130.0</td>\n",
       "      <td>140.00</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>302.0</td>\n",
       "      <td>246.500000</td>\n",
       "      <td>51.753489</td>\n",
       "      <td>126.0</td>\n",
       "      <td>211.00</td>\n",
       "      <td>240.5</td>\n",
       "      <td>274.75</td>\n",
       "      <td>564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>302.0</td>\n",
       "      <td>0.149007</td>\n",
       "      <td>0.356686</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>302.0</td>\n",
       "      <td>0.526490</td>\n",
       "      <td>0.526027</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalachh</th>\n",
       "      <td>302.0</td>\n",
       "      <td>149.569536</td>\n",
       "      <td>22.903527</td>\n",
       "      <td>71.0</td>\n",
       "      <td>133.25</td>\n",
       "      <td>152.5</td>\n",
       "      <td>166.00</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exng</th>\n",
       "      <td>302.0</td>\n",
       "      <td>0.327815</td>\n",
       "      <td>0.470196</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>302.0</td>\n",
       "      <td>1.043046</td>\n",
       "      <td>1.161452</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.60</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slp</th>\n",
       "      <td>302.0</td>\n",
       "      <td>1.397351</td>\n",
       "      <td>0.616274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caa</th>\n",
       "      <td>302.0</td>\n",
       "      <td>0.718543</td>\n",
       "      <td>1.006748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thall</th>\n",
       "      <td>302.0</td>\n",
       "      <td>2.314570</td>\n",
       "      <td>0.613026</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>output</th>\n",
       "      <td>302.0</td>\n",
       "      <td>0.543046</td>\n",
       "      <td>0.498970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count        mean        std    min     25%    50%     75%    max\n",
       "age       302.0   54.420530   9.047970   29.0   48.00   55.5   61.00   77.0\n",
       "sex       302.0    0.682119   0.466426    0.0    0.00    1.0    1.00    1.0\n",
       "cp        302.0    0.963576   1.032044    0.0    0.00    1.0    2.00    3.0\n",
       "trtbps    302.0  131.602649  17.563394   94.0  120.00  130.0  140.00  200.0\n",
       "chol      302.0  246.500000  51.753489  126.0  211.00  240.5  274.75  564.0\n",
       "fbs       302.0    0.149007   0.356686    0.0    0.00    0.0    0.00    1.0\n",
       "restecg   302.0    0.526490   0.526027    0.0    0.00    1.0    1.00    2.0\n",
       "thalachh  302.0  149.569536  22.903527   71.0  133.25  152.5  166.00  202.0\n",
       "exng      302.0    0.327815   0.470196    0.0    0.00    0.0    1.00    1.0\n",
       "oldpeak   302.0    1.043046   1.161452    0.0    0.00    0.8    1.60    6.2\n",
       "slp       302.0    1.397351   0.616274    0.0    1.00    1.0    2.00    2.0\n",
       "caa       302.0    0.718543   1.006748    0.0    0.00    0.0    1.00    4.0\n",
       "thall     302.0    2.314570   0.613026    0.0    2.00    2.0    3.00    3.0\n",
       "output    302.0    0.543046   0.498970    0.0    0.00    1.0    1.00    1.0"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### count: Total de registros existentes.\n",
    "###### mean: valor da média.\n",
    "###### std: desvio padrão.\n",
    "###### min e max: valores mínimos e máximos.\n",
    "###### 25%: ou primeiro quartil, que corresponde aos primeiros 25% dos valores de nossos registros.\n",
    "###### 50%: ou mediana, que corresponde aos primeiros 50% dos valores de nossos registros, essa é a nossa medida central dos dados. Metade dos nossos valores são menores que a mediana e a outra metade é maior a este valor. A mediana é uma opção muito interessante quando comparado a média, pois ela não sofre com valores discrepantes.\n",
    "###### 75%: ou terceiro quartil, que corresponde aos primeiros 75% dos valores de nossos registros.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
       "      <th>thall</th>\n",
       "      <th>output</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.094962</td>\n",
       "      <td>-0.063107</td>\n",
       "      <td>0.283121</td>\n",
       "      <td>0.207216</td>\n",
       "      <td>0.119492</td>\n",
       "      <td>-0.111590</td>\n",
       "      <td>-0.395235</td>\n",
       "      <td>0.093216</td>\n",
       "      <td>0.206040</td>\n",
       "      <td>-0.164124</td>\n",
       "      <td>0.302261</td>\n",
       "      <td>0.065317</td>\n",
       "      <td>-0.221476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.094962</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051740</td>\n",
       "      <td>-0.057647</td>\n",
       "      <td>-0.195571</td>\n",
       "      <td>0.046022</td>\n",
       "      <td>-0.060351</td>\n",
       "      <td>-0.046439</td>\n",
       "      <td>0.143460</td>\n",
       "      <td>0.098322</td>\n",
       "      <td>-0.032990</td>\n",
       "      <td>0.113060</td>\n",
       "      <td>0.211452</td>\n",
       "      <td>-0.283609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.063107</td>\n",
       "      <td>-0.051740</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046486</td>\n",
       "      <td>-0.072682</td>\n",
       "      <td>0.096018</td>\n",
       "      <td>0.041561</td>\n",
       "      <td>0.293367</td>\n",
       "      <td>-0.392937</td>\n",
       "      <td>-0.146692</td>\n",
       "      <td>0.116854</td>\n",
       "      <td>-0.195356</td>\n",
       "      <td>-0.160370</td>\n",
       "      <td>0.432080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trtbps</th>\n",
       "      <td>0.283121</td>\n",
       "      <td>-0.057647</td>\n",
       "      <td>0.046486</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.125256</td>\n",
       "      <td>0.178125</td>\n",
       "      <td>-0.115367</td>\n",
       "      <td>-0.048023</td>\n",
       "      <td>0.068526</td>\n",
       "      <td>0.194600</td>\n",
       "      <td>-0.122873</td>\n",
       "      <td>0.099248</td>\n",
       "      <td>0.062870</td>\n",
       "      <td>-0.146269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.207216</td>\n",
       "      <td>-0.195571</td>\n",
       "      <td>-0.072682</td>\n",
       "      <td>0.125256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011428</td>\n",
       "      <td>-0.147602</td>\n",
       "      <td>-0.005308</td>\n",
       "      <td>0.064099</td>\n",
       "      <td>0.050086</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.086878</td>\n",
       "      <td>0.096810</td>\n",
       "      <td>-0.081437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.119492</td>\n",
       "      <td>0.046022</td>\n",
       "      <td>0.096018</td>\n",
       "      <td>0.178125</td>\n",
       "      <td>0.011428</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.083081</td>\n",
       "      <td>-0.007169</td>\n",
       "      <td>0.024729</td>\n",
       "      <td>0.004514</td>\n",
       "      <td>-0.058654</td>\n",
       "      <td>0.144935</td>\n",
       "      <td>-0.032752</td>\n",
       "      <td>-0.026826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.111590</td>\n",
       "      <td>-0.060351</td>\n",
       "      <td>0.041561</td>\n",
       "      <td>-0.115367</td>\n",
       "      <td>-0.147602</td>\n",
       "      <td>-0.083081</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.041210</td>\n",
       "      <td>-0.068807</td>\n",
       "      <td>-0.056251</td>\n",
       "      <td>0.090402</td>\n",
       "      <td>-0.083112</td>\n",
       "      <td>-0.010473</td>\n",
       "      <td>0.134874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalachh</th>\n",
       "      <td>-0.395235</td>\n",
       "      <td>-0.046439</td>\n",
       "      <td>0.293367</td>\n",
       "      <td>-0.048023</td>\n",
       "      <td>-0.005308</td>\n",
       "      <td>-0.007169</td>\n",
       "      <td>0.041210</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.377411</td>\n",
       "      <td>-0.342201</td>\n",
       "      <td>0.384754</td>\n",
       "      <td>-0.228311</td>\n",
       "      <td>-0.094910</td>\n",
       "      <td>0.419955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exng</th>\n",
       "      <td>0.093216</td>\n",
       "      <td>0.143460</td>\n",
       "      <td>-0.392937</td>\n",
       "      <td>0.068526</td>\n",
       "      <td>0.064099</td>\n",
       "      <td>0.024729</td>\n",
       "      <td>-0.068807</td>\n",
       "      <td>-0.377411</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.286766</td>\n",
       "      <td>-0.256106</td>\n",
       "      <td>0.125377</td>\n",
       "      <td>0.205826</td>\n",
       "      <td>-0.435601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.206040</td>\n",
       "      <td>0.098322</td>\n",
       "      <td>-0.146692</td>\n",
       "      <td>0.194600</td>\n",
       "      <td>0.050086</td>\n",
       "      <td>0.004514</td>\n",
       "      <td>-0.056251</td>\n",
       "      <td>-0.342201</td>\n",
       "      <td>0.286766</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.576314</td>\n",
       "      <td>0.236560</td>\n",
       "      <td>0.209090</td>\n",
       "      <td>-0.429146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slp</th>\n",
       "      <td>-0.164124</td>\n",
       "      <td>-0.032990</td>\n",
       "      <td>0.116854</td>\n",
       "      <td>-0.122873</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>-0.058654</td>\n",
       "      <td>0.090402</td>\n",
       "      <td>0.384754</td>\n",
       "      <td>-0.256106</td>\n",
       "      <td>-0.576314</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.092236</td>\n",
       "      <td>-0.103314</td>\n",
       "      <td>0.343940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caa</th>\n",
       "      <td>0.302261</td>\n",
       "      <td>0.113060</td>\n",
       "      <td>-0.195356</td>\n",
       "      <td>0.099248</td>\n",
