churn-prediction-crisp-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8e87fc5b",
   "metadata": {},
   "source": [
    "# Entendimento de negócio\n",
    "\n",
    "`Objetivo geral`\n",
    "\n",
    "- Um banco requisitou um modelo de _machine learning_ para a identificação de clientes em _churn_, buscando encontrar o maior número de clientes em churn possível.\n",
    "\n",
    "- É necessário entregar, no final, uma lista com os 20 clientes mais prioritários."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67fce0bd",
   "metadata": {},
   "source": [
    "### Breve descrição dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98d116a",
   "metadata": {},
   "source": [
    "Coluna | Descrição\n",
    "-------|----------\n",
    "RowNumber | Nº da linha\n",
    "CustomerId | ID do cliente\n",
    "Surname | Sobrenome do cliente\n",
    "CreditScore | Pontuação de crédito do cliente para o mercado de consumo\n",
    "Geography | País onde o cliente reside\n",
    "Gender | Gênero do cliente\n",
    "Age | Idade do cliente\n",
    "Tenure| Nº de meses que o cliente permaneceu ativo\n",
    "Balance  | Valor gasto pelo cliente\n",
    "NumOfProducts | Nº de produtos comprados pelo cliente\n",
    "HasCrCard | Indica se o cliente possui ou não um cartão de crédito\n",
    "IsActiveMember | Indica se o cliente ainda tem o cadastro ativo na empresa\n",
    "EstimatedSalary | Estimativa de salário mensal do cliente\n",
    "Exited | Indica se o cliente está ou não em churn "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "502b0a63",
   "metadata": {},
   "source": [
    "# 1. Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d9e73bae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:44.915952Z",
     "start_time": "2023-06-23T22:35:39.810754Z"
    }
   },
   "outputs": [],
   "source": [
    "# Manipulação dos dados\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# DataViz\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE # Separabilidade dos dados\n",
    "import scikitplot as skplt # Cumulative gains / Lift\n",
    "from sklearn.metrics import precision_recall_curve # Gráfico de recall e precisão\n",
    "\n",
    "\n",
    "# Split dos dados\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Pipeline / transformação\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "# Métricas\n",
    "\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, classification_report, roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from scikitplot.metrics import plot_roc\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "# Balanceamento das classes\n",
    "\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Modelagem\n",
    "\n",
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "# Feature selection\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest, f_classif, mutual_info_classif\n",
    "\n",
    "# Otimização dos hiperparâmetros\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "484b3671",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 2. Carregando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7486e07",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:44.993521Z",
     "start_time": "2023-06-23T22:35:44.919298Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df_raw = pd.read_csv('data/churn.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76bfd58c",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 3. Inspeção inicial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "653dc2b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:45.065339Z",