       "      <td>0.086878</td>\n",
       "      <td>0.144935</td>\n",
       "      <td>-0.083112</td>\n",
       "      <td>-0.228311</td>\n",
       "      <td>0.125377</td>\n",
       "      <td>0.236560</td>\n",
       "      <td>-0.092236</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.160085</td>\n",
       "      <td>-0.408992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thall</th>\n",
       "      <td>0.065317</td>\n",
       "      <td>0.211452</td>\n",
       "      <td>-0.160370</td>\n",
       "      <td>0.062870</td>\n",
       "      <td>0.096810</td>\n",
       "      <td>-0.032752</td>\n",
       "      <td>-0.010473</td>\n",
       "      <td>-0.094910</td>\n",
       "      <td>0.205826</td>\n",
       "      <td>0.209090</td>\n",
       "      <td>-0.103314</td>\n",
       "      <td>0.160085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.343101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>output</th>\n",
       "      <td>-0.221476</td>\n",
       "      <td>-0.283609</td>\n",
       "      <td>0.432080</td>\n",
       "      <td>-0.146269</td>\n",
       "      <td>-0.081437</td>\n",
       "      <td>-0.026826</td>\n",
       "      <td>0.134874</td>\n",
       "      <td>0.419955</td>\n",
       "      <td>-0.435601</td>\n",
       "      <td>-0.429146</td>\n",
       "      <td>0.343940</td>\n",
       "      <td>-0.408992</td>\n",
       "      <td>-0.343101</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp    trtbps      chol       fbs  \\\n",
       "age       1.000000 -0.094962 -0.063107  0.283121  0.207216  0.119492   \n",
       "sex      -0.094962  1.000000 -0.051740 -0.057647 -0.195571  0.046022   \n",
       "cp       -0.063107 -0.051740  1.000000  0.046486 -0.072682  0.096018   \n",
       "trtbps    0.283121 -0.057647  0.046486  1.000000  0.125256  0.178125   \n",
       "chol      0.207216 -0.195571 -0.072682  0.125256  1.000000  0.011428   \n",
       "fbs       0.119492  0.046022  0.096018  0.178125  0.011428  1.000000   \n",
       "restecg  -0.111590 -0.060351  0.041561 -0.115367 -0.147602 -0.083081   \n",
       "thalachh -0.395235 -0.046439  0.293367 -0.048023 -0.005308 -0.007169   \n",
       "exng      0.093216  0.143460 -0.392937  0.068526  0.064099  0.024729   \n",
       "oldpeak   0.206040  0.098322 -0.146692  0.194600  0.050086  0.004514   \n",
       "slp      -0.164124 -0.032990  0.116854 -0.122873  0.000417 -0.058654   \n",
       "caa       0.302261  0.113060 -0.195356  0.099248  0.086878  0.144935   \n",
       "thall     0.065317  0.211452 -0.160370  0.062870  0.096810 -0.032752   \n",
       "output   -0.221476 -0.283609  0.432080 -0.146269 -0.081437 -0.026826   \n",
       "\n",
       "           restecg  thalachh      exng   oldpeak       slp       caa  \\\n",
       "age      -0.111590 -0.395235  0.093216  0.206040 -0.164124  0.302261   \n",
       "sex      -0.060351 -0.046439  0.143460  0.098322 -0.032990  0.113060   \n",
       "cp        0.041561  0.293367 -0.392937 -0.146692  0.116854 -0.195356   \n",
       "trtbps   -0.115367 -0.048023  0.068526  0.194600 -0.122873  0.099248   \n",
       "chol     -0.147602 -0.005308  0.064099  0.050086  0.000417  0.086878   \n",
       "fbs      -0.083081 -0.007169  0.024729  0.004514 -0.058654  0.144935   \n",
       "restecg   1.000000  0.041210 -0.068807 -0.056251  0.090402 -0.083112   \n",
       "thalachh  0.041210  1.000000 -0.377411 -0.342201  0.384754 -0.228311   \n",
       "exng     -0.068807 -0.377411  1.000000  0.286766 -0.256106  0.125377   \n",
       "oldpeak  -0.056251 -0.342201  0.286766  1.000000 -0.576314  0.236560   \n",
       "slp       0.090402  0.384754 -0.256106 -0.576314  1.000000 -0.092236   \n",
       "caa      -0.083112 -0.228311  0.125377  0.236560 -0.092236  1.000000   \n",
       "thall    -0.010473 -0.094910  0.205826  0.209090 -0.103314  0.160085   \n",
       "output    0.134874  0.419955 -0.435601 -0.429146  0.343940 -0.408992   \n",
       "\n",
       "             thall    output  \n",
       "age       0.065317 -0.221476  \n",
       "sex       0.211452 -0.283609  \n",
       "cp       -0.160370  0.432080  \n",
       "trtbps    0.062870 -0.146269  \n",
       "chol      0.096810 -0.081437  \n",
       "fbs      -0.032752 -0.026826  \n",
       "restecg  -0.010473  0.134874  \n",
       "thalachh -0.094910  0.419955  \n",
       "exng      0.205826 -0.435601  \n",
       "oldpeak   0.209090 -0.429146  \n",
       "slp      -0.103314  0.343940  \n",
       "caa       0.160085 -0.408992  \n",
       "thall     1.000000 -0.343101  \n",
       "output   -0.343101  1.000000  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Observando a correlação das features;\n",
    "# \"Correlação não implica em Causalidade.\".\n",
    "plt.figure(figsize=(16,8))\n",
    "sns.heatmap(ds.corr(), annot = True, cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "categoricas = ['sex', 'cp', 'fbs','restecg','exng', 'slp','caa', 'thall']\n",
    "numericas = ['age', 'trtbps', 'chol', 'thalachh', 'oldpeak']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     output 0: \n",
      "            sex 0 = 24 -> 17.39%\n",
      "            sex 1 = 114 -> 82.61%\n",
      "\n",
      "     output 1: \n",
      "            sex 0 = 72 -> 43.9%\n",
      "            sex 1 = 92 -> 56.1%\n",
      "\n",
      "     GERAL: \n",
      "            sex 0 e output 0 = 24 -> 7.947%\n",
      "            sex 0 e output 1 = 72 -> 23.84%\n",
      "            sex 1 e output 0 = 114 -> 37.75%\n",
      "            sex 1 e output 1 = 92 -> 30.46%\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     output 0: \n",
      "            cp 0 = 104 -> 75.36%\n",
      "            cp 1 = 9 -> 6.522%\n",
      "            cp 2 = 18 -> 13.04%\n",
      "            cp 3 = 7 -> 5.072%\n",
      "\n",
      "     output 1: \n",
      "            cp 0 = 39 -> 23.78%\n",
      "            cp 1 = 41 -> 25.0%\n",
      "            cp 2 = 68 -> 41.46%\n",
      "            cp 3 = 16 -> 9.756%\n",
      "\n",
      "     GERAL: \n",
      "            cp 0 e output 0 = 104 -> 34.44%\n",
      "            cp 0 e output 1 = 39 -> 12.91%\n",
      "            cp 1 e output 0 = 9 -> 2.98%\n",
      "            cp 1 e output 1 = 41 -> 13.58%\n",
      "            cp 2 e output 0 = 18 -> 5.96%\n",
      "            cp 2 e output 1 = 68 -> 22.52%\n",
      "            cp 3 e output 0 = 7 -> 2.318%\n",
      "            cp 3 e output 1 = 16 -> 5.298%\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     output 0: \n",
      "            fbs 0 = 116 -> 84.06%\n",
      "            fbs 1 = 22 -> 15.94%\n",
      "\n",
      "     output 1: \n",
      "            fbs 0 = 141 -> 85.98%\n",
      "            fbs 1 = 23 -> 14.02%\n",
      "\n",
      "     GERAL: \n",
      "            fbs 0 e output 0 = 116 -> 38.41%\n",
      "            fbs 0 e output 1 = 141 -> 46.69%\n",
      "            fbs 1 e output 0 = 22 -> 7.285%\n",
      "            fbs 1 e output 1 = 23 -> 7.616%\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     output 0: \n",
      "            restecg 0 = 79 -> 57.25%\n",
      "            restecg 1 = 56 -> 40.58%\n",
      "            restecg 2 = 3 -> 2.174%\n",
      "\n",
      "     output 1: \n",
      "            restecg 0 = 68 -> 41.46%\n",
      "            restecg 1 = 95 -> 57.93%\n",
      "            restecg 2 = 1 -> 0.6098%\n",
      "\n",
      "     GERAL: \n",
      "            restecg 0 e output 0 = 79 -> 26.16%\n",
      "            restecg 0 e output 1 = 68 -> 22.52%\n",
      "            restecg 1 e output 0 = 56 -> 18.54%\n",
      "            restecg 1 e output 1 = 95 -> 31.46%\n",
      "            restecg 2 e output 0 = 3 -> 0.9934%\n",
      "            restecg 2 e output 1 = 1 -> 0.3311%\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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c09MTqPxBeHl5mZxGREREREREmrrc3FwiIiKq+uiZNPlCfvIwdS8vLxVyERERERERqTdnO21ag7qJiIiIiIiImECFXERERERERMQEKuQiIiIiIiIiJlAhFxERERERETGBCrmIiIiIiIiICVTIRUREREREREygQi4iIiIiIiJiAhVyEREREREREROokIuIiIiIiIiYQIVcRERERERExAQOZgcQEREREfMkJCSQnp5udox6ExAQQGRkpNkxREQAFXIRERGRZishIYEOHTtSVFhodpR64+rmxt64OJVyEWkQVMhFREREmqn09HSKCguZ8OiLBEe2NjvOBZeacIj3n59Jenq6CrmINAgq5CIiIiLNXHBka8LbdjY7hohIs6NB3URERERERERMoEIuIiIiIiIiYgIVchERERERERETqJCLiIiIiIiImECFXERERERERMQEKuQiIiIiIiIiJlAhFxERERERETGBCrmIiIiIiIiICVTIRUREREREREygQi4iIiIiIiJiAhVyEREREREREROokIuIiIiIiIiYQIVcRERERERExAQq5CIiIiIiIiImUCEXERERERERMYEKuYiIiIiIiIgJVMhFRERERERETKBCLiIiIiIiImICFXIRERERERERE6iQi4iIiIiIiJhAhVxERERERETEBCrkIiIiIiIiIiZQIRcRERERERExgQq5iIiIiIiIiAlUyEVERERERERMoEIuIiIiIiIiYgIVchERERERERETqJCLiIiIiIiImECFXERERERERMQEKuQiIiIiIiIiJjC1kD/11FNYLJZqXx06dKiaX1xczJQpU/D398fDw4Nx48aRmppqYmIRERERERGRumH6HvLOnTuTnJxc9bV27dqqeQ8++CBLly5l8eLFrF69mqSkJMaOHWtiWhEREREREZG64WB6AAcHQkJCTpmek5PDW2+9xaJFixgyZAgACxcupGPHjmzYsIH+/fufdn0lJSWUlJRU3c7Nzb0wwUVERESkRgzDwGKxmB1DRKTBMb2QHzhwgNDQUFxcXIiJiWH27NlERkYSGxtLWVkZQ4cOrVq2Q4cOREZGsn79+jMW8tmzZ/P000/XV3wRERGRZqu4rIL0/BKyC8vIKy4nr6Ty38LSCsoq7JSV2ymrMKgwDKwWsFktOFit2KwW3JxseDg7VH65OODj6kiApzM+ro4q7yLSbJhayPv168fbb79N+/btSU5O5umnn2bgwIHs2rWLlJQUnJyc8PHxqXaf4OBgUlJSzrjOWbNmMWPGjKrbubm5REREXKinICIiItIslFfYSc4pJjG7iLS8EtLyS8grLq/x/e0G2CsMyioqAMgvKedEXskpyznaLAR4OBPs5UKEryvhvm44OZh+lqWIyAVhaiEfOXJk1ffdunWjX79+REVF8fHHH+Pq6lqrdTo7O+Ps7FxXEUVERESaJcMwSMkt5mhGIYlZRaTkFFNhGKcs5+XigK+7E14ujni6OODp4oC7kwOODlYcrRYcHaw4WC3Y7VBut1NhNyi3GxSWVpBfXE5+SeWe9cyCUtLzSymrMEjOKSY5p5htx7KxWiDEy4VIfzfaBHrg76H3eSLSdJh+yPpv+fj40K5dOw4ePMgVV1xBaWkp2dnZ1faSp6amnvaccxERERE5P+V2O8cyizicls/h9AIKSyuqzXd3thHu40aItwuBHs4EeDjh7Girs8e32w2yCktJyyshKaeYhMxCcorKSMopJimnmA2HMwnwcKJdsCftgz3xcnWss8cWETFDgyrk+fn5HDp0iFtuuYVevXrh6OjI8uXLGTduHAD79u0jISGBmJgYk5OKiIiINBUW0oot7ItL5cCJfErK7VVznGxWovzdiPRzI9zXFe8LfH631WrB38MZfw9nOrTwAiCnqIxjmYUcTi8gPqOA9PxS0vMzWHcog3BfV3pE+NAywB2rzjsXkUbI1EL+8MMPM3r0aKKiokhKSuLJJ5/EZrNx00034e3tzR133MGMGTPw8/PDy8uLadOmERMTc8YB3URERESkZhKzi1i0M4+we95kzQlHoPLKNO7ONloHeNAq0J1wXzdsVnOLrrerI95h3nQJ86a4rIKDJ/LZl5rH8ayiqi8vFwe6hfvQOdQLlzrcYy8icqGZWsiPHz/OTTfdREZGBoGBgVxyySVs2LCBwMBAAF5++WWsVivjxo2jpKSE4cOH8/rrr5sZWURERKTRqrAbrDmQxvsb4lmx9wR2Axy8g3GwGLQL8aZDiCfhvq4NdpRzF0cbXX4t57nFZew8nsOuxBxyi8tZezCdjUcy6BHhQ89IXxVzEWkULIZxmtE5mpDc3Fy8vb3JycnBy8vL7DgiIiIi9S6/pJyPNx9j4bojHMssqpreJciJlW8+y133TSeqfWcTE9ZeeYWdfal5bD2WTUZ+KVB5qH33CO9TivnxA7uZM2UssbGx9OzZ06zIItIM1LSHNqhzyEVERESk7qTkFPP2uqMs2hhP7q+XKPNyceC6XhHc3C+S3OP76fXQGmzW6eYGPQ8ONiudQ73p1MKLw+kFbDicQXp+KZuPZrH9WA79WvrRPcLH9EPvRUROR4VcREREpImJzyjg9ZWH+GzrccoqKg+GbBngzh2XtGRcz3BcnSr3Gm85bmbKumWxWGgd6EGrAHcOpRWw4UgGGfml/HQwnZ2JOQxsF4Bjkz4uVEQaIxVyERERkSbi4Il8Xl95kC+2J1Fhr2yffaP9uOvSVlzeIQhrM9hLbLFYaBPkQetAd/Yk57LuUAbZRWUs3Z5MkIsDDr6hZkcUEamiQi4iIiLSyCVkFDLnh318sT2Jk6MDDWofyLQhbegV5WduOJNYLBY6h3rTJsiDzUez2JaQzYliKy1ue40le/Pp1t2Og81qdkwRaeZUyEVEREQaqfT8El5bfoBFmxKqDk0f1imYqUPa0C3cx9xwDYSzg41L2gTQJdSLb7Ye4QTOvLsjj60ZP/P8uG50DvU2O6KINGMq5CIiIiKNTF5xGf/+6Qhv/nSYwtIKAC5tF8gjw9vTJUwF83R83Jy4JLCcNxb8k+gxD7ErMZer//kzUwa1ZtrlbXHU3nIRMYEKuYiIiEgjUVJewfsbEvjnyoNkFlRe4qt7uDePjuzAgNYBJqdr+CwWKNi1glf/9RyfHrHx9c5kXl1xkDUH0pl7Yw+i/N3NjigizYwKuYiIiEgDZ7cbfLE9kZe+38/xrMrriLcKcGfm8PaM6BKCxdL0B2urSz4uNuZN6MnIHUn85bOdbDuWzZVzf+KpqztzXa9w/TxFpN6okIuIiIg0YNuPZfPkl7vZdiwbgCBPZ6YPbccNvcM1KNl5uqpbKBdF+vLgR9vYdCSTmZ/sYPX+NJ4b1w0PZ71NFpELT79pRERERBqg9PwSXvx2Hx/HHsMwwN3Jxn2D23D7xS2rriMu5y/Mx5UP7urPgtWHePmH/Xy1I5m45FwWTOxF22BPs+OJSBOnQi4iIiLSgJRX2Hl3QzxzfthPXnE5AGMvCuPPIzsQ5OVicrqmyWa1MGVwG/q38mfK+1s4lFbANfMqR2Ef3V3XLReRC0eFXERERKSBWHconae+3M3+1HwAOod68fTVnekd3TyvJV7fekX58vX9l3D/h1v5+WAG0z7YypaELP5yZUeNwi4iF4QKuYiIiIjJkrKL+PvXcXy9MxkAXzdHZg7vwPg+EdisGmCsPvl7OPPf2/sx54d9zFt5iIU/H2V/ah6v39wLbzdHs+OJSBOjQi4iIiJikgq7wX/XH+Uf3+2joLQCqwUm9o9ixhXt8HFzMjtes2WzWpg5vAPdw3148KNt/HwwgzGv/8ybk3rTKtDD7Hgi0oTo2BsRERERE+xNyWXc/HU8vXQPBaUV9I7y5atpA3nmmi4q4w3EsM4hfHLvAMJ8XDmcXsC1835m7YF0s2OJSBOiQi4iIiJSj4rLKnjxu71c9epath3LxtPZgWev7cLHd8fQKdTL7HjyOx1bePH5lIvpGelDbnE5kxZu4oNNCWbHEpEmQoVcREREpJ5sOJzByLk/MW/lIcrtBsM7B/PDjMuY2D8Kq84Vb7ACPZ1ZdFd/xlwURoXdYNZnO3n5h/0YhmF2NBFp5HQOuYiIiMivEhISSE+v+0OS80vt/Hd7Lj8eKQLA18XKXT296B9uJenQHpLq/BFrJi4uzqRHbnxcHG3MuaE74b6uvLbiIHOXHyAlp5i/j+mCg0ZgF5FaUiEXERERobKMd+jYkaLCwjpdr2vrvviNmIqDR+Wly/K2fkPC6reZUlJQp49zPvLz882O0ChYLBYeGtaeEG8XHv98Fx/9coy0/BL+efNFuDnpbbWInDv95hAREREB0tPTKSosZMKjLxIc2fq811dmhx1ZNo4W2ADwcDDo5VdOwDWXwzWXn/f660LcptV8885ciouLzY7SqEzoF0WQpwvTPtjCir0nmPDmRt6e3FeXRRORc6ZCLiIiIvIbwZGtCW/b+bzWcTyrkJV7UskrLgegZ6QPMa38G9yhzakJh8yO0Ghd0SmY9+/sz+1vb2ZrQjY3/nsD797RlwAPZ7OjiUgj0rD+KoiIiIg0YuUVdlbvT+PTLYnkFZfj5eLAdT3DGdg2sMGVcTl/vaJ8+eju/gR4OBOXnMsNC9aTlF1kdiwRaUT0l0FERESkDqTmFrNoUwLbjmUD0CXUiwn9ogjzdTU3mFxQHUK8WHxPTNW1yq9fsJ4j6Q1nfAARadhUyEVERETOQ4XdYMPhDD765RhZhWW4O9m4unsol3cMxslBb7Wag5YB7iy+J4ZWAe4kZhdxw7/Wc/CEBsoTkbPTXwkRERGRWsopKmNx7DE2HsnEMKBdkAcT+kfRMsDd7GhSz0J9XPno7hg6hHiSllfCjW9s4OCJPLNjiUgDp0IuIiIiUgsHUvNYtDGB1NwSnB2sjOgcwsiuLXB1tJkdTUwS6OnMorv607GFF+n5laX8QKpKuYicmQq5iIiIyDkor7CzfG8qy3alUFphp4W3Czf3i6R9iKfZ0aQB8HN3YtGd/ejUwov0/FJu+vcG9quUi8gZ6LJnIiIiIjWUWVDKsl3JZOSXAtAn2pd+Lf2xWS0mJ5NzERcXd8Ef49G+rjy1upAj2aVc9/pa/jbIjwjv+r9OeUBAAJGRkfX+uCJSMyrkIiIiImdhGAZ7knNZtS+NcruBm5ONYZ2CifLXueKNSW5mGgATJ06sl8ezungQNP5ZckPaMOWTfaQu+jPl2cn18tgnubq5sTcuTqVcpIFSIRcRERH5A6XldlbsO8G+lMrDjiP93BjWKRh3Z72NamyK8nMBGHX3Y7Tv1qteHrOkAtacsJPr6U/b+97g0uAy3Otp00lNOMT7z88kPT1dhVykgdJfEhEREZEzSM8v4esdyWQXlWGxQEwrf3pH+WKx6BD1xsw/NIrwtp3r7fFCWpbzyZbjZBeWsT7Lnet6heOhD3REBA3qJiIiInJa+1Pz+GjzMbKLyvBwduC6nuH0ifZTGZdz5u7swNiLwvBycSCnqIwlWxIpLC03O5aINAAq5CIiIiK/YTdg7cF0vtmVQrndIMLPlZv7RRLq42p2NGnEPF0cGduzcs94ZmEpX2xLorTcbnYsETGZCrmIiIjIr6wunvyc5kBsfBYAvaJ8ubZ7mK4tLnXC29WRsRdVbk8n8kpYuiOJ8gqVcpHmTIVcREREBDicVUaLSS9zotiKg9XCyC4hXNImAKsuaSZ1yNfdiWt6hOJos3A8q4hvd6dgNwyzY4mISVTIRUREpNlbsvU4f1mRjoNPCO4OBuP7RNAu2NPsWNJEBXu5MLpbKDaLhUNpBazYewJDpVykWVIhFxERkWarrMLO00t38+BH2ymtgKJDvzAkpIwAD2ezo0kTF+HnxoguIViA3Um5rDuUYXYkETGBCrmIiIg0SzmFZUz6zyYW/nwUgOs6enDi02dw0rsjqSdtgjwY0iEIgF/is9hxPNvcQCJS7/QnR0RERJqd+IwCxsz/mXWHMnBzsrFgYk9u7uoJhgbYkvrVJcyb/i39AFi1L43DafkmJxKR+qRCLiIiIs3KL0czGfP6Og6nFdDC24VP7hnAiC4tzI4lzVjfln50DvXCAL7ZlUJKTrHZkUSknjSYQv7cc89hsViYPn161bTi4mKmTJmCv78/Hh4ejBs3jtTUVPNCioiISKP2xbZEbn5zI5kFpXQJ8+LzKRfTKdTL7FjSzFksFga3DyLK341yu8GX25PIKiw1O5aI1IMGUcg3b97Mv/71L7p161Zt+oMPPsjSpUtZvHgxq1evJikpibFjx5qUUkRERBorwzB4dfkBHvhwG6Xldq7oFMzHd8cQ7OVidjQRAGxWC1d2aUGQpzNFZRV8sS2JorIKs2OJyAVmeiHPz89nwoQJ/Pvf/8bX17dqek5ODm+99RZz5sxhyJAh9OrVi4ULF7Ju3To2bNhgYmIRERFpTErKK3jo4+3M+WE/AHcNbMmCib1wc3IwOZlIdU4OVq7uHoqXiwM5RWV8vSOZCrsuhybSlJleyKdMmcKoUaMYOnRotemxsbGUlZVVm96hQwciIyNZv379GddXUlJCbm5utS8RERFpnrIKSrnlzU18tjURm9XC38d04bFRnbBZLWZHEzktd2cHru4eipPNSmJ2ESv36RrlIk2ZqYX8ww8/ZMuWLcyePfuUeSkpKTg5OeHj41NtenBwMCkpKWdc5+zZs/H29q76ioiIqOvYIiIi0gjEZxQwdv46Nh3NxNPZgYWT+zChX5TZsUTOyt/DmZG/uUb51mPZZkcSkQvEtEJ+7NgxHnjgAd5//31cXOru/K1Zs2aRk5NT9XXs2LE6W7eIiIg0DrsScxg3fx1H0gsI83Hl0/sGcGm7QLNjidRYdIA7A9sGAPDTgXQOp+tyaCJNkWmFPDY2lhMnTtCzZ08cHBxwcHBg9erVvPrqqzg4OBAcHExpaSnZ2dnV7peamkpISMgZ1+vs7IyXl1e1LxEREWk+1h1M58Y3NpCeX0qnFl4smTKAdsGeZscSOWc9Inzo8utVAL7dlUJaXonJiUSkrplWyC+//HJ27tzJtm3bqr569+7NhAkTqr53dHRk+fLlVffZt28fCQkJxMTEmBVbREREGrCvdiQxaeEm8kvKiWnlz0d39yfIUyOpS+NksVgY1D6IcF9XyioMlu5IoqCk3OxYIlKHTBte1NPTky5dulSb5u7ujr+/f9X0O+64gxkzZuDn54eXlxfTpk0jJiaG/v37mxFZRESk2UlISCA9Pd3sGDWy7EABb23NxQAGhLvwwEWOHNizs8b3j4uLu3DhRGrJZrUwqmsLPtp8jOyiMr7emczYi8JwsJk+NrOI1IEGfb2Pl19+GavVyrhx4ygpKWH48OG8/vrrZscSERFpFhISEujQsSNFhYVmRzkrn4ET8R5wIwC5sV/xwQtv8IFhr9W68vN1rq40LC6ONq7uEcpHm4+RnFPM8r0nGNYpGItFVwsQaewaVCFftWpVtdsuLi7MmzePefPmmRNIRESkGUtPT6eosJAJj75IcGRrs+Oclt2ArZk2jhbYAOjkXU6Ha4dhGTPsnNcVt2k137wzl+Li4rqOKXLefN2cuLJrCz7flsjelDx83Z3oG+1ndiwROU8NqpCLiIhIwxMc2Zrwtp3NjnGK8go73+xK4WhBARZgSIcguoR513p9qQmH6i6cyAUQ6efGoHaBrNyXxvpDGfi5OdEmyMPsWCJyHnTyiYiIiDQ6JeUVfL4ticPpBZXn2HZrcV5lXKSx6BbuQ49wHwB+2JNKZkGpuYFE5LyokIuIiEijUlxWwZKtiSRmF+HkYGVMjzBaB2ovoTQfl7QNIMzHldIKO1/tSKK0vHbjJYiI+VTIRUREpNEoKCnnky3HSc0twdXRxrieYYT5upodS6Re2awWRnYJwcPZgazCMr7fk4JhGGbHEpFaUCEXERGRRiGvuIxPYo+TkV+Ku1NlGdc1xqW5cnd2YFTXFtgsFg6lFRAbn2V2JBGpBRVyERERafCyC0tZHHuc7KIyPF0cuK5XOP4ezmbHEjFViLcLl7UPBGDdoQziMwpMTiQi50qFXERERBq0jPwSPok9Tl5xOT5ujlzfKxwfNyezY4k0CF1CvejUwgsD+HZ3CrlFZWZHEpFzoEIuIiIiDdaJ3GI+2XKcgtIK/D2cuK5nOJ4ujmbHEmkwLBYLg9sHEuTpTHGZna93JlNeoUHeRBoLFXIRERFpkJKyi/h0ayLFZXaCvZy5rmc47s4OZscSaXAcbFZGdWuBq6ONE3klrNh3QoO8iTQSKuQiIiLS4BzLLOTzbYmUltsJ9XFhzEVhuDjazI4l0mB5uTgyoksIFiAuOY+diTlmRxKRGlAhFxERkQblcHo+X2xPoqzCINLPjWt7hOHsoDIucjaRfm5c3CYAgNX708gosZicSETORoVcREREGoz9qXl8vSOZCrtB60B3RndvgaNNb1dEaqpnpA9tgjywG7Ah3QGrq5fZkUTkD+gvnIiIiDQIu5Ny+HZXCnYD2gd7MrJLCxyseqsici4sFgtXdAzG182R4goLAVc9hF3nk4s0WPorJyIiIqbbfiybH+NOYFB5GadhnYOxWXW4rUhtODlYubJrC2wWA9dWvViyV9cnF2moVMhFRETEVL8czWTV/jQAekT4MKRDEFaLyrjI+QjwcKaHbwUAH+zKY9ORTJMTicjpqJCLiIiIKQzDYP2hDH4+lAFA32g/Lm0bgEVlXKRORLnbyd+1ArsB93+wlYz8ErMjicjvqJCLiIhIvTMMgzUH0tl0tHKv3cVt/Ilp7a8yLlKHLBbI/P51wjxtpOQWM+Pj7djtOp9cpCFRIRcREZF6ZTcMlu89wbZj2QAMah9I7yg/c0OJNFFGWTEPx/ji7GBl9f40Fqw5ZHYkEfkNFXIRERGpNxV2g+92p7A7KRcLcEWnYLqH+5gdS6RJi/Jx5JlrOgPw0vf72XxU55OLNBQq5CIiIlIvyivsLNuZzP7UfKwWGNklhE4tdI1kkfpwQ+8Iru0RSoXdYNqirWQWlJodSURQIRcREZF6UFZhZ+mOZA6nF2CzWriqWyhtgz3NjiXSbFgsFv4+piutAt1/PZ98m84nF2kAVMhFRETkgiopr+DzrYkkZBbiaLNwTfdQWga4mx1LpNlxd3Zg3s09cXawsmpfGv9ac9jsSCLNngq5iIiIXDCFpeV8uiWRpJxinBysXNsjjAg/N7NjiTRbHVt48dTVleeT/+P7ffyi88lFTKVCLiIiIhdEbnEZi2OPk5ZXgqujjet6hhPq42p2LJFm78Y+EVzz6/nkD3y4jZyiMrMjiTRbKuQiIiJS57IKSln8y3GyC8vwdHHg+t7hBHo6mx1LRKg8n/zZa7sQ6edGYnYRjy3ZiWHofHIRM6iQi4iISJ06kVfM4tjj5JeU4+vmyPW9wvF1czI7loj8hqeLI3Nv7IHNauGrHcl8uiXR7EgizZIKuYiIiNSZxKwiPo1NpKisgiBPZ67rFY6ni6PZsUTkNC6K9GXGFe0AeOKLXRxNLzA5kUjzU6tC3qpVKzIyMk6Znp2dTatWrc47lIiIiDQ+R9IL+HxbIqUVdsJ8XBnbMww3JwezY4nIH7jnstb0a+lHYWkF93+4ldJyu9mRRJqVWhXyo0ePUlFRccr0kpISEhN1uIuIiEhzsz81j692JFFuN4j2d+PaHqE4O9jMjiUiZ2GzWnh5fA+8XR3ZcTyHl3/cb3YkkWblnD62/vLLL6u+/+677/D29q66XVFRwfLly4mOjq6zcCIiItLwbT+ezap9aQC0D/bkik7B2KwWk1OJSE2F+rjy3Niu3Pv+FhasPsTANgEMaBNgdiyRZuGcCvm1114LVI7MOGnSpGrzHB0diY6O5qWXXqqzcCIiItJwGYbBz4cyiI3PAqBrmDeD2wdisaiMizQ2I7u24MY+EXy4+RgPfryNbx+4FF93DcYocqGd0yHrdrsdu91OZGQkJ06cqLptt9spKSlh3759XHXVVRcqq4iIiDQQ5XY73+5OqSrjMa38VcZFGrknRneiVYA7qbklPPrpDl0KTaQe1Ooc8iNHjhAQoMNYREREmqOSsgq+2JrE/tR8rBYY1imYvi39VMZFGjk3JwdevekiHG0Wvt+TyqJNCWZHEmnyaj306fLly1m+fHnVnvLf+s9//nPewURERKThyS0u48ttSWQUlOJks3Jl1xCi/N3NjiUidaRLmDePDO/A35fF8bev9tCvpR9tgjzNjiXSZNVqD/nTTz/NsGHDWL58Oenp6WRlZVX7EhERkaYnLa+Ej385RkZBKe7ONq7rFa4yLtIE3XFJSwa2DaC4zM60D7ZRUn7q1ZVEpG7Uag/5ggULePvtt7nlllvqOo+IiIg0QPEZBSzbmUJphR1/dyeu6RGKp4uj2bFE5AKwWi28dH13Rsz9ibjkXOb8sJ9ZIzuaHUukSarVHvLS0lIGDBhQ11lERESkAYrPt/Ll9iRKK+yE+7pyfa9wlXGRJi7Iy4XZY7sC8Maaw2w8nGFyIpGmqVaF/M4772TRokV1nUVEREQaELth4HPZJH7JdMBuVF5j/JoeoTg72syOJiL1YHjnEG7oHY5hwIyPt5NbXGZ2JJEmp1aHrBcXF/PGG2/w448/0q1bNxwdq39KPmfOnDoJJyIiIubILynnuZ+z8O5/PQB9on2JaeWvkdRFmpknRndm/eEMjmUW8dSXu5lzQw+zI4k0KbUq5Dt27KBHjx4A7Nq1q9o8/aEWERFp3I5lFnLnO7+wL7UEo7yUvsFWBrTW5U5FmiMPZwdevqEHN/xrPZ9tSWRox2Cu7NrC7FgiTUatDllfuXLlGb9WrFhR4/XMnz+fbt264eXlhZeXFzExMXzzzTdV84uLi5kyZQr+/v54eHgwbtw4UlNTaxNZREREauDng+lc/c+17EvNw9fFSsqiPxPpbj/7HUWkyeod7ce9g1oD8JclO0nNLTY5kUjTUatCXlfCw8N57rnniI2N5ZdffmHIkCFcc8017N69G4AHH3yQpUuXsnjxYlavXk1SUhJjx441M7KIiEiTZBgGC1Yf4pa3NpJVWEbXMG9eGBpAafJ+s6OJSAPwwOXt6BLmRXZhGTM/2YFhGGZHEmkSanXI+uDBg//w0PSa7iUfPXp0tdt///vfmT9/Phs2bCA8PJy33nqLRYsWMWTIEAAWLlxIx44d2bBhA/37969NdBEREfmd/JJyZi7ezje7UgC4rlc4z17bhT07t5ucTEQaCicHKy/f0IOrXlvLmv1pvLshnltjos2OJdLo1aqQnzx//KSysjK2bdvGrl27mDRpUq2CVFRUsHjxYgoKCoiJiSE2NpaysjKGDh1atUyHDh2IjIxk/fr1ZyzkJSUllJSUVN3Ozc2tVR4REZHm4OCJPO5+N5ZDaQU42iw8ObozE/pFakwYETlF22BP/jyyA08v3cP/LYtjQOsA2gR5mB1LpFGrVSF/+eWXTzv9qaeeIj8//5zWtXPnTmJiYiguLsbDw4MlS5bQqVMntm3bhpOTEz4+PtWWDw4OJiUl5Yzrmz17Nk8//fQ5ZRAREWluDMPgk9jjPPHFborKKgjxcuH1iT3pGelrdjQRacAmxUSzYu8JfjqQzoyPt/HpvQNwtJl6FqxIo1an/3smTpzIf/7zn3O6T/v27dm2bRsbN27k3nvvZdKkSezZs6fWGWbNmkVOTk7V17Fjx2q9LhERkaaooKSchz7ezsxPdlBUVsHAtgEsnXaJyriInJXVauHF67rj7erIjuM5vLr8gNmRRBq1Wu0hP5P169fj4uJyTvdxcnKiTZs2APTq1YvNmzczd+5cxo8fT2lpKdnZ2dX2kqemphISEnLG9Tk7O+Ps7Fyr/CIiIk3d7qQcpi3ayuH0AqwWeGhYe+69rDVWqw5RF5GaCfF24e9jujB10VbmrTzIoPZB9IrSB3oitVGrQv77kc4NwyA5OZlffvmFxx9//LwC2e12SkpK6NWrF46Ojixfvpxx48YBsG/fPhISEoiJiTmvxxAREWluKuwG//7pMC99v4+yCoMQLxdeveki+rb0MzuaiDRCV3ULZXncCZZsTeShj7ex7IGBuDnV6b4+kWahVv9rvL29q922Wq20b9+eZ555hmHDhtV4PbNmzWLkyJFERkaSl5fHokWLWLVqFd999x3e3t7ccccdzJgxAz8/P7y8vJg2bRoxMTEaYV1EROQcHMss5KHF29l0JBOAoR2DeeG6bvi5O5mcTEQas6eu7syGwxkczSjk/5bF8ey1Xc2OJNLo1KqQL1y4sE4e/MSJE9x6660kJyfj7e1Nt27d+O6777jiiiuAysHjrFYr48aNo6SkhOHDh/P666/XyWOLiIg0dYZh8OmWRJ76cjf5JeW4Odl4cnQnbugdoVHUReS8ebs68o/ruzPhzY28tyGBKzqFcFm7QLNjiTQq53VcSWxsLHFxcQB07tyZiy666Jzu/9Zbb/3hfBcXF+bNm8e8efNqnVFERKQ5Ssou4i9LdrJqXxoAvaJ8mXNDd6L83U1OJiJNycVtApg8IJq31x1l5uLtfP/gpfi46egbkZqqVSE/ceIEN954I6tWraoacC07O5vBgwfz4YcfEhioT8ZERETMYLcbLNqUwHPf7CW/pBwnm5UHhrbl7ktb4aBLE4nIBfDoiA6sOZDG4bQCHv9iN6/ddG476USas1r9ZZ42bRp5eXns3r2bzMxMMjMz2bVrF7m5udx///11nVFERERq4OCJPG769wb++vku8kvK6Rnpw7IHLmHK4DYq4yJywbg62Xj5hh7YrBaWbk/iy+1JZkcSaTRqtYf822+/5ccff6Rjx45V0zp16sS8efPOaVA3EREROX9FpRW8tuIA//7pMGUVBq6ONmYOb8+kAdHYdDkzEakH3SN8mDq4DXOXH+Dxz3fRN9qPEO9zuxyySHNUq0Jut9txdHQ8ZbqjoyN2u/28Q4mIiEjN/LAnlae+3E1idhEAQzoE8fTVnYnwczM5mYg0FCfHfLrQLvY1+MrXkUNZZdyzcC2PD/Q1ZQDJgIAAIiMj6/1xRWqjVoV8yJAhPPDAA3zwwQeEhoYCkJiYyIMPPsjll19epwFFRETkVAdS83j26zhW768ctC3Mx5UnR3fiik7BGkFdRADIzaz8/TBx4sR6e0wH/3BaTJrLthQYdOfj5G/7pt4e+yRXNzf2xsWplEujUKtC/s9//pOrr76a6OhoIiIiADh27BhdunThvffeq9OAIiIi8j9ZBaW88uN+3tuYQIXdwNFm4fZLWvLA5W1xczqvi6eISBNTlJ8LwKi7H6N9t1719rgHcq3syIagEfdx0+S78Dz1wNoLJjXhEO8/P5P09HQVcmkUavWXOyIigi1btvDjjz+yd+9eADp27MjQoUPrNJyIiIhUKi6r4N318by24gC5xeUADOsUzKwrO9IyQJcyE5Ez8w+NIrxt53p7vDDDIHNrIsezithZ6MV1PcOxajwLkdM6p0K+YsUKpk6dyoYNG/Dy8uKKK67giiuuACAnJ4fOnTuzYMECBg4ceEHCioiINDflFXY+3XKcV348QHJOMQAdW3jx+FUdGdA6wOR0IiKnslgsXNEpmPc3JJCcU8wvCVn0jfYzO5ZIg3ROhfyVV17hrrvuwsvL65R53t7e3H333cyZM0eFXERE5DzZ7Qbf7ErhpR/2cTitAIBQbxemX9GOcT3DNXq6iDRoXi6ODGofyPd7Utl4OINofzeCPDXqusjvndNFSbdv386IESPOOH/YsGHExsaedygREZHmym43WLYzmZFzf2LKoi0cTivA182Rv47qyIqHB3FD7wiVcRFpFDqEeNI60B27Ad/vTqW8QldjEvm9c9pDnpqaetrLnVWtzMGBtLS08w4lIiLS3FTYDb7dlcKryw+wLzUPAE9nB267pCV3DWyJp0s9jookIlIHLBYLQzoEkZSdQEZBKesPZzCwbaDZsUQalHMq5GFhYezatYs2bdqcdv6OHTto0aJFnQQTERFpDorLKvh0y3H+veYwRzMKgf8V8Tsubom3m4q4iDRebk4ODO0YxNIdyWxJyKZVgAdhvq5mxxJpMM6pkF955ZU8/vjjjBgxAheX6ueAFBUV8eSTT3LVVVfVaUAREZGmKLuwlPc2xPP2uqOk55cC4O3qyKQB0SriItKktAr0oFMLL/Yk5/L9nhQm9IvCyeGczpwVabLOqZD/9a9/5bPPPqNdu3ZMnTqV9u3bA7B3717mzZtHRUUFjz322AUJKiIi0hQkZhfx1k9H+HBzAoWlFQCE+bhyxyUtGd8nAndnXUtcRJqeS9sFcDyrkNzictYcSGNox2CzI4k0COf0Vz84OJh169Zx7733MmvWLAzDACrPDxk+fDjz5s0jOFj/uURERH5vV2IOb609wpfbk6iwV/797NjCi3sua8WVXVvgaNPeIhFpupwdbFzRKZhPtySyOymXVgHutAr0MDuWiOnO+WP4qKgoli1bRlZWFgcPHsQwDNq2bYuvr++FyCciItJolZbb+WZXMu+sO8qWhOyq6Re38efuS1szsG0AFotGTBeR5iHc142ekT5sScjmx7gTTPR2wc1JRwVJ81br/wG+vr706dOnLrOIiIg0Cck5RSzamMAHmxKqzg93tFkY2aUFdw1sRddwb5MTioiYI6aVP/EZhWQUlLJi7wlGdW2hDyalWdNHUiIiInXAMAzWH87g3fXxfL8nteqw9BAvF27uF8mNfSMI8nQ5y1pERJo2B5uVYZ2D+WjzMQ6lFbA3JY+OLbzMjiViGhVyERGR85BfUs6SrYn8d91RDpzIr5rev5Uft8ZEc0WnYJ0fLiLyG0GeLvRr6c/6wxms2pdGuK8rni66soQ0TyrkIiIitXDwRD7vbYjnk9jj5JeUA+DmZGPMRWHcGhNN+xBPkxOKiDRcvaN8OZJeQEpuMd/vSWXsRWE6dF2aJRVyERGRGqqwGyyPS+W/6+NZezC9anqrAHduiYliXK9wvLSXR0TkrKxWC8M6B7NoYwLHs4rYfjyHHhE+ZscSqXcq5CIiImeRkV/CR78c4/0NCSRmFwFgtcCQDsFMGhDFxa0DsFq1Z0dE5Fz4ujlxSdsAVu1LY+3BdCL93PBzdzI7lki9UiEXERE5g23Hsvnv+qN8tT2Z0go7AL5ujozvE8mEfpFE+LmZnFBEpHHrFubN4bQCEjIL+W53Cjf0jsCmDzilGVEhFxGRWktISCA9Pf3sCzYiZRUGa48V8c3BQg5mllVNb+3ryJVt3bi6RzhtW0WbF1BEpAmxWCxc0TGY9zbGcyKvhM1HM+nfyt/sWCL1RoVcRERqJSEhgQ4dO1JUWGh2lDph8/DDo8dIPHuMwObuC4BRXkbB3p/I2/IV8cn7WQE87ubG3rg4IiMjzQ0sItJEeLg4MLh9EN/uTmHT0UxaBrgT7KXLRErzoEIuIiK1kp6eTlFhIRMefZHgyNZmx6m1zBILB/OsHC+0YlB5mKSrzaCVRwUtPQycW10CV14CQGrCId5/fibp6ekq5CIidahdsAeH0jw4cCKf73ancHPfSBx0yUhpBlTIRUTkvARHtia8bWezY5yTCrvBgRN5bDuWTWpuSdX0UG8XekT40CrQQ+cwiojUI4vFwuAOQSRlF5FVWMbPhzK4rF2g2bFELjgVchERaTYKSsrZmZjDzsQcCksrALBZLLQL8aBHuA9BOkRSRMQ0ro42hnYM5ovtSWw7lk3LAHciNXimNHEq5CIi0uSl5Baz/Vg2+1PzsBuV09ydbXQL86FLmBduTvpzKCLSEEQHuNMlzItdibn8sCeVif0icXa0mR1L5ILROxAREWmS7IbBkfQCYuOzSM4prprewtuF7uE+tAnSYekiIg3RwDaBHMssIqeojNX70xjWOcTsSCIXjAq5iIg0KeUVdvYk57I1IZvsosrLllkt0C7Yk+4RPoTosHQRkQbNycHKsE7BfBJ7nLiUPFoFetAmyMPsWCIXhAq5iIg0CYWl5ew4nsOO4zkUlVWeH+7sYKVrmDc9Inxwd9afPBGRxiLUx5VeUb78Ep/F8r2ptPB20e9xaZK0VYuISKOWVVjK1oRs9iTnUvHrCeJeLg5cFOlLpxZeODnosjkiIo1R/1b+HM0oID2/lOV7TzC6WwssFp1qJE2LCrmIiDRKJ/KK+eVoFgdO5FdNC/ZypmekL20CPbDq/HARkUbNZrUwvHMIH246xpH0AnYn59Il1NvsWCJ1SoVcREQalcSsIjbHZxKfUVg1rWWAO70ifQn1camXvSdxcXEX/DEagubyPEWk4QrwcCamtT9rD6azZn8aEb5ueLs6mh1LpM6okIuISINnGAZHMwrZfDSzasR0y68DtfWO8iXAw7lecuRmpgEwceLEenm8hiI/P//sC4mIXCAXRfpwOC2fpJxivt+Twrie4Vh16Lo0ESrkIiLSYNkNg4Mn8tl8NJP0/FIAbBYLnUK96BXlW+97SYrycwEYdfdjtO/Wq14f2wxxm1bzzTtzKS4uPvvCIiIXiNViYVjnEN7fGE9SdjFbE7LpFeVrdiyROqFCLiIiDU653c7e5Dx+ic8i59dLlznaLHQL8+GiSPNHTPcPjSK8bWdTM9SH1IRDZkcQEQHA29WRS9sGsnzvCdYfyiDK363ejo4SuZBUyEVEpMEoLbezKymHLQlZFJRUXrrMxcFKjwgfukf44OJoMzmhiIiYpXOoF4fS8jmaUch3u1O4sU8kNg3gKY2cCrmIiJiupLyCHccri3hxmR0AD2cHLor0oUuoty5dJiIiWCwWhnYM5r2N8aTnl7LuUDoD2waaHUvkvKiQi4iIaUrKK9h+rLKIl5RXFnFvV0d6R/nSoYUnDlYVcRER+R93ZweGdgzmqx3JbEnIJtLPjSh/d7NjidSaqe90Zs+eTZ8+ffD09CQoKIhrr72Wffv2VVumuLiYKVOm4O/vj4eHB+PGjSM1NdWkxCIiUheKyyrYcDiD//x8lPWHMygpt+Pr5sjwzsHc2j+KLmHeKuMiInJarQM96BpWeT3y7/ekUlhabnIikdoz9d3O6tWrmTJlChs2bOCHH36grKyMYcOGUVBQULXMgw8+yNKlS1m8eDGrV68mKSmJsWPHmphaRERqq6isgvWHMlj481E2HsmktNyOn7sTIzqHMLF/FB1CvLDqfEARETmLgW0D8HN3orC0gh/2pGIYhtmRRGrF1EPWv/3222q33377bYKCgoiNjeXSSy8lJyeHt956i0WLFjFkyBAAFi5cSMeOHdmwYQP9+/c3I7aIiJyjwtJytiZks/14NmUVlW+a/D2c6BftR5sgDyy6nqyIiJwDR5uVkV1C+HDzMY5mFLL9eA49InzMjiVyzhrUOeQ5OTkA+Pn5ARAbG0tZWRlDhw6tWqZDhw5ERkayfv360xbykpISSkpKqm7n5uZe4NQiInImBSWVRXxH4v+KeKCHM31b+tE60F1FXEREai3Aw5mBbQJYtT+NtQfSCfNxNTuSyDlrMIXcbrczffp0Lr74Yrp06QJASkoKTk5O+Pj4VFs2ODiYlJSU065n9uzZPP300xc6roiI/IGCknJi47PYmZhDub2yiAd5OtOvpR8tA1TERUSkbnQL9yY+s5Aj6QV8uyuFgX5mJxI5Nw2mkE+ZMoVdu3axdu3a81rPrFmzmDFjRtXt3NxcIiIizjeeiIjUQH5xOb/EZ7IrKZeKX4t4sJcz/Vr6E+3vpiIuIiJ1qvJSaEEs2phAZmEpO6w2syOJnJMGUcinTp3KV199xZo1awgPD6+aHhISQmlpKdnZ2dX2kqemphISEnLadTk7O+Ps7HyhI4uIyG/kFpcRezSL3Um5VPw6sE4Lbxf6tfQj0k9FXERELhw3JweGdQ5hydZEjuTbcG0XY3YkkRoztZAbhsG0adNYsmQJq1atomXLltXm9+rVC0dHR5YvX864ceMA2LdvHwkJCcTE6D+aiIjZbF5BbMm0EX/sKL/uECfUx4V+Lf2J8HVVERcRkXoR6edGryhfYuOz8B9xP+mFFWZHEqkRUwv5lClTWLRoEV988QWenp5V54V7e3vj6uqKt7c3d9xxBzNmzMDPzw8vLy+mTZtGTEyMRlgXETFRQkYh8zZnE/anNziSX3l4YLivK/1a+hHu62ZyOhERaY5iWvlzODmDLDyZuzGbywcY2HQpTWngTC3k8+fPB2DQoEHVpi9cuJDJkycD8PLLL2O1Whk3bhwlJSUMHz6c119/vZ6TiogIwJH0Av654iCfb0ukwm5gsTkQ5GLn0k6RhPlqdFsRETGPzWqhj38538ZXsDsNXl95kGmXtzU7lsgfMv2Q9bNxcXFh3rx5zJs3rx4SiYjI6Rw8kc+8lQf5Ylti1aHpF4U4s+zFaYx77P9UxkVEpEHwdITMH+YTMGoGL/+4n97RfsS09jc7lsgZWc0OICIiDdf+1DymfbCVK15ezZKtlWX88g5BfD7lYh6/1I/SpL1mRxQREammYNcKBke7Yjfg/g+3ciKv2OxIImfUIEZZFxGRhiUuOZd/rjjIsl3JnDyYaVinYKYNaUvXcG8AtqSZGFBEROQP3NXTi8QiG/tT83ngg228d2c/nU8uDZIKuYiIVNmdlMOryw/w3e7Uqmkju4QwdUgbOod6m5hMRESk5lwcrLw+oSdX//Nn1h/OYO7yA8y4op3ZsUROoUIuIiLsOJ7Nq8sP8mNcZRG3WGBU1xZMG9KW9iGeJqcTERE5d22CPJk9tisPfLiN11YcoHeUL5e2CzQ7lkg1KuQiIs3Y1oQsXl1+gJX7Ko8/t1pgdPdQpg5uQ9tgFXEREWncrukRxsYjmSzamMD0j7ax7P6BhHi7mB1LpIoKuYhIMxQbn8nc5QdZs/9/Rfzai8KYMrgNrQM9TE4nIiJSd564qhPbErLZk5zLtA+2sOiu/jjaNLa1NAwq5CIizcjGwxm8uuIAPx/MACqv2Tr21yIeHeBucjoREZG65+Jo4/UJPbnqtbVsPprFP77fx6yRHc2OJQKokIuI1KmEhATS09PNjlGNYRjsSivl49357E4rBcBmgSEtXRnTwYMQjwoyE/aRmXBu642Li7sAaUVEROpedIA7L1zXjfve38K/Vh+mT5QfQzsFmx1LRIVcRKSuJCQk0KFjR4oKC82OUsUlugfeA27CJaIzAEZFGfk7fiBnw2LezE3jzTp4jPz8/DpYi4iIyIV1ZdcWTB4QzdvrjvLQ4u18Ne0SIvzczI4lzZwKuYhIHUlPT6eosJAJj75IcGRr03IYBqQWW4jLsZFZWnmOnBWDlh522nkZuLUcCtcMPe/Hidu0mm/emUtxcfF5r0tERKQ+/OXKjmw9ls32Y9lMXbSFj++JwdnBZnYsacZUyEVE6lhwZGvC23au98c1DIP4jEI2HMkgNbcEqDxHvGuYN72ifPFwrttf+akJh+p0fSIiIheak4OVf950EVe9tpbtx3N4euke/m9MV7NjSTOmQi4i0sgZhkFCZiEbDmeSklu5t9rBaqFbuDc9I31xr+MiLiIi0phF+Lnxyo09uP3tzSzamED3cG/G94k0O5Y0U3qXJiLSSJ0s4huPZJKcoyIuIiJSU4PbBzFjaDte+mE/j3++m/YhXvSI8DE7ljRDercmItLIGIbBsawiNhzOqCriNquFbr8emq4iLiIicnZTBrdh+/EcfoxL5d73Ylk67RICPJzNjiXNjNXsACIiUjMn94h/EnucJVsTSc4pxma10CPCh9sGRHNpu0CVcRERkRqyWi3MGd+dVgHuJOcUM3XRFsor7GbHkmZGhVxEpBE4llnIJ1sqi3jSySIeXlnEL1MRFxERqRUvF0feuLUX7k42NhzO5Nmv48yOJM2M3sGJiDRgx7MqB2tLzC4CKg9N7xLqRe9ovzofNV1ERKQ5ahPkyUs39OCe92J5e91ROoR4cmNfDfIm9UPv5kREGqDEX88RP36yiFssdAnzoneUHx4u+tUtIiJSl0Z0CWHGFe2Y88N+Hv9iF62DPOgT7Wd2LGkG9K5ORKQBScouYv3hDI5n/a+Idw71one0L54ujianExERabqmDWnDvpQ8vt6ZzD3vxvLltEsI83E1O5Y0cSrkIiINQFpeCesOpXM0oxAAqwU6h3rTR0VcRESkXlgsFl68vhtH0gvYk5zLne/8wqf3xuDmpMokF44GdRMRMVFWYSnf7Epm0aYEjmYUYrFAl1AvJg2IZkiHIJVxERGReuTm5MC/J/UmwMOJuORcZny0HbvdMDuWNGEq5CIiJsgvLmd5XCrvbohnf2o+AO2CPbilfxSXdwzGS0VcRETEFGE+riyY2Asnm5Vvd6fw/Hd7zY4kTZiOvxARqUdFpRX8Ep/J9uM5VPz6iXu0vxsDWgcQ6OlscjoREREB6B3txwvXdWP6R9v41+rDRPu7c5NGXpcLQIVcRKQelJbb2ZqQxZaEbEor7ACE+rgwoHWABowRERFpgK69KIwj6QXMXX6Axz/fRYSvG5e0DTA7ljQxKuQiIhdQeYWdHYk5/HI0i6KyCgACPZwZ0NqfKH83LBaLyQlFRETkTKYPbcvRjAK+2JbEve/H8tm9A2gb7Gl2LGlCVMhFRC4Au91gT0ouGw9nkl9SDoCPmyMxrfxpG+ShIi4iItIIWCwWnh/XjcSsIn6Jz+K2tzfz2X0DCPJ0MTuaNBEa1E1EpE5ZOF5g5d2N8SyPO0F+STkezg5c3iGIW/pF0S7YU2VcRESkEXFxtPHGrb2J9nfjeFYRty3cXPVhu8j5UiEXEakDhmGwJbmYkEkvszHDgezCMlwdbQxsG8CkmCi6hHljtaqIi4iINEZ+7k68c3tf/N2d2J2Uy73vxVJabjc7ljQBKuQiIufpl6OZjP/XBp79KQvnkDY4WAz6t/Rj0oAoekb64mDTr1oREZHGLsrfnYW39cHNycZPB9L586c7MAxdo1zOj94liojU0u6kHG5buInrFqxn09FMHK2Qs+kzRoSW0a+VP84ONrMjioiISB3qFu7DvAk9sVktfLY1kRe+22d2JGnkVMhFRM7RkfQCpn2wlVGvrmXlvjRsVgs39Y1k3pVBZK/8D87q4SIiIk3W4PZBzB7bFYD5qw7x5k+HTU4kjZlGWRcRqaHknCJeXX6Qj385RoW98hC10d1DmXFFO1oGuLNlyxaTE4qIiEh9uKF3BGl5Jbz43T6e/ToOL1dHbugdYXYsaYRUyEVEziKzoJT5qw7yzvr4qgFchnQI4qFh7egc6m1yOhERETHDfYNak1NUxhtrDvPnT3fg5eLAiC4tzI4ljYwKuYjIGeSXlPPWT0f490+Hqy5v0jfaj5kj2tMn2s/kdCIiImImi8XCrJEdyCks46NfjnH/B9v4z2RHLmkbYHY0aURUyEVEfqe4rIL3NsTz+qpDZBaUAtCphRczR7RnULtAXUdcREREgMpS/n9ju5JXUsaynSn86d1fePeOvvSK0gf3UjMq5CIivyqvsPNJ7HHmLj9Ack4xAK0C3JkxrB1Xdmmh64iLiIjIKWxWCy+P70F+SSxr9qcx6T+bee/OfvSI8DE7mjQCKuQi0uzZ7QbLdiUz5/v9HE4vAKCFtwvTh7ZlXM9wXUdcRERE/pCzg41/TezFbW9vYsPhTG55ayOL7uxP13CNNSN/TIVcRJotwzBYtT+Nf3y3j91JuQD4uTtx36DWTOwfhYujrl8mIiIiNePqZOOtSX2YvHATm49mMfGtjSy6q58GgJU/pEIuIs3S5qOZvPjtPjYdzQTAw9mBuwa24o6BLfFw1q9GEREROXfuzg4svK0vt761kS0J2Ux8cyOL7upPxxZeZkeTBkrvOkWkWdlxPJuXvt/P6v1pADg5WJkUE8W9g9rg5+5kcjoRERFp7DycHXj79r7c8tYmth/L5uZ/b+DdO/rRJUx7yuVUpp4YuWbNGkaPHk1oaCgWi4XPP/+82nzDMHjiiSdo0aIFrq6uDB06lAMHDpgTVkQatb0pufzpv79w9T9/ZvX+NBysFm7qG8nqmYN4bFQnlXERERGpM14ujvz39r50j/Ahq7CMm/+9ga0JWWbHkgbI1EJeUFBA9+7dmTdv3mnnv/DCC7z66qssWLCAjRs34u7uzvDhwykuLq7npCLSWB1Ky2faB1sZOfcnvt+TitUCY3uGseKhQcwe25UW3q5mRxQREZEmyNvVkffu6EvvKF9yi8u55a1NbP71VDmRk0w9ZH3kyJGMHDnytPMMw+CVV17hr3/9K9dccw0A//3vfwkODubzzz/nxhtvrM+oItLIHMssZO7yA3y25Th2o3LaqG4teHBoW9oEeZobTkRERJoFTxdH3rm9L3e+8wvrD2dw61ubeHNSby5uE2B2NGkgGuy1fI4cOUJKSgpDhw6tmubt7U2/fv1Yv379Ge9XUlJCbm5utS8RaT6Sc4p4bMlOBv9jFZ/EVpbxoR2DWXb/QObd3FNlXEREROpV5UBvfbisXSBFZRXc9vZmvt2VYnYsaSAabCFPSancSIODg6tNDw4Orpp3OrNnz8bb27vqKyIi4oLmFJGGIS2vhGeW7uGyF1fx/sYEyu0Gl7YL5PMpF/PmpN50CtXopiIiImIOF0cbb9zai2Gdgiktt3Pf+7F8uCnB7FjSADS5UdZnzZrFjBkzqm7n5uaqlIs0Yen5Jfz7p8P8d108RWUVAPRt6cfDw9rTt6WfyelEREREKjk72Hh9Qk8eW7KLj345xp8/20lGQSn3DWqNxWIxO56YpMEW8pCQEABSU1Np0aJF1fTU1FR69Ohxxvs5Ozvj7Ox8oeOJiMlO5BbzrzWHeX9jPMVldgC6R/jw8LB2XNImQH/YREREpMFxsFl5blxX/D2ceH3VIV78bh8Z+aX8dVRHrFa9d2mOGmwhb9myJSEhISxfvryqgOfm5rJx40buvfdec8OJiGmSc4r41+rDLNqUQGn5/4r4A5e3YXD7IBVxERERadAsFguPjOiAn7sTz34dx39+PkJyThEvj++Bi6PN7HhSz0wt5Pn5+Rw8eLDq9pEjR9i2bRt+fn5ERkYyffp0nn32Wdq2bUvLli15/PHHCQ0N5dprrzUvtIiYIjG7iPmrDvLx5uOUVlQW8V5RvjxweVsGttUecREREWlc7hzYikBPZ2Yu3sE3u1JIztnAm5N6E+Cho32bE1ML+S+//MLgwYOrbp8893vSpEm8/fbbPPLIIxQUFPCnP/2J7OxsLrnkEr799ltcXFzMiiwi9exoegH/WnOIT2KPU1ZRef2yvi39mH55W2Ja+6uIi4iISKN1TY8wQrxc+NO7sWw7ls2Y139m4eQ+uipMM2JqIR80aBCGYZxxvsVi4ZlnnuGZZ56px1Qi0hDsPJ7DgtWH+GZXctV1xAe09uf+y9vSv5W/ueFERERE6ki/Vv58dt8Ablu4mYTMQsa+vo7XJ/Tikra6Vnlz0GDPIReR5scwDNYdymD+qkOsPZheNX1IhyDuG9Sa3tEaNV1ERESantaBHiy5bwB/ejeW2Pgsbv3PRh4b1YnbL47W0YBNnAq5iJiuwm7w3e4UFqw+xI7jOQDYrBau7h7K3Ze1okOIriEuIiIiTZu/hzPv39mPx5bs4tMtx/nbV3vYk5TL38d00WBvTZgKuYiYpqi0gs+2HufNn45wJL0AABdHKzf2ieSOS1oS4edmckIRERGR+uPiaOMf13ejc6gXf18Wx6dbjnMwLZ9/TexFiLfG0WqKVMhF5IJJSEggPT39lOkZhRV8c7CA7w8Xkl9aeYK4h5OFkW3cubKNG94uJaQd3Uva0XoOfJ7i4uLMjiAiIiI0/r/JPdzg8YG+/GN9FtuPZTNszkoe7O9D9+BTR2APCAggMjLShJRSF1TIReSCSEhIoEPHjhQVFlZNcwppg2fva3DvMBCLrfLXT1l2Cnm/fEnCju/ZXVbMP8wKXIfy8/PNjiAiItIs5WamATBx4kSTk9QNB+9gAsc8Rm5wK55alU7Ozx+Qs+4jMOxVy7i6ubE3Lk6lvJFSIReRCyI9PZ2iwkJufvRFKgLacCDPRkaJtWp+gLOdNp4VhEb4Yek2GZhsVtQ6E7dpNd+8M5fi4mKzo4iIiDRLRfm5AIy6+zHad+tlcpq6UWGHbVkVHC2w4XPJBNoNvYk+/uW42CA14RDvPz+T9PR0FfJGSoVcRC6InOIKvPqNY6utA4XplaODWi3QLtiTHhE+BHs1vfOgUhMOmR1BREREAP/QKMLbdjY7Rp2JAuKSc1mx9wQniq2sTHNlWKdggs0OJudNhVxE6oxhGMTGZ/Hehni+2nEC30G3UVhROVBb1zBvuoX74OGsXzsiIiIi56pjCy+CPJ1ZtjOFzMJSPt+WRFtPG9j03qox06snIuctv6Scz7cm8t6GePam5FVNL0nax4AurenfrT0ONusfrEFEREREzsbfw5kb+0aw9kA6OxJzOJBno8WtL5OQU0ZPs8NJraiQi0itxSXnsmhjAku2JpJfUg5U7g2/unsovX2KGH/FQ0TP+0xlXERERKSOONqsDO4QRFSAG9/vTIKglsz8IZ1U6yHuHNgKm9VidkQ5ByrkInJOcorK+HJ7Eh9vPsbOxJyq6a0C3JnQP4rreobj7ebIli1bTEwpIiIi0rS1CvBgaIsyPlmzA1r3ZvY3e/lmVwr/uL4bbYI8zY4nNaRCLiJnZbcbbDiSwcebj/HNrhRKyisvteFos3BFp2Am9ItiQGt/LBZ9IisiIiJSX1xscOKTp3jhk5/4744Cth3L5spX1/Lg0HbcNbCljlJsBFTIReSMknOK+DT2OB//cpyEzP9dT7xdsAc39I5gzEVh+Hs4m5hQRERERC5v6cbEob2Y9dlOVu1L4/lv9/Ll9iT+b0wXLor0NTue/AEVcpF6lJCQQHp6utkx/lBRmZ31x4tZE1/EzhOlGL9Od3WwcEmkK0NbutLGzxGLJZv4/dnEn2E9cXFx9RVZREREpNlr4e3Kwsl9+CT2OM9+HUdcci5j56/j5r6RPDK8A95ujmZHlNNQIRepJwkJCXTo2JGiwsKzL1zfLFZcWvbEo/MgXNv2x+r4v2uEFyfsJH/HDxTu/5m9ZSW8eY6rzs/Pr9usIiIiInJaFouF63tHMKRDEP+3bC+fbjnO+xsT+G53Cn8e2ZGxF4Vh1aBvDYoKuUg9SU9Pp6iwkAmPvkhwZGuz42AYkF1qIaHQyrECKyX2//1y9nAwiHSvINLdjntke7ikPTD1nNYft2k137wzl+Li4jpOLiIiIiJ/xN/DmZdu6M51vcL56+c7OZRWwMOLt/Pu+qM8MboTvaL8zI4ov1IhF6lnwZGtCW/b2bTHT88v4UBqPvtT88guKqua7upoo12wBx1aeBHs6XzeA7SlJhw636giIiIich5iWvvzzQOX8p+fj/DPFQfZfjyHcfPXc3X3UB4d2YEwH1ezIzZ7KuQizUBWQSn7U/PYfyKfzILSquk2q4XWAe50aOFFpJ+brlspIiIi0sQ4OVi557LWjO0Zxkvf7efj2GN8uT2Jb3enMHlANPde1hpfdyezYzZbKuQiTVROURn7U/M4kJpPWn5J1XSbxUKUvxttgz1oFeCBk4MuhyEiIiLS1AV5uvD8dd24JSaKv321h41HMnljzWE+2JjAPYNac9vF0bg5qR7WN/3ERZoIwzBIzy/lUFo+h9LySc//355wqwUi/NxoF+RJ60B3nB1tJiYVEREREbN0CfPmwz/1Z9X+NF74dh9xybm8+N0+Fv58lLsvbcWE/pEq5vVIP2mRRswwDJJzin8t4QXk/OaccIsFwn1caRfsSesgD1xVwkVERESEytHYB7cP4rK2gSzdkcQ/vt/Hscwi/r4sjgWrD/GnS1sxsX8U7s6qixeafsIijUyF3eB4ViEH0/I5nFZAYWlF1Tyb1UKUnxutAz1oGeiuEi4iIiIiZ2S1WrimRxgju7Rgydbj/HPlQY5lFjH7m738a81hbo2J4taYaPx0jvkFo0Iu0giUltuJzyjgUFoBRzIKKC23V81zcrDSMsCd1oHuRPu742jTOeEiIiIiUnNODlbG94lkbM9wvtiWxD9XHOBoRiGv/HiABasPcX2vCO4c2JIof3ezozY5KuQiDVRWYSlH0ws4mlFIYlYRFYZRNc/NyUarQHfaBHoQ7qvR0UVERETk/DnarFzXK5xre4Tyza4U3lhzmJ2JOby7IZ73NsZzRcdgJg2IZkBr//O+RK5UUiEXaSDKK+wkZhdxNL2QIxnVzwcH8HZ1pHWgO60DPWjh7aJfgiIiIiJyQTjYrIzuHspV3Vqw/nAG/1p9mNX70/h+Tyrf70mlTZAHk2KiGNMzHA+dZ35e9NMTMVFecRlH0ws5mlFAQmYh5fb/7QW3WiDMx5XoAHda+rvj4+aoEi4iIiIi9cZisTCgdQADWgdwIDWP/66P59Mtxzl4Ip/Hv9jNc9/sZXT3UG7oE8FFET56r1oLKuQi9cnmwIliC/EH0zmSUUDGby5NBuDubCPav/Jc8Eg/N10jXEREREQahLbBnvzt2i7MHNGez2KP898N8RxOK+DDzcf4cPMx2gV7cEPvCK7uEUqQp4vZcRsNFXKRC8gwDPal5rH2QDpfx2YScf+H/HTCEcgCwAKEeLsQ7e9OywB3Ajyc9MmiiIiIiDRYXi6OTL64JZMGRLP5aBYfbk5g2c5k9qfm8+zXcfzfsjgGtg1kzEVhDOscrGuan4V+OiJ1LCWnmLUH01l7II21BzNIzy+pmmd1csHZahAd5EW0vxtR/ro0mYiIiIicn7i4OFMe1wGY2AbGRAbyU0IRq+KL2J9Rxur9aazen4azzULPFs70D3ehZ4gz7k51c/RnQEAAkZGRdbIus6mQi5yn5JwiNhzOYOPhTDYczuBoRmG1+a6ONvq29KOlazH/d/+tTH3qZSLahZiUVkRERESaitzMNAAmTpxocpL/cfBpgXvnwbh3HgS+oaw/Xsz648UYFWUUH91O4f51FB7ciL0wp9aP4ermxt64uCZRylXIRc5RUnZlAd9wOIONRzKJ/10Bt1qga5g3l7QN4JI2gfSM8sHZwcaWLVt4Oj0eHZEuIiIiInWhKD8XgFF3P0b7br1MTlOdYUB2aRmJRVYSC63k44hr6964tu6NPwYBzgahrnZC3ey4n0MrTU04xPvPzyQ9PV2FXKSpK6uwszc5j9j4TGITstkSn0VidlG1ZWxWC11Cvejfyp/+rfzpFe2Ll4ujSYlFREREpLnxD40ivG1ns2OcIgLo+uv3mQWlHDyRz6G0fE7klZBeYiG9xMqO7MrL+0b6uRHp50aEryvOzeiUThVykd/ILChla0IWWxKyiI3PYvuxHIrKKqotY7Na6BrmTf9W/vRr5UfvKF88VcBFRERERM7Iz92Jvi396NvSj9yiMg6l5XMorYDknCJyisrYmZjDzsQcLBYI8XKpKujBXi7YrE33EFMVcmm2sgtL2ZmYw47jOez69d/f7/2Gyk/sekb60CvKl55RvnQP98HdWf91RERERERqw8vVkYsifbko0peS8goSs4pIyCwkPrOQ7MIyknOKSc4pZuORTJxsVlr4uBDq40qYjysVhtnp65ZahTR5hmGQklvMvpQ84pLz2JmYzc7EHI5lnlq+AVoFutM7ypdev361CvDA2oQ/lRMRERERMYuzg41WgR60CvQAILeojITMQhIyCzmWWUhxuZ34jMKqcZusOOJ/5YNmRq5TKuTSpOQUlbE/NY+9KXnsS8llf0o+e1NyyS0uP+3yUf5udA3zplu4N13CKr90/reIiIiIiDm8XB2r3pfbDYP0/BKSsotJzC4iKbuIwtIKDPvp39s3Rirk0uiUlFeQkFHIkfQCjmYUcCS98utoeiEpucWnvY/NaqFlgDvtgz3pGu5N1zBvuoR64+2m8i0iIiIi0hBZLRaCPF0I8nShR4QPhmGwN24PC9/4FJhsdrw6oUIuDU5puZ3U3P99CpaUXURidhHHs4o4kl5AUnYR9j84dyTU24X2IZ60D/GifYgH7YO9aBXojkszGq1RRERERKSpsVgseDpCeVaS2VHqjAq51JvisgrS8kpIzy/59d9S0vNLqr6Sc4pJyi7iRF4JxlkGa/BwdqBlgDvRAe609HejZaA70f7utAr0wNtVe71FRERERKThUyFvQBISEkhPTzc7xinKKgxKKwyKyw1KKgxKyg2Kf/235NfphWV2Ckrt5JcaFJTZKSg1yP/NtPxSO0XlNR8S0dEKAW42At1sBPz6Fehmo4WnA6GeNrydrVgsFsAACsAowEiHQw3vx1clLi7O7AgiIiIiItKAqJA3EN9u3sv4ibdSWlqOxWoDqw2LzaHyX4sVbA7/m261gdUBi9X66+2T3/9+GRsWmw0sp1/X/5Z1AKv1f+u1OWJxdMbq6IzF0aXyvnXEKC+loiD7168sKgqzsRdkVd7Oz6Q89wTluWnYC3M4WGeP2rDk5+ebHUFERERERBqARlHI582bx4svvkhKSgrdu3fntddeo2/fvmbHqlOPf3MU3zFPmh3jD1kwsFnAwUrlv5bK2zZL5R5tJ6vxu3/B8TfTXGzgYAGLxQfwIW5TPN989hqj7n6M9t3Gmv30Lri4Tav55p25FBeffuA5ERERERFpXhp8If/oo4+YMWMGCxYsoF+/frzyyisMHz6cffv2ERQUZHa8OhPqaSMx4TCBLSJwdnHFarFgtYDVasFqsWCxgO23/1p/nW+xnLLsKdMtFqzW6uv4w/tZLTjaLDjarDhYK/91tFmxWvj1MPG6kZpwCAD/0CjC23aus/U2VCefr4iIiIiICDSCQj5nzhzuuusubrvtNgAWLFjA119/zX/+8x/+/Oc/m5yu7jx5mT+9ZgzjpnmfEd420uw4IiIiIiIicoE16EJeWlpKbGwss2bNqppmtVoZOnQo69evP+19SkpKKCkpqbqdk5MDQG5u7oUNe55Onld8/MBuSooKTU5TP07uMU45up9D7m4mp7nw9Hybvub2nJvb84Xm95z1fJu+5vac9Xybvub2nJvb8wVIO34EqOxPDbnjncxmnOXyURbjbEuYKCkpibCwMNatW0dMTEzV9EceeYTVq1ezcePGU+7z1FNP8fTTT9dnTBEREREREZFTHDt2jPDw8DPOb9B7yGtj1qxZzJgxo+q23W4nMzMTf3//Oj3/WWonNzeXiIgIjh07hpeXl9lxxATaBkTbgGgbENB2INoGpGlvA4ZhkJeXR2ho6B8u16ALeUBAADabjdTU1GrTU1NTCQkJOe19nJ2dcXZ2rjbNx8fnQkWUWvLy8mpy/+nk3GgbEG0Dom1AQNuBaBuQprsNeHt7n3UZaz3kqDUnJyd69erF8uXLq6bZ7XaWL19e7RB2ERERERERkcamQe8hB5gxYwaTJk2id+/e9O3bl1deeYWCgoKqUddFREREREREGqMGX8jHjx9PWloaTzzxBCkpKfTo0YNvv/2W4OBgs6NJLTg7O/Pkk0+eclqBNB/aBkTbgGgbENB2INoGRNsANPBR1kVERERERESaqgZ9DrmIiIiIiIhIU6VCLiIiIiIiImICFXIRERERERERE6iQi4iIiIiIiJhAhVzqXEVFBY8//jgtW7bE1dWV1q1b87e//Y3fjh9oGAZPPPEELVq0wNXVlaFDh3LgwAETU8v5WrNmDaNHjyY0NBSLxcLnn39ebX5NXvPMzEwmTJiAl5cXPj4+3HHHHeTn59fjs5Dz8UfbQFlZGY8++ihdu3bF3d2d0NBQbr31VpKSkqqtQ9tA43a23wO/dc8992CxWHjllVeqTdc20LjVZBuIi4vj6quvxtvbG3d3d/r06UNCQkLV/OLiYqZMmYK/vz8eHh6MGzeO1NTUenwWcj7Otg3k5+czdepUwsPDcXV1pVOnTixYsKDaMtoGGrfZs2fTp08fPD09CQoK4tprr2Xfvn3VlqnJa5yQkMCoUaNwc3MjKCiImTNnUl5eXp9PpV6okEude/7555k/fz7//Oc/iYuL4/nnn+eFF17gtddeq1rmhRde4NVXX2XBggVs3LgRd3d3hg8fTnFxsYnJ5XwUFBTQvXt35s2bd9r5NXnNJ0yYwO7du/nhhx/46quvWLNmDX/605/q6ynIefqjbaCwsJAtW7bw+OOPs2XLFj777DP27dvH1VdfXW05bQON29l+D5y0ZMkSNmzYQGho6CnztA00bmfbBg4dOsQll1xChw4dWLVqFTt27ODxxx/HxcWlapkHH3yQpUuXsnjxYlavXk1SUhJjx46tr6cg5+ls28CMGTP49ttvee+994iLi2P69OlMnTqVL7/8smoZbQON2+rVq5kyZQobNmzghx9+oKysjGHDhlFQUFC1zNle44qKCkaNGkVpaSnr1q3jnXfe4e233+aJJ54w4yldWIZIHRs1apRx++23V5s2duxYY8KECYZhGIbdbjdCQkKMF198sWp+dna24ezsbHzwwQf1mlUuDMBYsmRJ1e2avOZ79uwxAGPz5s1Vy3zzzTeGxWIxEhMT6y271I3fbwOns2nTJgMw4uPjDcPQNtDUnGkbOH78uBEWFmbs2rXLiIqKMl5++eWqedoGmpbTbQPjx483Jk6ceMb7ZGdnG46OjsbixYurpsXFxRmAsX79+gsVVS6Q020DnTt3Np555plq03r27Gk89thjhmFoG2iKTpw4YQDG6tWrDcOo2Wu8bNkyw2q1GikpKVXLzJ8/3/Dy8jJKSkrq9wlcYNpDLnVuwIABLF++nP379wOwfft21q5dy8iRIwE4cuQIKSkpDB06tOo+3t7e9OvXj/Xr15uSWS6smrzm69evx8fHh969e1ctM3ToUKxWKxs3bqz3zHLh5eTkYLFY8PHxAbQNNAd2u51bbrmFmTNn0rlz51Pmaxto2ux2O19//TXt2rVj+PDhBAUF0a9fv2qHNMfGxlJWVlbt70WHDh2IjIzUe4QmYsCAAXz55ZckJiZiGAYrV65k//79DBs2DNA20BTl5OQA4OfnB9TsNV6/fj1du3YlODi4apnhw4eTm5vL7t276zH9hadCLnXuz3/+MzfeeCMdOnTA0dGRiy66iOnTpzNhwgQAUlJSAKr9Bzt5++Q8aVpq8pqnpKQQFBRUbb6DgwN+fn7aLpqg4uJiHn30UW666Sa8vLwAbQPNwfPPP4+DgwP333//aedrG2jaTpw4QX5+Ps899xwjRozg+++/Z8yYMYwdO5bVq1cDlduAk5NT1Qd1J+k9QtPx2muv0alTJ8LDw3FycmLEiBHMmzePSy+9FNA20NTY7XamT5/OxRdfTJcuXYCavcYpKSmnfd94cl5T4mB2AGl6Pv74Y95//30WLVpE586d2bZtG9OnTyc0NJRJkyaZHU9ETFZWVsYNN9yAYRjMnz/f7DhST2JjY5k7dy5btmzBYrGYHUdMYLfbAbjmmmt48MEHAejRowfr1q1jwYIFXHbZZWbGk3ry2muvsWHDBr788kuioqJYs2YNU6ZMITQ0tNoeU2kapkyZwq5du1i7dq3ZURos7SGXOjdz5syqveRdu3bllltu4cEHH2T27NkAhISEAJwykmJqamrVPGlaavKah4SEcOLEiWrzy8vLyczM1HbRhJws4/Hx8fzwww9Ve8dB20BT99NPP3HixAkiIyNxcHDAwcGB+Ph4HnroIaKjowFtA01dQEAADg4OdOrUqdr0jh07Vo2yHhISQmlpKdnZ2dWW0XuEpqGoqIi//OUvzJkzh9GjR9OtWzemTp3K+PHj+cc//gFoG2hKpk6dyldffcXKlSsJDw+vml6T1zgkJOS07xtPzmtKVMilzhUWFmK1Vt+0bDZb1SfjLVu2JCQkhOXLl1fNz83NZePGjcTExNRrVqkfNXnNY2JiyM7OJjY2tmqZFStWYLfb6devX71nlrp3sowfOHCAH3/8EX9//2rztQ00bbfccgs7duxg27ZtVV+hoaHMnDmT7777DtA20NQ5OTnRp0+fUy5/tH//fqKiogDo1asXjo6O1f5e7Nu3j4SEBL1HaALKysooKyv7w/eJ2gYaP8MwmDp1KkuWLGHFihW0bNmy2vyavMYxMTHs3Lmz2oe0Jz/I//2Heo2dDlmXOjd69Gj+/ve/ExkZSefOndm6dStz5szh9ttvB8BisTB9+nSeffZZ2rZtS8uWLXn88ccJDQ3l2muvNTe81Fp+fj4HDx6sun3kyBG2bduGn58fkZGRZ33NO3bsyIgRI7jrrrtYsGABZWVlTJ06lRtvvPG0l0aShuePtoEWLVpw3XXXsWXLFr766isqKiqqzgHz8/PDyclJ20ATcLbfA7//EMbR0ZGQkBDat28P6PdAU3C2bWDmzJmMHz+eSy+9lMGDB/Ptt9+ydOlSVq1aBVQO+HnHHXcwY8YM/Pz88PLyYtq0acTExNC/f3+TnpWci7NtA5dddhkzZ87E1dWVqKgoVq9ezX//+1/mzJkDaBtoCqZMmcKiRYv44osv8PT0rPp77+3tjaura41e42HDhtGpUyduueUWXnjhBVJSUvjrX//KlClTcHZ2NvPp1T2TR3mXJig3N9d44IEHjMjISMPFxcVo1aqV8dhjj1W7RIHdbjcef/xxIzg42HB2djYuv/xyY9++fSamlvO1cuVKAzjla9KkSYZh1Ow1z8jIMG666SbDw8PD8PLyMm677TYjLy/PhGcjtfFH28CRI0dOOw8wVq5cWbUObQON29l+D/ze7y97ZhjaBhq7mmwDb731ltGmTRvDxcXF6N69u/H5559XW0dRUZFx3333Gb6+voabm5sxZswYIzk5uZ6fidTW2baB5ORkY/LkyUZoaKjh4uJitG/f3njppZcMu91etQ5tA43bmf7eL1y4sGqZmrzGR48eNUaOHGm4uroaAQEBxkMPPWSUlZXV87O58CyGYRgXtPGLiIiIiIiIyCl0DrmIiIiIiIiICVTIRUREREREREygQi4iIiIiIiJiAhVyEREREREREROokIuIiIiIiIiYQIVcRERERERExAQq5CIiIiIiIiImUCEXERERERERMYEKuYiISCO1atUqLBYL2dnZ57We6OhoXnnllTrJBDBo0CCmT5/+h8tYLBY+//zzOntMERGRxkiFXEREpJGoSdEVERGRxkOFXERERERERMQEKuQiIiKNwOTJk1m9ejVz587FYrFgsVg4evQoALGxsfTu3Rs3NzcGDBjAvn37qu536NAhrrnmGoKDg/Hw8KBPnz78+OOPf/hYc+bMoWvXrri7uxMREcF9991Hfn5+tWV+/vlnBg0ahJubG76+vgwfPpysrKyq+Xa7nUceeQQ/Pz9CQkJ46qmnTnmc9PR0xowZg5ubG23btuXLL7+s/Q9IRESkEVIhFxERaQTmzp1LTEwMd911F8nJySQnJxMREQHAY489xksvvcQvv/yCg4MDt99+e9X98vPzufLKK1m+fDlbt25lxIgRjB49moSEhDM+ltVq5dVXX2X37t288847rFixgkceeaRq/rZt27j88svp1KkT69evZ+3atYwePZqKioqqZd555x3c3d3ZuHEjL7zwAs888ww//PBDtcd5+umnueGGG9ixYwdXXnklEyZMIDMzs65+ZCIiIg2exTAMw+wQIiIicnaDBg2iR48eVQOwrVq1isGDB/Pjjz9y+eWXA7Bs2TJGjRpFUVERLi4up11Ply5duOeee5g6dSpQOajb9OnTz3h++ieffMI999xDeno6ADfffDMJCQmsXbv2jDkrKir46aefqqb17duXIUOG8NxzzwGVg7r99a9/5W9/+xsABQUFeHh48M033zBixIhz+8GIiIg0UtpDLiIi0sh169at6vsWLVoAcOLECaByD/nDDz9Mx44d8fHxwcPDg7i4uD/cQ36y4IeFheHp6cktt9xCRkYGhYWFwP/2kNc008lcJzOdbhl3d3e8vLxOWUZERKQpUyEXERFp5BwdHau+t1gsQOU53AAPP/wwS5Ys4f/+7//46aef2LZtG127dqW0tPS06zp69ChXXXUV3bp149NPPyU2NpZ58+YBVN3H1dX1nDKdzHUy07ksIyIi0pSpkIuIiDQSTk5O1c7Tromff/6ZyZMnM2bMGLp27UpISEjVYHCnExsbi91u56WXXqJ///60a9eOpKSkast069aN5cuX1+YpiIiIyG+okIuIiDQS0dHRbNy4kaNHj5Kenl6jvclt27bls88+Y9u2bWzfvp2bb775D+/Xpk0bysrKeO211zh8+DDvvvsuCxYsqLbMrFmz2Lx5M/fddx87duxg7969zJ8/v+occxEREakZFXIREZFG4uGHH8Zms9GpUycCAwP/8Dzwk+bMmYOvry8DBgxg9OjRDB8+nJ49e55x+e7duzNnzhyef/55unTpwvvvv8/s2bOrLdOuXTu+//57tm/fTt++fYmJieGLL77AwcHhvJ+jiIhIc6JR1kVERERERERMoD3kIiIiIiIiIiZQIRcRERERERExgQq5iIiIiIiIiAlUyEVERERERERMoEIuIiIiIiIiYgIVchERERERERETqJCLiIiIiIiImECFXERERERERMQEKuQiIiIiIiIiJlAhFxERERERETGBCrmIiIiIiIiICf4fomVoQZfV+UQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     output 0: \n",
      "            exng 0 = 62 -> 44.93%\n",
      "            exng 1 = 76 -> 55.07%\n",
      "\n",
      "     output 1: \n",
      "            exng 0 = 141 -> 85.98%\n",
      "            exng 1 = 23 -> 14.02%\n",
      "\n",
      "     GERAL: \n",
      "            exng 0 e output 0 = 62 -> 20.53%\n",
      "            exng 0 e output 1 = 141 -> 46.69%\n",
      "            exng 1 e output 0 = 76 -> 25.17%\n",
      "            exng 1 e output 1 = 23 -> 7.616%\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     output 0: \n",
      "            slp 0 = 12 -> 8.696%\n",
      "            slp 1 = 91 -> 65.94%\n",
      "            slp 2 = 35 -> 25.36%\n",
      "\n",
      "     output 1: \n",
      "            slp 0 = 9 -> 5.488%\n",
      "            slp 1 = 49 -> 29.88%\n",
      "            slp 2 = 106 -> 64.63%\n",
      "\n",
      "     GERAL: \n",
      "            slp 0 e output 0 = 12 -> 3.974%\n",
      "            slp 0 e output 1 = 9 -> 2.98%\n",
      "            slp 1 e output 0 = 91 -> 30.13%\n",
      "            slp 1 e output 1 = 49 -> 16.23%\n",
      "            slp 2 e output 0 = 35 -> 11.59%\n",
      "            slp 2 e output 1 = 106 -> 35.1%\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     output 0: \n",
      "            caa 0 = 45 -> 32.61%\n",
      "            caa 1 = 44 -> 31.88%\n",
      "            caa 2 = 31 -> 22.46%\n",
      "            caa 3 = 17 -> 12.32%\n",
      "            caa 4 = 1 -> 0.7246%\n",
      "\n",
      "     output 1: \n",
      "            caa 0 = 130 -> 79.27%\n",
      "            caa 1 = 21 -> 12.8%\n",
      "            caa 2 = 7 -> 4.268%\n",
      "            caa 3 = 3 -> 1.829%\n",
      "            caa 4 = 3 -> 1.829%\n",
      "\n",
      "     GERAL: \n",
      "            caa 0 e output 0 = 45 -> 14.9%\n",
      "            caa 0 e output 1 = 130 -> 43.05%\n",
      "            caa 1 e output 0 = 44 -> 14.57%\n",
      "            caa 1 e output 1 = 21 -> 6.954%\n",
      "            caa 2 e output 0 = 31 -> 10.26%\n",
      "            caa 2 e output 1 = 7 -> 2.318%\n",
      "            caa 3 e output 0 = 17 -> 5.629%\n",
      "            caa 3 e output 1 = 3 -> 0.9934%\n",
      "            caa 4 e output 0 = 1 -> 0.3311%\n",
      "            caa 4 e output 1 = 3 -> 0.9934%\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     output 0: \n",
      "            thall 0 = 1 -> 0.7246%\n",
      "            thall 1 = 12 -> 8.696%\n",
      "            thall 2 = 36 -> 26.09%\n",
      "            thall 3 = 89 -> 64.49%\n",
      "\n",
      "     output 1: \n",
      "            thall 0 = 1 -> 0.6098%\n",
      "            thall 1 = 6 -> 3.659%\n",
      "            thall 2 = 129 -> 78.66%\n",
      "            thall 3 = 28 -> 17.07%\n",
      "\n",
      "     GERAL: \n",
      "            thall 0 e output 0 = 1 -> 0.3311%\n",
      "            thall 0 e output 1 = 1 -> 0.3311%\n",
      "            thall 1 e output 0 = 12 -> 3.974%\n",
      "            thall 1 e output 1 = 6 -> 1.987%\n",
      "            thall 2 e output 0 = 36 -> 11.92%\n",
      "            thall 2 e output 1 = 129 -> 42.72%\n",
      "            thall 3 e output 0 = 89 -> 29.47%\n",
      "            thall 3 e output 1 = 28 -> 9.272%\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in ds_copy.columns:\n",
    "    plt.figure(figsize=(12,4))\n",
    "    plt.title(f'Coluna avaliada: {c}')\n",
    "    if c in categoricas:\n",
    "        sns.countplot(x = ds_copy[c], hue= ds.output)\n",
    "        plt.show()\n",
    "\n",
    "        n = ds_copy[c].loc[ds_copy.output == 0].value_counts()\n",
    "        y = ds_copy[c].loc[ds_copy.output == 1].value_counts()\n",
    "\n",
    "        print('     output 0: ')\n",
    "        for i in range(len(n)):\n",
    "            print(f'            {c} '+str(i)+ ' = '+str(n[i])+' -> {:.4}'.format((n[i]/len(ds_copy[c].loc[ds_copy.output==0]))*100)+'%')\n",
    "        print('')\n",
    "        print('     output 1: ')\n",
    "        for i in range(len(y)):\n",
    "            print(f'            {c} '+str(i)+' = '+str(y[i])+' -> {:.4}'.format((y[i]/len(ds_copy[c].loc[ds_copy.output==1]))*100)+'%')\n",
    "        print('')\n",
    "        print('     GERAL: ')\n",
    "        for i in range(len(n)):\n",
    "            print(f'            {c} '+str(i)+' e output 0 = '+str(n[i])+' -> {:.4}'.format((n[i]/len(ds_copy[c]))*100)+'%')\n",
    "            print(f'            {c} '+str(i)+' e output 1 = '+str(y[i])+' -> {:.4}'.format((y[i]/len(ds_copy[c]))*100)+'%')\n",
    "        print('')\n",
    "\n",
    "    if c in numericas:\n",
    "        sns.histplot(ds_copy[c], kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análise:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### caa: existem entradas incorrentas, segundo o link da kaggle para este dataset, caa deveria se comportar de 0 a 3, porém observamos que há um tipo de entrada incorreta, trataremos isto:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.caa.loc[ds.caa == 4] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### tha: existem entradas incorrentas, segundo o link da kaggle para este dataset, thall deveria se comportar de 1 a 3, porém observamos que há um tipo de entrada incorreta, trataremos isto:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.thall.loc[ds.thall == 0] = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Podemos observar outliers nas numericas com os histogramas, para evidenciar confira o boxplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,8))\n",
    "sns.boxplot(data = ds_copy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tratamento dos outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### trtbps: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    302.000000\n",
       "mean     131.258278\n",
       "std       16.605232\n",
       "min       94.000000\n",
       "25%      120.000000\n",
       "50%      130.000000\n",
       "75%      140.000000\n",
       "max      170.000000\n",
       "Name: trtbps, dtype: float64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.boxplot(ds_copy.trtbps)\n",
    "plt.show\n",
    "\n",
    "# Tratamento dos outliers\n",
    "#ds.trtbps.quantile(0.97)\n",
    "ds.trtbps.loc[ds.trtbps > 170] = 170\n",
    "\n",
    "# Plotando novo boxplot após o tratamento de outliers\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.boxplot(ds.trtbps)\n",
    "plt.show()\n",
    "\n",
    "ds.trtbps.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### chol: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    302.000000\n",
       "mean     245.205298\n",
       "std       47.049535\n",
       "min      126.000000\n",
       "25%      211.000000\n",
       "50%      240.500000\n",
       "75%      274.750000\n",
       "max      360.000000\n",
       "Name: chol, dtype: float64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Há presença de outliers\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.boxplot(ds_copy.chol)\n",
    "plt.show()\n",
    "\n",
    "# Tratando os outliers\n",
    "#ds_copy.chol.quantile(0.98338871)\n",
    "ds.chol.loc[ds.chol > 360] = 360\n",
    "\n",
    "# Plotagem com outliers tratados\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.boxplot(ds.chol)\n",
    "plt.show()\n",
    "\n",