     "start_time": "2023-06-23T22:35:45.011112Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analisando colunas\n",
    "\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "223f439f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:45.081106Z",
     "start_time": "2023-06-23T22:35:45.071262Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de linhas: 10000\n",
      "Total de colunas: 14\n"
     ]
    }
   ],
   "source": [
    "# Verificando a dimensionalidade\n",
    "\n",
    "print(f'Total de linhas: {df_raw.shape[0]}')\n",
    "print(f'Total de colunas: {df_raw.shape[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cfab35dc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:45.121701Z",
     "start_time": "2023-06-23T22:35:45.085378Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10000 non-null  int64  \n",
      " 1   CustomerId       10000 non-null  int64  \n",
      " 2   Surname          10000 non-null  object \n",
      " 3   CreditScore      10000 non-null  int64  \n",
      " 4   Geography        10000 non-null  object \n",
      " 5   Gender           10000 non-null  object \n",
      " 6   Age              10000 non-null  int64  \n",
      " 7   Tenure           10000 non-null  int64  \n",
      " 8   Balance          10000 non-null  float64\n",
      " 9   NumOfProducts    10000 non-null  int64  \n",
      " 10  HasCrCard        10000 non-null  int64  \n",
      " 11  IsActiveMember   10000 non-null  int64  \n",
      " 12  EstimatedSalary  10000 non-null  float64\n",
      " 13  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(9), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Informações gerais dos dados\n",
    "\n",
    "df_raw. info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d42ce0c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:45.215145Z",
     "start_time": "2023-06-23T22:35:45.125718Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5000.5</td>\n",
       "      <td>15690940.6</td>\n",
       "      <td>650.5</td>\n",
       "      <td>38.9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>76485.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>100090.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2886.9</td>\n",
       "      <td>71936.2</td>\n",
       "      <td>96.7</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.9</td>\n",
       "      <td>62397.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>57510.5</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>15565701.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2500.8</td>\n",
       "      <td>15628528.2</td>\n",
       "      <td>584.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51002.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5000.5</td>\n",
       "      <td>15690738.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>97198.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100193.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7500.2</td>\n",
       "      <td>15753233.8</td>\n",
       "      <td>718.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>127644.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>149388.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>15815690.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>250898.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>199992.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RowNumber  CustomerId  CreditScore      Age   Tenure   Balance  \\\n",
       "count    10000.0     10000.0      10000.0  10000.0  10000.0   10000.0   \n",
       "mean      5000.5  15690940.6        650.5     38.9      5.0   76485.9   \n",
       "std       2886.9     71936.2         96.7     10.5      2.9   62397.4   \n",
       "min          1.0  15565701.0        350.0     18.0      0.0       0.0   \n",
       "25%       2500.8  15628528.2        584.0     32.0      3.0       0.0   \n",
       "50%       5000.5  15690738.0        652.0     37.0      5.0   97198.5   \n",
       "75%       7500.2  15753233.8        718.0     44.0      7.0  127644.2   \n",
       "max      10000.0  15815690.0        850.0     92.0     10.0  250898.1   \n",
       "\n",
       "       NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary   Exited  \n",
       "count        10000.0    10000.0         10000.0          10000.0  10000.0  \n",
       "mean             1.5        0.7             0.5         100090.2      0.2  \n",
       "std              0.6        0.5             0.5          57510.5      0.4  \n",
       "min              1.0        0.0             0.0             11.6      0.0  \n",
       "25%              1.0        0.0             0.0          51002.1      0.0  \n",
       "50%              1.0        1.0             1.0         100193.9      0.0  \n",
       "75%              2.0        1.0             1.0         149388.2      0.0  \n",
       "max              4.0        1.0             1.0         199992.5      1.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a distribuição geral dos dados\n",
    "\n",
    "np.round(df_raw.describe(),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1238b2f0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:45.283573Z",
     "start_time": "2023-06-23T22:35:45.223751Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checando se há dados duplicados\n",
    "\n",
    "df_raw.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "83f05a34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:45.308787Z",
     "start_time": "2023-06-23T22:35:45.291177Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checando se há dados nulos\n",
    "\n",
    "df_raw.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943b0156",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 4. Split do dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e146a33f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:45.322640Z",
     "start_time": "2023-06-23T22:35:45.312979Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Cópia do dataset\n",
    "\n",
    "df1 = df_raw.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "88de9a4b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:45.339400Z",
     "start_time": "2023-06-23T22:35:45.330329Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Separando features de targets\n",
    "\n",
    "X = df1.drop('Exited', axis=1)\n",
    "y = df1['Exited']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f4a74fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:45.378627Z",
     "start_time": "2023-06-23T22:35:45.348093Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Separando os dados em treino, teste e validação\n",
    "\n",
    "X_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size=0.1, stratify=y, random_state=42)\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X_train_val, y_train_val, test_size=0.1, stratify=y_train_val, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cd4c1f30",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:45.392445Z",