    "ds.chol.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### thalachh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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85V/+ZV/dBHbTK62BZ599NitWrMjcuXOzYsWKfOc738nq1atz1lln9djPGujfdvZzYJtFixblvvvuS0dHx8u2WQP9287WwG9/+9v8xV/8RY499tjceeedefDBBzN37ty0tLTU97nkkkvyve99L9/61reyZMmSrF27NmeffXZf3QR2087WwKWXXpof/vCH+fd///esWrUqs2fPzqxZs3LTTTfV97EG+rclS5Zk5syZue+++3LLLbekq6srZ5xxRp555pn6Pjs7xlu3bs2ZZ56Z559/Pvfee2+uv/76/Ou//ms+97nPNeIm7V0V/caZZ55ZXXjhhT0uO/vss6vp06dXVVVV3d3dVXt7e/WlL32pvn3Dhg1Vc3Nz9c1vfrNPZ2XvSFItWrSo/nlvjvl///d/V0mq+++/v77P4sWLq1qtVv3hD3/os9nZM166Brbnpz/9aZWkeuyxx6qqsgZKs6M18Pvf/7467LDDql/+8pfV4YcfXn3lK1+pb7MGyrK9NXDOOedUH/rQh3Z4nQ0bNlQDBw6svvWtb9UvW7VqVZWkWrp06d4alb1ke2vg+OOPrz7/+c/3uOxNb3pTdfnll1dVZQ2UaP369VWSasmSJVVV9e4Y/+AHP6gGDBhQrVu3rr7PddddV7W1tVVbtmzp2xuwlzmj249MmDAht912Wx5++OEkyQMPPJB77rknkydPTpI88sgjWbduXU4//fT6dYYPH55TTjklS5cubcjM7F29OeZLly7N/vvvn7e85S31fU4//fQMGDAgy5Yt6/OZ2fv++Mc/plarZf/9909iDewLuru7M2PGjMyZMyfHH3/8y7ZbA2Xr7u7O97///RxzzDGZOHFiDj744Jxyyik9ntq6fPnydHV19bi/OPbYYzN69GiPEQoxYcKE3HTTTfnDH/6Qqqpyxx135OGHH84ZZ5yRxBoo0R//+MckyYgRI5L07hgvXbo0Y8eOzSGHHFLfZ+LEidm4cWMeeuihPpx+7xO6/chf//Vf59xzz82xxx6bgQMH5o1vfGNmz56d6dOnJ0nWrVuXJD0W7rbPt22jLL055uvWrcvBBx/cY3tTU1NGjBhhXRSos7Mzn/70p3Peeeelra0tiTWwL7jyyivT1NSUv/qrv9rudmugbOvXr8/mzZvzxS9+MZMmTcqPf/zjTJ06NWeffXaWLFmS5E9rYNCgQfVfgG3jMUI5rrnmmowZMyYjR47MoEGDMmnSpFx77bV5+9vfnsQaKE13d3dmz56dP//zP88JJ5yQpHfHeN26ddt93LhtW0maGj0Avfef//mfueGGG7Jw4cIcf/zxWblyZWbPnp2Ojo6cf/75jR4PaLCurq588IMfTFVVue666xo9Dn1k+fLlufrqq7NixYrUarVGj0MDdHd3J0ne+9735pJLLkmSnHzyybn33nuzYMGCvOMd72jkePSRa665Jvfdd19uuummHH744bnrrrsyc+bMdHR09DjDRxlmzpyZX/7yl7nnnnsaPcprljO6/cicOXPqZ3XHjh2bGTNm5JJLLskVV1yRJGlvb0+Sl72z2hNPPFHfRll6c8zb29uzfv36HttfeOGFPP3009ZFQbZF7mOPPZZbbrmlfjY3sQZKd/fdd2f9+vUZPXp0mpqa0tTUlMceeyyf/OQnc8QRRySxBkp30EEHpampKWPGjOlx+XHHHVd/1+X29vY8//zz2bBhQ499PEYow3PPPZe/+Zu/yfz58zNlypSceOKJmTVrVs4555x8+ctfTmINlGTWrFm5+eabc8cdd2TkyJH1y3tzjNvb27f7uHHbtpII3X7k2WefzYABPQ/ZfvvtV/9N7pFHHpn29vbcdttt9e0bN27MsmXLMn78+D6dlb7Rm2M+fvz4bNiwIcuXL6/vc/vtt6e7uzunnHJKn8/Mnrctcn/961/n1ltvzYEHHthjuzVQthkzZuTBBx/MypUr6386OjoyZ86c/OhHP0piDZRu0KBBGTdu3Mv+m5GHH344hx9+eJLkzW9+cwYOHNjj/mL16tVZs2aNxwgF6OrqSldX1ys+TrQG+r+qqjJr1qwsWrQot99+e4488sge23tzjMePH59f/OIXPX75ue0X5C/9ZVl/56nL/ciUKVPyj//4jxk9enSOP/74/PznP8/8+fNz4YUXJklqtVpmz56df/iHf8jRRx+dI488MnPnzk1HR0fe9773NXZ4dtnmzZvzm9/8pv75I488kpUrV2bEiBEZPXr0To/5cccdl0mTJuVjH/tYFixYkK6ursyaNSvnnnvudv8LEl57XmkNHHroofnABz6QFStW5Oabb87WrVvrr7EZMWJEBg0aZA0UYGc/B176y42BAwemvb09b3jDG5L4OVCCna2BOXPm5Jxzzsnb3/72vPOd78wPf/jDfO9738udd96Z5E9vVPjRj340l156aUaMGJG2trZ8/OMfz/jx4/O2t72tQbeKV2Nna+Ad73hH5syZk9bW1hx++OFZsmRJvvGNb2T+/PlJrIESzJw5MwsXLsx3v/vdDBs2rH5/P3z48LS2tvbqGJ9xxhkZM2ZMZsyYkXnz5mXdunX57Gc/m5kzZ6a5ubmRN2/Pa/C7PvMqbNy4sfrEJz5RjR49umppaale//rXV5dffnmPtwLv7u6u5s6dWx1yyCFVc3Nzddppp1WrV69u4NTsrjvuuKNK8rI/559/flVVvTvmTz31VHXeeedVQ4cOrdra2qqPfOQj1aZNmxpwa9gVr7QGHnnkke1uS1Ldcccd9a9hDfRvO/s58FIv/e+Fqsoa6O96swb++Z//uTrqqKOqlpaW6qSTTqpuvPHGHl/jueeeqy6++OLqgAMOqAYPHlxNnTq1evzxx/v4lrCrdrYGHn/88eqCCy6oOjo6qpaWluoNb3hDddVVV1Xd3d31r2EN9G87ur//l3/5l/o+vTnGjz76aDV58uSqtbW1Ouigg6pPfvKTVVdXVx/fmr2vVlVVtVdLGgAAAPqQ1+gCAABQFKELAABAUYQuAAAARRG6AAAAFEXoAgAAUBShCwAAQFGELgAAAEURugDwEnfeeWdqtVo2bNiwW1/niCOOyFe/+tU9MlOSnHrqqZk9e/Yr7lOr1XLjjTfuse8JAP2R0AVgn9ebgAQA+g+hCwAAQFGELgD7tAsuuCBLlizJ1VdfnVqtllqtlkcffTRJsnz58rzlLW/J4MGDM2HChKxevbp+vd/+9rd573vfm0MOOSRDhw7NuHHjcuutt77i95o/f37Gjh2bIUOGZNSoUbn44ouzefPmHvv85Cc/yamnnprBgwfngAMOyMSJE/O///u/9e3d3d351Kc+lREjRqS9vT1/93d/97Lv8+STT2bq1KkZPHhwjj766Nx00027/hcEAP2Q0AVgn3b11Vdn/Pjx+djHPpbHH388jz/+eEaNGpUkufzyy3PVVVflZz/7WZqamnLhhRfWr7d58+a8+93vzm233Zaf//znmTRpUqZMmZI1a9bs8HsNGDAgX/va1/LQQw/l+uuvz+23355PfepT9e0rV67MaaedljFjxmTp0qW55557MmXKlGzdurW+z/XXX58hQ4Zk2bJlmTdvXj7/+c/nlltu6fF9/v7v/z4f/OAH8+CDD+bd7353pk+fnqeffnpP/ZUBwGteraqqqtFDAEAjnXrqqTn55JPrbxx155135p3vfGduvfXWnHbaaUmSH/zgBznzzDPz3HPPpaWlZbtf54QTTshFF12UWbNmJfnTm1HNnj17h6///a//+q9cdNFFefLJJ5Mk06ZNy5o1a3LPPffscM6tW7fm7rvvrl/21re+Ne9617vyxS9+Mcmf3ozqs5/9bL7whS8kSZ555pkMHTo0ixcvzqRJk17dXwwA9FPO6ALADpx44on1jw899NAkyfr165P86YzuZZddluOOOy77779/hg4dmlWrVr3iGd1t4XzYYYdl2LBhmTFjRp566qk8++yzSf7vjG5vZ9o217aZtrfPkCFD0tbW9rJ9AKBkQhcAdmDgwIH1j2u1WpI/vUY2SS677LIsWrQo//RP/5S77747K1euzNixY/P8889v92s9+uijec973pMTTzwx3/72t7N8+fJce+21SVK/Tmtr66uaadtc22Z6NfsAQMmELgD7vEGDBvV4HWxv/OQnP8kFF1yQqVOnZuzYsWlvb6+/idX2LF++PN3d3bnqqqvytre9Lcccc0zWrl3bY58TTzwxt912267cBADgRYQuAPu8I444IsuWLcujjz6aJ598sldnP48++uh85zvfycqVK/PAAw9k2rRpr3i9o446Kl1dXbnmmmvyP//zP/m3f/u3LFiwoMc+n/nMZ3L//ffn4osvzoMPPphf/epXue666+qv4QUAekfoArDPu+yyy7LffvtlzJgxed3rXveKr7PdZv78+TnggAMyYcKETJkyJRMnTsyb3vSmHe5/0kknZf78+bnyyitzwgkn5IYbbsgVV1zRY59jjjkmP/7xj/PAAw/krW99a8aPH5/vfve7aWpq2u3bCAD7Eu+6DAAAQFGc0QUAAKAoQhcAAICiCF0AAACKInQBAAAoitAFAACgKEIXAACAoghdAAAAiiJ0AQAAKIrQBQAAoChCFwAAgKIIXQAAAIoidAEAACjK/wOtrCNyztnWQAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    302.000000\n",
       "mean     149.612997\n",
       "std       22.765983\n",
       "min       84.125000\n",
       "25%      133.250000\n",
       "50%      152.500000\n",
       "75%      166.000000\n",
       "max      202.000000\n",
       "Name: thalachh, dtype: float64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "sns.boxplot(ds_copy.thalachh)\n",
    "\n",
    "# Tratamento do outlier\n",
    "# Limite Inferior do boxplot = Primeiro Quartil (– 1,5 * (Terceiro Quartil – Primeiro Quartil))\n",
    "limite_inferior = ( ds.thalachh.quantile(0.25)) -1.5 * (ds.thalachh.quantile(0.75)-ds.thalachh.quantile(0.25))\n",
    "# o limite_inferior é igual a 84.75\n",
    "# quase ds_copy.thalachh.quantile(0.00254)\n",
    "\n",
    "ds.thalachh.loc[ds.thalachh < limite_inferior] = limite_inferior\n",
    "# Outlier tratado\n",
    "\n",
    "# Plot novo boxplot\n",
    "plt.figure(figsize=(12, 4))\n",
    "sns.boxplot(ds.thalachh)\n",
    "plt.show()\n",
    "\n",
    "ds.thalachh.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers e Entradas-Incorretas devidamente tratadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,8))\n",
    "sns.boxplot(data = ds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### numericas = \n",
    "    ['age', 'trtbps', 'chol', 'thalachh', 'oldpeak']\n",
    "##### categoricas = \n",
    "    ['sex', 'cp', 'fbs', 'restecg', 'exng', 'slp', 'caa', 'thall']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>output</th>\n",
       "      <th>sex_0</th>\n",
       "      <th>sex_1</th>\n",
       "      <th>cp_0</th>\n",
       "      <th>cp_1</th>\n",
       "      <th>...</th>\n",
       "      <th>slp_0</th>\n",
       "      <th>slp_1</th>\n",
       "      <th>slp_2</th>\n",
       "      <th>caa_0</th>\n",
       "      <th>caa_1</th>\n",
       "      <th>caa_2</th>\n",
       "      <th>caa_3</th>\n",
       "      <th>thall_1</th>\n",
       "      <th>thall_2</th>\n",
       "      <th>thall_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>187.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>123.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>302 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  trtbps  chol  thalachh  oldpeak  output  sex_0  sex_1  cp_0  cp_1  \\\n",
       "0     63     145   233     150.0      2.3       1      0      1     0     0   \n",
       "1     37     130   250     187.0      3.5       1      0      1     0     0   \n",
       "2     41     130   204     172.0      1.4       1      1      0     0     1   \n",
       "3     56     120   236     178.0      0.8       1      0      1     0     1   \n",
       "4     57     120   354     163.0      0.6       1      1      0     1     0   \n",
       "..   ...     ...   ...       ...      ...     ...    ...    ...   ...   ...   \n",
       "298   57     140   241     123.0      0.2       0      1      0     1     0   \n",
       "299   45     110   264     132.0      1.2       0      0      1     0     0   \n",
       "300   68     144   193     141.0      3.4       0      0      1     1     0   \n",
       "301   57     130   131     115.0      1.2       0      0      1     1     0   \n",
       "302   57     130   236     174.0      0.0       0      1      0     0     1   \n",
       "\n",
       "     ...  slp_0  slp_1  slp_2  caa_0  caa_1  caa_2  caa_3  thall_1  thall_2  \\\n",
       "0    ...      1      0      0      1      0      0      0        1        0   \n",
       "1    ...      1      0      0      1      0      0      0        0        1   \n",
       "2    ...      0      0      1      1      0      0      0        0        1   \n",
       "3    ...      0      0      1      1      0      0      0        0        1   \n",
       "4    ...      0      0      1      1      0      0      0        0        1   \n",
       "..   ...    ...    ...    ...    ...    ...    ...    ...      ...      ...   \n",
       "298  ...      0      1      0      1      0      0      0        0        0   \n",
       "299  ...      0      1      0      1      0      0      0        0        0   \n",
       "300  ...      0      1      0      0      0      1      0        0        0   \n",
       "301  ...      0      1      0      0      1      0      0        0        0   \n",
       "302  ...      0      1      0      0      1      0      0        0        1   \n",
       "\n",
       "     thall_3  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  \n",
       "..       ...  \n",
       "298        1  \n",
       "299        1  \n",
       "300        1  \n",
       "301        1  \n",
       "302        0  \n",
       "\n",
       "[302 rows x 29 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1 = pd.get_dummies(ds, columns = ['sex', 'cp', 'fbs', 'restecg', 'exng', 'slp', 'caa', 'thall'])\n",
    "ds1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aprendizagem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function learn:\n",
    "\n",
    "        Dividir o dataset em subconjuntos de treinamento e teste;\n",
    "        test size = 33% e treino size = 67%;\n",
    "        Normatizar as Features numéricas com StandardScaler;\n",
    "        Invocar Algoritmos de Machine Learning;\n",
    "        Gerar Relatório mostrando as principais métricas de classificação;\n",
    "        Calcular a matriz de confusão para avaliar a precisão de uma classificação.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "def learn(dataset, algoritmo, opt = 2):\n",
    "    X = dataset.drop('output', axis=1)\n",
    "    y = dataset.output\n",
    "\n",
    "    # train_test_split\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)\n",
    "\n",
    "    # Padronização das features numéricas com StandardScaler;\n",
    "    # Os modelos Não Baseados em Árvores de Decisão se beneficiam mais deste tipo de padronização.\n",
    "    scaler = StandardScaler()\n",
    "\n",
    "    columns_scaler = ['age', 'trtbps', 'chol', 'thalachh', 'oldpeak']\n",
    "\n",
    "    X_train[columns_scaler] = scaler.fit_transform(X_train[columns_scaler])\n",
    "    X_test[columns_scaler] = scaler.transform(X_test[columns_scaler])\n",
    "    \n",
    "\n",
    "    if opt == 0:\n",
    "        ml = algoritmo(max_iter = 1000)\n",
    "    elif opt == 1:\n",
    "        ml = algoritmo(n_estimators = 1000)\n",
    "    elif opt == 2:\n",
    "        ml = algoritmo()\n",
    "\n",