     "start_time": "2023-06-23T22:35:45.385360Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dados de treino: 8100\n",
      "Dados de validação:900\n",
      "Dados de teste: 1000\n"
     ]
    }
   ],
   "source": [
    "# Dimensionalidade\n",
    "\n",
    "print(f'Dados de treino: {X_train.shape[0]}')\n",
    "print(f'Dados de validação:{X_val.shape[0]}')\n",
    "print(f'Dados de teste: {X_test.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42900a4f",
   "metadata": {},
   "source": [
    "# 5. EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f7021ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:45.873892Z",
     "start_time": "2023-06-23T22:35:45.396557Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Verificando se as classes estão desbalanceadas\n",
    "\n",
    "sns.countplot(data=X_train.join(y_train), x='Exited', palette=['lightblue', 'red'])\n",
    "plt.grid()\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "222eb7d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:45.897283Z",
     "start_time": "2023-06-23T22:35:45.877868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.796296\n",
       "1    0.203704\n",
       "Name: Exited, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checando a porcentagem das classes\n",
    "\n",
    "y_train.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a8dc6933",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:46.521275Z",
     "start_time": "2023-06-23T22:35:45.906604Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Churn na quantidade de meses ativos\n",
    "\n",
    "data = pd.concat([X_train, y_train], axis=1)\n",
    "\n",
    "sns.histplot(data=data, x='Tenure', hue='Exited', discrete=True)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8485ce55",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:46.547687Z",
     "start_time": "2023-06-23T22:35:46.525394Z"
    }
   },
   "outputs": [],
   "source": [
    "# Separando colunas com dados numéricos\n",
    "colunas_numericas = X_train.join(\n",
    "    y_train).select_dtypes(include=['int64', 'float64'])\n",
    "\n",
    "# Separando colunas com dados categóricos\n",
    "colunas_categoricas = X_train.join(y_train).select_dtypes(\n",
    "    exclude=['int64', 'float64'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f66f12a8",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 5.1 Dados numéricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a4b9a8b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:51.305621Z",
     "start_time": "2023-06-23T22:35:46.553479Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAAMBqktAGAAAAAGAqSGgDAA+oqkdU1Z9X1Uer6paq+qWh/JKq+nRV3TS8ThjKq6reWFXbq+pjVfXUNT0AAAAANrSlPBQSANh4vpbkWd29q6oeluT9VfUHw7J/291X7VX/uUmOG15PT/Lm4ScAAAAsOwltFmXredcuar0d5z9vmVsC083fEuvN8BDnXcPsw4ZX72OVU5NcNqz3gao6vKqO7O47V7ipq87fKwAAG53PvEwDQ44AAA9SVQdX1U1J7klyfXffMCx67TCsyIVVdchQdlSS28dWv2MoAwAAgGXnCm0A4EG6+xtJTqiqw5O8s6q+L8krk9yV5OFJLkryiiS/vNBtVtXZSc5OkpmZmczNzS1zq1feucfvXtR68x3rrl27pjIOq0FsJhOX+YnNZOICMP1cMQ0PJaENAEzU3fdV1fuSnNzdrx+Kv1ZVv53kF4b5nUmOGVvt6KFs721dlFEiPNu2bevZ2dkVa/dKOXOxXyZOn51YPjc3l2mMw2oQm8nEZX5iM5m4AAAbkSFHAIAHVNXjhyuzU1WHJnl2kr+oqiOHskry/CQfH1a5JslLauTEJPdvxPGzAQAAWB9cob0PbuvgQK32e2ax+1usS04+bFX3B6yJI5NcWlUHZ3Ti+8rufldVvbeqHp+kktyU5KeH+tclOSXJ9iRfSfLS1W8ym43PaAAAsHlJaAMAD+jujyV5yoTyZ81Tv5Ocs9LtAgAAgERCe9Nb7St8AQAAAAAWyxjaAAAAAABMBVdorwDjOs5vWsaYNlY0AAAAAKw/myKhffPO+3OmoTUAAAAAAKbapkhoA8tjKSeHNvodCE6cAQAAAKw8CW1gXZuWYWrOPX5RqwGsG4ZMAwAApoGE9gaw+ATc7kzLW2Cxxwg8mIQVACycfhMAYP2ZjmzmJiFpu3n53QMwrZbSh6120m+1+1tJzc1LIhwAYOVIaAMAABuaiwcAADYOCW2YwAP+AAAAAGD9kdAGNiRXYgHAyjKsBgAAa+GgtW4AAAAAAAAshCu0AQAANiFX2QNsXO5aZiOT0AZWhc4UAAAAgKWS0AYAgBUwLVe/OukMAMA0kdAGmEKSDwAAAMBmJKENwH4tNoF+ycmHLXNLgP1xwguASarq8CS/leT7knSSn0jyl0nenmRrkh1JXtTd91ZVJXlDklOSfCXJmd394dVvNQA81EFr3QAAAABgxb0hyR929/ckeXKSW5Ocl+Q93X1ckvcM80ny3CTHDa+zk7x59ZsLAJO5QhsAYAXNd8X0ucfvzpn7uJp6tcdRZvq5Oh+YT1U9OskPJjkzSbr775L8XVWdmmR2qHZpkrkkr0hyapLLuruTfKCqDq+qI7v7zlVuOgA8xKIT2lX1iCR/muSQYTtXdferqurYJFckeWySG5P8eHf/XVUdkuSyJN+f5PNJ/nl371hi+wEANiTJSdh8puVBokylY5N8NslvV9WTM/qu/nNJZsaS1HclmRmmj0py+9j6dwxlEtoArLmlXKH9tSTP6u5dVfWwJO+vqj9I8vNJLuzuK6rqvyQ5K6Pbk85Kcm93P7GqTkvyuiT/fIntBwAAAPZtS5KnJvmZ7r6hqt6Qbw0vkiTp7q6qPpCNVtXZGQ1JkpmZmczNzS1Tc9eHXbt2bbhjWg7iMr+ViM25x+9e1u2tlH0dt/fM/MRmcRad0B5uPdo1zD5seHWSZyX5F0P5pUlenVFC+9RhOkmuSvLrVVXDdgAAAOa1r6uX9zeED5A7ktzR3TcM81dllNC+e89QIlV1ZJJ7huU7kxwztv7RQ9mDdPdFSS5Kkm3btvXs7OwKNX9tzM3NZaMd03IQl/mtRGympX/bcfrsvMu8Z+YnNouzpDG0q+rgjG5VemKS30jyqST3dfee00d7bktKxm5Z6u7dVXV/RsOSfG4pbQAAgI1kseOuTwvD6cDq6+67qur2qvru7v7LJCcl+cTwOiPJ+cPPq4dVrknysqq6IsnTk9xv/GwA1oslJbS7+xtJTqiqw5O8M8n3LLVBK3HL0syh03OLxmoSl/mJzWTiMj+xmcztUwAA68bPJLm8qh6e5LYkL01yUJIrq+qsJJ9J8qKh7nVJTkmyPclXhroAsC4sKaG9R3ffV1XvS/KMJIdX1ZbhKu3x25L23LJ0R1VtSfLojB4Oufe2lv2WpTddfnUuuHlZDnVDOff43eIyD7GZTFzmJzaTXXLyYW6fAgBYB7r7piTbJiw6aULdTnLOSrcJABZj0dmXqnp8kq8PyexDkzw7owc9vi/JC5JckYfesnRGkj8blr/X+NkAAABLs9GHqQFg/Vvssy52nP+8lWoSG9hSLic8MsmlwzjaByW5srvfVVWfSHJFVb0myUeSXDzUvzjJW6tqe5IvJDltCfsGAAAAAGCTWXRCu7s/luQpE8pvS/K0CeVfTfLCxe4PAAAAAIDNzYCvAMADquoRSf40ySEZfU64qrtfVVXHZjSc2GOT3Jjkx7v776rqkCSXJfn+jJ6N8c+7e8dqtHVftzUyHQyTAAAAHKiD1roBAMC68rUkz+ruJyc5IcnJVXViRs/JuLC7n5jk3iRnDfXPSnLvUH7hUA8AAABWhIQ2APCAHtk1zD5seHWSZyW5aii/NMnzh+lTh/kMy0+qqlqd1gIAALDZGHIEAHiQ4YHPNyZ5YpLfSPKpJPd19+6hyh1Jjhqmj0pye5J09+6quj+jYUk+t6qNBgCAdcxwebB8JLQBgAfp7m8kOaGqDk/yziTfs9RtVtXZSc5OkpmZmczNzS11kzn3+N37r7SOzRw6/cewUsRmMnGZn9hMtlJxWY7/4QAAiyWhDQBM1N33VdX7kjwjyeFVtWW4SvvoJDuHajuTHJPkjqrakuTRGT0ccu9tXZTkoiTZtm1bz87OLrl90/7QwHOP350LbvZRbBKxmUxc5ic2k61UXHacPrvs2wQAWChjaAMAD6iqxw9XZqeqDk3y7CS3JnlfkhcM1c5IcvUwfc0wn2H5e7u7V63BAAAAbCouYwAAxh2Z5NJhHO2DklzZ3e+qqk8kuaKqXpPkI0kuHupfnOStVbU9yReSnLYWjQYAAGBzkNAGAB7Q3R9L8pQJ5bcledqE8q8meeEqNA0AANbcvh7ueO7xu6d+WDyYBoYcAQAAAABgKkhoAwAAAAAwFSS0AQAAAACYChLaAAAAAABMBQltAAAAAACmgoQ2AAAAAABTQUIbAAAAAICpIKENAAAAAMBUkNAGAAAAAGAqSGgDAAAAADAVJLQBAAAAAJgKW9a6AQAAAADA5rP1vGsXtd6O85+3zC1hmrhCGwAAAACAqSChDQAAAADAVJDQBgAAAABgKkhoAwAAAAAwFSS0AQAAAACYChLaAAAAAABMBQltAAAAAACmgoQ2AAAAbHBVtaOqbq6qm6rqQ0PZY6rq+qr65PDziKG8quqNVbW9qj5WVU9d29YDwLdIaAMAAMDm8E+7+4Tu3jbMn5fkPd19XJL3DPNJ8twkxw2vs5O8edVbCgDzWHRCu6qOqar3VdUnquqWqvq5ofzVVbVzOOt7U1WdMrbOK4czvH9ZVT+8HAcAAAAALMqpSS4dpi9N8vyx8st65ANJDq+qI9egfQDwEFuWsO7uJOd294er6tuS3FhV1w/LLuzu149XrqonJTktyfcmeUKSP66q7+rubyyhDQAAAMD+dZI/qqpO8l+7+6IkM91957D8riQzw/RRSW4fW/eOoezOsbJU1dkZXcGdmZmZzM3NrVzr18CuXbs23DEth80el3OP3z3vsplD9718s1qJuGyU9+Bm/3tarEUntIdO785h+ktVdWtGHdx8Tk1yRXd/Lcmnq2p7kqcl+bPFtgEAAABYkB/o7p1V9feSXF9VfzG+sLt7SHYv2JAUvyhJtm3b1rOzs8vW2PVgbm4uG+2YlsNmj8uZ510777Jzj9+dC25eyrWjG9NKxGXH6bPLur21stn/nhZrWcbQrqqtSZ6S5Iah6GXDgyPesuehEpn/DC8AAACwgrp75/DzniTvzOgCs7v3DCUy/LxnqL4zyTFjqx89lAHAmlvy6ZGqelSS30vy8u7+YlW9OcmvZHQ7068kuSDJTxzA9pb9liW3fEwmLvMTm8nEZX5iM5nbpwAA1l5VHZbkoOHu6sOSPCfJLye5JskZSc4ffl49rHJNRheqXZHk6UnuHxuaBADW1JIS2lX1sIyS2Zd39zuSpLvvHlv+m0neNcwu6AzvStyy9KbLr3bLxwRuhZmf2EwmLvMTm8kuOfkwt08BAKy9mSTvrKpklAf4ne7+w6r6YJIrq+qsJJ9J8qKh/nVJTkmyPclXkrx09ZsMAJMtOvtSo57w4iS3dvevjpUfOXbm9p8l+fgwfU2S36mqX83ooZDHJfnzxe4fAAAA2L/uvi3JkyeUfz7JSRPKO8k5q9A0ADhgS7mc8JlJfjzJzVV101D2i0leXFUnZDTkyI4kP5Uk3X1LVV2Z5BNJdic5p7u/sYT9AwAAAACwiSw6od3d709SExZdt491XpvktYvdJwCwsqrqmCSXZXRrcie5qLvfUFWvTvKTST47VP3F7r5uWOeVSc5K8o0kP9vd7171hgMAALApGPAVABi3O8m53f3hqvq2JDdW1fXDsgu7+/XjlavqSUlOS/K9GQ0p9sdV9V3uwgIAAGAlHLTWDQAA1o/uvrO7PzxMfynJrUmO2scqpya5oru/1t2fzujhUU9b+ZYCAACwGblCGwCYqKq2JnlKkhsyenbGy6rqJUk+lNFV3PdmlOz+wNhqd2TfCXAAAFhzW8+7dq2bACyShDYA8BBV9agkv5fk5d39xap6c5JfyWhc7V9JckGSnziA7Z2d5OwkmZmZydzc3JLbeO7xu5e8jbU0c+j0H8NKEZvJxGV+YjPZSsVlOf6HAwAsloQ2APAgVfWwjJLZl3f3O5Kku+8eW/6bSd41zO5McszY6kcPZQ/S3RcluShJtm3b1rOzs0tu55lTflXNucfvzgU3+yg2idhMJi7zE5vJViouO06fXfZtAgAslDG0AYAHVFUluTjJrd39q2PlR45V+2dJPj5MX5PktKo6pKqOTXJckj9frfYCAACwubiMAQAY98wkP57k5qq6aSj7xSQvrqoTMhpyZEeSn0qS7r6lqq5M8okku5Oc093fWOU2AwAAsElIaAMAD+ju9yepCYuu28c6r03y2hVrFAAAAAwMOQIAAAAAwFSQ0AYAAAAAYCpIaAMAAAAAMBUktAEAAAAAmAoS2gAAAAAATAUJbQAAAAAApoKENgAAAAAAU0FCGwAAAACAqSChDQAAAADAVJDQBgAAAABgKmxZ6wYAAAAAACzU1vOuXdR6O85/3jK3hLXgCm0AAAAAAKaChDYAAAAAAFNBQhsAAAAAgKkgoQ0AAAAAwFSQ0AYAAAAAYCpIaAMAAAAAMBUktAEAAAAAmAoS2gAAAAAATIUta90AAAAAADhQW8+7dq2bAKwBV2gDAADAJlBVB1fVR6rqXcP8sVV1Q1Vtr6q3V9XDh/JDhvntw/Kta9pwABgjoQ0AAACbw88luXVs/nVJLuzuJya5N8lZQ/lZSe4dyi8c6gHAurDohHZVHVNV76uqT1TVLVX1c0P5Y6rq+qr65PDziKG8quqNwxnej1XVU5frIAAAAID5VdXRSZ6X5LeG+UryrCRXDVUuTfL8YfrUYT7D8pOG+gCw5pZyhfbuJOd295OSnJjknKp6UpLzkrynu49L8p5hPkmem+S44XV2kjcvYd8AAADAwv1akn+X5JvD/GOT3Nfdu4f5O5IcNUwfleT2JBmW3z/UB4A1t+iHQnb3nUnuHKa/VFW3ZtTpnZpkdqh2aZK5JK8Yyi/r7k7ygao6vKqOHLYDAAAArICq+pEk93T3jVU1u4zbPTujC9YyMzOTubm55dr0urBr164Nd0zLYT3F5dzjd++/0iqaOXT9tWk9WE9xWS/v3T3W09/TNFl0Qnvc8ICIpyS5IcnMWJL6riQzw/QDZ3gHe87+SmgDAADAynlmkh+rqlOSPCLJtyd5Q5LDq2rLcBX20Ul2DvV3JjkmyR1VtSXJo5N8fu+NdvdFSS5Kkm3btvXs7OxKH8eqmpuby0Y7puWwnuJy5nnXrnUTHuTc43fngpuXJdW2oaynuOw4fXatm/Ag6+nvaZos+d1UVY9K8ntJXt7dXxwfVqu7u6r6ALe37Gd419OZoPVEXOYnNpOJy/zEZjJnmwEA1l53vzLJK5NkuEL7F7r79Kr63SQvSHJFkjOSXD2scs0w/2fD8vcOd1sDwJpbUkK7qh6WUTL78u5+x1B8956hRKrqyCT3DOV7zvDuMX729wErcYb3TZdfvW7OBK0n6+kM2XojNpOJy/zEZrJLTj7M2WYAgPXrFUmuqKrXJPlIkouH8ouTvLWqtif5QpLT1qh9APAQi34o5PCE44uT3Nrdvzq2aM+Z3OShZ3hfUiMnJrnf+NkAsL5U1TFV9b6q+kRV3VJVPzeUP6aqrq+qTw4/jxjKq6reWFXbq+pjVfXUtT0CAGBfunuuu39kmL6tu5/W3U/s7hd299eG8q8O808clt+2tq0GgG9ZdEI7ozG4fjzJs6rqpuF1SpLzkzy7qj6Z5IeG+SS5LsltSbYn+c0k/3oJ+wYAVsbuJOd295OSnJjknKp6UpLzkrynu49L8p5hPkmem+S44XV2kjevfpMBAADYLBZ9f3x3vz9JzbP4pAn1O8k5i90fALDyhrun7hymv1RVt2b0EOdTk8wO1S5NMpfRbcqnJrls6Oc/UFWH7xl6bLXbDgAAwMa3lCu0AYANrKq2JnlKkhuSzIwlqe9KMjNMH5Xk9rHV7hjKAAAAYNl5ghkA8BBV9aiMHvz88u7+4ujRGSPd3VXVB7i9szMakiQzMzOZm5tbchvPPX73krexlmYOnf5jWCliM5m4zE9sJlupuCzH/3AAgMWS0AYAHqSqHpZRMvvy7n7HUHz3nqFEqurIJPcM5TuTHDO2+tFD2YN090VJLkqSbdu29ezs7JLbeeZ51y55G2vp3ON354KbfRSbRGwmE5f5ic1kKxWXHafPLvs2AQAWypAjAMADanQp9sVJbu3uXx1bdE2SM4bpM5JcPVb+kho5Mcn9xs8GAABgpbiMAQAY98wkP57k5qq6aSj7xSTnJ7myqs5K8pkkLxqWXZfklCTbk3wlyUtXtbUAAAALtHWRd3nuOP95y9wSlkJCGwB4QHe/P0nNs/ikCfU7yTkr2igAAAAYGHIEAAAAAICpIKENAAAAAMBUkNAGAAAAAGAqSGgDAAAAADAVJLQBAAAAAJgKEtoAAAAAAEwFCW0AAAAAAKaChDYAAAAAAFNBQhsAAAAAgKkgoQ0AAAAAwFSQ0AYAAAAAYCpIaAMAAAAAMBUktAEAAAAAmAoS2gAAAAAATAUJbQAAAAAApsKWtW4AAAAAAJvX1vOuXesmAFPEFdoAAAAAAEwFCW0AAAAAAKaChDYAAAAAAFNBQhsAAAAAgKkgoQ0AAAAAwFSQ0AYAAAAAYCpsWesGAAAAADD9tp537Vo3AdgEXKENAAAAG1hVPaKq/ryqPlpVt1TVLw3lx1bVDVW1vareXlUPH8oPGea3D8u3rukBAMCYJSW0q+otVXVPVX18rOzVVbWzqm4aXqeMLXvl0CH+ZVX98FL2DQAAACzI15I8q7ufnOSEJCdX1YlJXpfkwu5+YpJ7k5w11D8ryb1D+YVDPQBYF5Z6hfYlSU6eUH5hd58wvK5Lkqp6UpLTknzvsM5/rqqDl7h/AAAAYB96ZNcw+7Dh1