    "    # training\n",
    "    ml.fit(X_train, y_train)\n",
    "    print('Acurácia:')\n",
    "    score_train = ml.score(X_train, y_train)\n",
    "    print('     Treino = {:.4}'.format(score_train*100)+'%')\n",
    "\n",
    "    score_test = ml.score(X_test, y_test)\n",
    "    print('     Teste = {:.4}'.format(score_test*100)+'%')\n",
    "\n",
    "    # predict\n",
    "\n",
    "    y_predict = ml.predict(X_test)\n",
    "    print('\\nClassification:\\n',classification_report(y_test, y_predict))\n",
    "    print('Confusion Matrix:')\n",
    "    confusion = confusion_matrix(y_test, y_predict)\n",
    "    sns.heatmap(confusion, annot=True, cmap='Blues')\n",
    "    \n",
    "    return score_train, score_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Invocando Algoritmos de Machine Learning:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     KNeighborsClassifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia:\n",
      "     Treino = 89.6%\n",
      "     Teste = 84.0%\n",
      "\n",
      "Classification:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.84      0.82        43\n",
      "           1       0.87      0.84      0.86        57\n",
      "\n",
      "    accuracy                           0.84       100\n",
      "   macro avg       0.84      0.84      0.84       100\n",
      "weighted avg       0.84      0.84      0.84       100\n",
      "\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kn_train, kn_test = learn(ds1, KNeighborsClassifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    LogisticRegression: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia:\n",
      "     Treino = 86.63%\n",
      "     Teste = 87.0%\n",
      "\n",
      "Classification:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.86      0.85        43\n",
      "           1       0.89      0.88      0.88        57\n",
      "\n",
      "    accuracy                           0.87       100\n",
      "   macro avg       0.87      0.87      0.87       100\n",
      "weighted avg       0.87      0.87      0.87       100\n",
      "\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_train, log_test = learn(ds1, LogisticRegression,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    DecisionTreeClassifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia:\n",
      "     Treino = 100.0%\n",
      "     Teste = 75.0%\n",
      "\n",
      "Classification:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.77      0.73        43\n",
      "           1       0.81      0.74      0.77        57\n",
      "\n",
      "    accuracy                           0.75       100\n",
      "   macro avg       0.75      0.75      0.75       100\n",
      "weighted avg       0.76      0.75      0.75       100\n",
      "\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_train, tree_test = learn(ds1, DecisionTreeClassifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    AdaBoostClassifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia:\n",
      "     Treino = 95.54%\n",
      "     Teste = 83.0%\n",
      "\n",
      "Classification:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.84      0.81        43\n",
      "           1       0.87      0.82      0.85        57\n",
      "\n",
      "    accuracy                           0.83       100\n",
      "   macro avg       0.83      0.83      0.83       100\n",
      "weighted avg       0.83      0.83      0.83       100\n",
      "\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada_train, ada_test = learn(ds1, AdaBoostClassifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    RandomForestClassifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia:\n",
      "     Treino = 100.0%\n",
      "     Teste = 85.0%\n",
      "\n",
      "Classification:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.86      0.83        43\n",
      "           1       0.89      0.84      0.86        57\n",
      "\n",
      "    accuracy                           0.85       100\n",
      "   macro avg       0.85      0.85      0.85       100\n",
      "weighted avg       0.85      0.85      0.85       100\n",
      "\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rand_train, rand_test = learn(ds1, RandomForestClassifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    MLPClassifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia:\n",
      "     Treino = 88.12%\n",
      "     Teste = 89.0%\n",
      "\n",
      "Classification:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.86      0.87        43\n",
      "           1       0.90      0.91      0.90        57\n",
      "\n",
      "    accuracy                           0.89       100\n",
      "   macro avg       0.89      0.89      0.89       100\n",
      "weighted avg       0.89      0.89      0.89       100\n",
      "\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlp_train, mlp_test = learn(ds1, MLPClassifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    BernoulliNB:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia:\n",
      "     Treino = 86.14%\n",
      "     Teste = 83.0%\n",
      "\n",
      "Classification:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.81      0.80        43\n",
      "           1       0.86      0.84      0.85        57\n",
      "\n",
      "    accuracy                           0.83       100\n",
      "   macro avg       0.83      0.83      0.83       100\n",
      "weighted avg       0.83      0.83      0.83       100\n",
      "\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bnb_train, bnb_test = learn(ds1, BernoulliNB)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    GaussianNB:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia:\n",
      "     Treino = 79.7%\n",
      "     Teste = 81.0%\n",
      "\n",
      "Classification:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.67      0.75        43\n",
      "           1       0.79      0.91      0.85        57\n",
      "\n",
      "    accuracy                           0.81       100\n",
      "   macro avg       0.82      0.79      0.80       100\n",
      "weighted avg       0.82      0.81      0.81       100\n",
      "\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gnb_train, gnb_test = learn(ds1, GaussianNB)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Criando um rank das Acurácias Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados = {'Models' :['LogisticRegression',\n",
    "                    'DecisionTreeClassifier',\n",
    "                    'KNeighborsClassifier',\n",
    "                    'RandomForestClassifier',\n",
    "                    'AdaBoostClassifier',\n",
    "                    'MLPClassifier',\n",
    "                    'GaussianNB',\n",
    "                    'BernoulliNB'],\n",
    "                    \n",
    "'Accuracy Train' : [round(log_train*100,2),\n",
    "                    round(tree_train*100,2),\n",
    "                    round(kn_train*100,2),\n",
    "                    round(rand_train*100,2),\n",
    "                    round(ada_train*100,2),\n",
    "                    round(mlp_train*100,2),\n",
    "                    round(gnb_train*100,2),\n",
    "                    round(bnb_train*100,2)],\n",
    "\n",
    "'Accuracy Test' :  [round(log_test*100,2),\n",
    "                    round(tree_test*100,2),\n",
    "                    round(kn_test*100,2),\n",
    "                    round(rand_test*100,2),\n",
    "                    round(ada_test*100,2),\n",
    "                    round(mlp_test*100,2),\n",
    "                    round(gnb_test*100,2),\n",
    "                    round(bnb_test*100,2)],}\n",
    "\n",
    "rank = pd.DataFrame(dados)\n",
    "rank.sort_values(by='Accuracy Test', ascending=False, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rank da Aprendizagem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Models</th>\n",
       "      <th>Accuracy Train</th>\n",
       "      <th>Accuracy Test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MLPClassifier</td>\n",
       "      <td>88.12</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>86.63</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>100.00</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>89.60</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>95.54</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>86.14</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>79.70</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>100.00</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Models  Accuracy Train  Accuracy Test\n",
       "5           MLPClassifier           88.12           89.0\n",
       "0      LogisticRegression           86.63           87.0\n",
       "3  RandomForestClassifier          100.00           85.0\n",
       "2    KNeighborsClassifier           89.60           84.0\n",
       "4      AdaBoostClassifier           95.54           83.0\n",
       "7             BernoulliNB           86.14           83.0\n",
       "6              GaussianNB           79.70           81.0\n",
       "1  DecisionTreeClassifier          100.00           75.0"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "MIT License\n",
    "\n",
    "Copyright (c) 2022 João Bruno\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy\n",
    "of this software and associated documentation files (the \"Software\"), to deal\n",
    "in the Software without restriction, including without limitation the rights\n",
    "to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
    "copies of the Software, and to permit persons to whom the Software is\n",
    "furnished to do so, subject to the following conditions:\n",
    "\n",
    "The above copyright notice and this permission notice shall be included in all\n",
    "copies or substantial portions of the Software.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
    "AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
    "OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
    "SOFTWARE."
   ]
  }
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