UmeleSqofzSJM8fpk8d5jMsP6mqanVaCwD7tqQxtLv7Tw/g1qNTk1zR3V9L8umq2p7kaUn+bCltAAAAAPZtuKDsxiRPTPIbST6V5L7u3j1UuSPJUcP0UUluT5Lu3l1V9yd5bJLP7bXNs5OcnSQzMzOZm5tb4aNYXbt27dpwx7Qc9hWXc4/fPbF8s5g5VAwm2QhxWan/Bf7PLM5KPRTyZVX1kiQfSnJud9+bUYf4gbE6450lAAAAsEK6+xtJTqiqw5O8M8n3LMM2L0pyUZJs27atZ2dnl7rJdWVubi4b7ZiWw77icuYmfyjkucfvzgU3r1SqbXpthLjsOH12Rbbr/8zirMS76c1JfiWj25d+JckFSX5ioSuvxBnejXAmaCWIy/zEZjJxmZ/YTOZsMwDA+tLd91XV+5I8I8nhVbVluEr76CQ7h2o7kxyT5I6q2pLk0Uk+vyYNBuCAbF3kiaUd5z9vmVuycpY9od3dd++ZrqrfTPKuYXZPh7jHeGc5vv6yn+F90+VXT/2ZoJWwEc6QrRSxmUxc5ic2k11y8mHONk+ZqnpLkh9Jck93f99Q9uokP5nks0O1Xxx7RsYrM3pw1DeS/Gx3v3vVGw0A7FNVPT7J14dk9qFJnp3Rgx7fl+QFSa5IckaSq4dVrhnm/2xY/t7u7lVvOABMsOzZl6o6srvvHGb/WZKPD9PXJPmdqvrVJE9IclySP1/u/QMAS3JJkl9Pctle5Rd29+vHC/Z64PMTkvxxVX3XcEszALB+HJnk0mEc7YOSXNnd76qqTyS5oqpek+QjSS4e6l+c5K3Ds6++kFF/D7BpLfaq52S6rnyeFktKaFfV25LMJnlcVd2R5FVJZqvqhIyGHNmR5KeSpLtvqaork3wiye4k5/jCCwDriwc+A8DG090fS/KUCeW3ZdR3713+1SQvXIWmAcABW1JCu7tfPKH44glle+q/Nslrl7JPAGBNeOAzAAAAa86ArwDA/izpgc/Jyjz0edofxOphsvMTm8nEZX5iM9lKxcUDnwGAtSShDQDs01If+DxsY9kf+nzmEsaxWw88THZ+YjOZuMxPbCZbqbjsOH122bcJALBQB611AwCA9a2qjhyb3fuBz6dV1SFVdWw88BkAAIAV5jIGAOABHvgMAADAeiahDQA8wAOfAQAAWM8MOQIAAAAAwFSQ0AYAAAAAYCpIaAMAAAAAMBUktAEAAAAAmAoS2gAAAAAATAUJbQAAAAAApoKENgAAAAAAU0FCGwAAAACAqSChDQAAAADAVJDQBgAAAABgKkhoAwAAAAAwFSS0AQAAAACYChLaAAAAAABMBQltAAAAAACmgoQ2AAAAAABTQUIbAAAAAICpsGWtGwAAAAAAsBFtPe/aeZede/zunDnP8h3nP2+lmjT1XKENAAAAAMBUkNAGAAAAAGAqSGgDAAAAADAVJLQBAAAAAJgKEtoAAAAAAEwFCW0AAAAAAKaChDYAAAAAAFNhy1o3AAAAAFg5VXVMksuSzCTpJBd19xuq6jFJ3p5ka5IdSV7U3fdWVSV5Q5JTknwlyZnd/eG1aDtrY+t518677Nzjd+fMfSwHWGlLukK7qt5SVfdU1cfHyh5TVddX1SeHn0cM5VVVb6yq7VX1sap66lIbDwAAAOzX7iTndveTkpyY5JyqelKS85K8p7uPS/KeYT5JnpvkuOF1dpI3r36TAWCypQ45ckmSk/cq0yECAADAOtHdd+65wrq7v5Tk1iRHJTk1yaVDtUuTPH+YPjXJZT3ygSSHV9WRq9tqAJhsSQnt7v7TJF/Yq1iHCAAAAOtQVW1N8pQkNySZ6e47h0V3ZTQkSTJKdt8+ttodQxkArLmVGEP7QDvEOwMAAACsqKp6VJLfS/Ly7v7iaKjske7uquoD3N7ZGd2BnZmZmczNzS1ja9ferl27NtwxLdS5x++ed9nMoftevpmJzWTiMr99xWax/38WG+tp+n+3og+FXC8doj+cycRlfmIzmbjMT2wm28xfAqZVVb0lyY8kuae7v28o88AoAJhyVfWwjJLZl3f3O4biu6vqyO6+c7iD+p6hfGeSY8ZWP3ooe5DuvijJRUmybdu2np2dXanmr4m5ublstGNaqH099PHc43fngptXNJ00tcRmMnGZ375is+P02UVtc7EPbV3s/tbCSryb1l2H+KbLr/aHM4F/KPMTm8nEZX5iM9klJx+2ab8ETLFLkvx6ksvGyvY8H+P8qjpvmH9FHvx8jKdn9HyMp69qawGA/RpOQl+c5Nbu/tWxRdckOSPJ+cPPq8fKX1ZVV2TUt98/dic2AKyppT4UcpI9HWLy0A7xJTVyYnSIALDueD4GAGxIz0zy40meVVU3Da9TMkpkP7uqPpnkh4b5JLkuyW1Jtif5zST/eg3aDAATLelywqp6W5LZJI+rqjuSvCqjDvDKqjoryWeSvGiofl1GtyRvz+i25JcuZd8AwKpZ8vMxVmJIsWkf5sdQRfMTm8nEZX5iM9lKxcVwYtOnu9+fpOZZfNKE+p3knBVtFAAs0pIS2t394nkW6RABYANazPMxhvWWfUixxY4Nt14Yqmh+YjOZuMxPbCZbqbhM0xibAMDGsxJDjgAAG8vde4YSWczzMQAAAGC5SGgDAPvj+RgAAACsC+7LAwAe4PkYAAAArGcS2gDAAzwfAwAAgPXMkCMAAAAAAEwFCW0AAAAAAKaChDYAAAAAAFNBQhsAAAAAgKkgoQ0AAAAAwFSQ0AYAAAAAYCpIaAMAAAAAMBUktAEAAAAAmApb1roBAAAAACy/reddu9ZNAFh2rtAGAAAAAGAqSGgDAAAAADAVJLQBAAAAAJgKEtoAAAAAAEwFCW0AAAAAAKaChDYAAAAAAFNhy1o3AAAAAACAb9l63rVr3YR1yxXaAAAAAABMBQltAAAAAACmgoQ2AAAAAABTQUIbAAAAAICpIKENAAAAAMBUkNAGAAAAAGAqSGgDAAAAADAVJLQBAAAAAJgKEtoAAAAAAEwFCW0AAAAAAKbCiiW0q2pHVd1cVTdV1YeGssdU1fVV9cnh5xErtX8AAAAgqaq3VNU9VfXxsbKJ389r5I1Vtb2qPlZVT127lgPAQ630Fdr/tLtP6O5tw/x5Sd7T3cclec8wDwAAAKycS5KcvFfZfN/Pn5vkuOF1dpI3r1IbAWBBtqzy/k5NMjtMX5pkLskrVrkNAMAiVNWOJF9K8o0ku7t7W1U9Jsnbk2xNsiPJi7r73rVqIwDwUN39p1W1da/i+b6fn5rksu7uJB+oqsOr6sjuvnOVmssEW8+7dq2bALBurOQV2p3kj6rqxqo6eyibGesE70oys4L7BwCWn7uvAGBjmO/7+VFJbh+rd8dQBgDrwkpeof0D3b2zqv5ekuur6i/GF3Z3V1XvvdKQ/D47SWZmZjI3N7fkhswcmpx7/O4lb2ejEZf5ic1k4jI/sZls165dy/J/nHXN3VcAMOXm+36+Pyvx/X09WU+fZdfTdw3ffeYnNpOJy/zWU2zWy/+7hVixhHZ37xx+3lNV70zytCR377lVqaqOTHLPhPUuSnJRkmzbtq1nZ2eX3JY3XX51Lrh5tUdXWf/OPX63uMxDbCYTl/mJzWSXnHxYluP/OOvGnruvOsl/Hfpsd18BwHSa7/v5ziTHjNU7eih7iJX4/r6ezM3NrZvPsmeuoyFHfPeZn9hMJi7zW0+x2XH67Fo3YcFWJGJVdViSg7r7S8P0c5L8cpJrkpyR5Pzh59UrsX8AYEUs6u6rZGWu4FovVzIs1nq6GmO9EZvJxGV+YjPZSsVlmq7gYp/m+35+TZKXVdUVSZ6e5H7jZwOwnqzUKYCZJO+sqj37+J3u/sOq+mCSK6vqrCSfSfKiFdo/ALDMFnv31bDOsl/BtZ6uVFqM9XQ1xnojNpOJy/zEZrKViss0XcHFSFW9LaMhwh5XVXckeVVGiexJ38+vS3JKku1JvpLkpaveYADYhxX51NfdtyV58oTyzyc5aSX2CQCsHHdfAcD06u4Xz7PoId/Pu7uTnLOyLQKAxXMZAwCwEO6+AgBYoq1TfocZwHogoQ0A7Je7rwAAAFgPDlrrBgAAAAAAwEJIaAMAAAAAMBUktAEAAAAAmAoS2gAAAAAATAUJbQAAAAAApoKENgAAAAAAU0FCGwAAAACAqSChDQAAAADAVJDQBgAAAABgKkhoAwAAAAAwFSS0AQAAAACYChLaAAAAAABMBQltAAAAAACmgoQ2AAAAAABTYctaNwAAAABgmmw979q1bgLApuUKbQAAAAAApoKENgAAAAAAU0FCGwAAAACAqSChDQAAAADAVJDQBgAAAABgKkhoAwAAAAAwFSS0AQAAAACYChLaAAAAAABMBQltAAAAAACmgoQ2AAAAAABTQUIbAAAAAICpIKENAAAAAMBUkNAGAAAAAGAqSGgDAAAAADAVtqz2Dqvq5CRvSHJwkt/q7vNXuw0AwPLSvwPAxrJWffvW865d1Ho7zn/eMrcEgPVqVRPaVXVwkt9I8uwkdyT5YFVd092fWM12AADLR/8OABvLNPbti02E78u5x+/OmSuwXQCWZrWHHHlaku3dfVt3/12SK5KcusptAACWl/4dADYWfTsA61Z19+rtrOoFSU7u7n81zP94kqd398vG6pyd5Oxh9ruT/OUy7PpxST63DNvZaMRlfmIzmbjMT2wmW664fGd3P34ZtsMKWMP+fdr5vzE/sZlMXOYnNpOt97jo39ephfTtQ/lG79/X+9/QWhGX+YnNZOIyv40Wm1Xp21d9DO396e6Lkly0nNusqg9197bl3OZGIC7zE5vJxGV+YjOZuLDHSvTv087fx/zEZjJxmZ/YTCYurLSN3r/7G5pMXOYnNpOJy/zEZnFWe8iRnUmOGZs/eigDAKaX/h0ANhZ9OwDr1montD+Y5LiqOraqHp7ktCTXrHIbAIDlpX8HgI1F3w7AurWqQ4509+6qelmSdyc5OMlbuvuWVdj1hr0FaonEZX5iM5m4zE9sJhOXTWAN+/dp5+9jfmIzmbjMT2wmExcWRd/+AH9Dk4nL/MRmMnGZn9gswqo+FBIAAAAAABZrtYccAQAAAACARZHQBgAAAABgKmz4hHZVnVxVf1lV26vqvLVuz0qrqmOq6n1V9YmquqWqfm4of0xVXV9Vnxx+HjGUV1W9cYjPx6rqqWPbOmOo/8mqOmOtjmk5VdXBVfWRqnrXMH9sVd0wHP/bhweepKoOGea3D8u3jm3jlUP5X1bVD6/RoSyrqjq8qq6qqr+oqlur6hneM0lV/Zvh7+jjVfW2qnrEZn3PVNVbquqeqvr4WNmyvUeq6vur6uZhnTdWVa3uEcLSTPob2Wv5bFXdX1U3Da//OLbsIf+Dh/KJf2PTZIXi8uqq2jm2zimrdTzLabGxqarvHiu7qaq+WFUvH5Zt2vfMfuKyqd8zw7J/U3t9phnKj60Jn2tgM6ll+I64EVXVjuHz+U1V9aGh7IA//280kz6fiMv8/bDYLF9egb1094Z9ZfTwik8l+QdJHp7ko0metNbtWuFjPjLJU4fpb0vyV0melOQ/JTlvKD8vyeuG6VOS/EGSSnJikhuG8sckuW34ecQwfcRaH98yxOfnk/xOkncN81cmOW2Y/i9J/u9h+l8n+S/D9GlJ3j5MP2l4Hx2S5Njh/XXwWh/XMsTl0iT/aph+eJLDN/t7JslRST6d5NCx98qZm/U9k+QHkzw1ycfHypbtPZLkz4e6Naz73LU+Zi+vA3lN+hvZa/nsnr5nwrKH/A8epif+jU3Ta4Xi8uokv7DWx7aWsRmrc3CSu5J8p/fMPuOyqd8zmeczzdj0Qz7XeHltpleW+B1xo76S7EjyuL3KDujz/0Z8Tfp8Ii4PidED/fBmj818fbD/M0t/bfQrtJ+WZHt339bdf5fkiiSnrnGbVlR339ndHx6mv5Tk1oz+gE7N6B9vhp/PH6ZPTXJZj3wgyeFVdWSSH05yfXd/obvvTXJ9kpNX70iWX1UdneR5SX5rmK8kz0py1VBl77jsiddVSU4a6p+a5Iru/lp3fzrJ9ozeZ1Orqh6d0Reki5Oku/+uu++L90ySbElyaFVtSfLIJHdmk75nuvtPk3xhr+JleY8My769uz/Qo577srFtwVSY529kv/bxPziZ/29saqxQXDaExcZmLycl+VR3f2aY37Tvmb3sHZcNYYmx2fszzd/s57MwbArL9B1xMznQz/8byjJ+d97oxvthsVmevAJ72egJ7aOS3D42f8dQtikMtyY8JckNSWa6+85h0V1JZobp+WK0EWP3a0n+XZJvDvOPTXJfd+8e5seP8YHjH5bfP9TfiHE5Nslnk/z2cKvdb1XVYdnk75nu3pnk9Un+OqMO5/4kN8Z7ZtxyvUeOGqb3LoeN5hlV9dGq+oOq+t6hbL7/wcn8f2MbzYHGJUleNtye+paawmE1DsCk2Iw7LcnbxuY383tm3N5xSTbxe2bSZ5ru/qPs+7MwbBa/lqV/R9yoOskfVdWNVXX2UHagn/83muX67rzRjffDmzo2y5hXYC8bPaG9aVXVo5L8XpKXd/cXx5cNV0D2mjRsjVTVjyS5p7tvXOu2rENbMrp99c3d/ZQkX87oVqAHbNL3zBEZnR09NskTkhyW6b/ifMVsxvcIHKAPZzT8wZOTvCnJ7w/l+/0fnGzov7HFxOXNSf5hkhMy+mJwwSq2dzXNF5skyTDW4o8l+d1JK2/C90ySeeOyqd8zkz7TVNW/XKtGwnrhO+J+/UB3PzXJc5OcU1U/OL5wA/cz++K7837s6/PJZoyNvMLK2egJ7Z1JjhmbP3oo29Cq6mEZJbMv7+53DMV377l9Y/h5z1A+X4w2WuyemeTHqmpHRkPPPCvJGzK6rWXLUGf8GB84/mH5o5N8PhsvLsnobOAd3X3DMH9VRp30Zn/P/FCST3f3Z7v760nekdH7yHvmW5brPbJzmN67HDaM7v5id+8apq9L8rCqelzm/x+czP83tmEsJi7dfXd3f6O7v5nkNzOFwzgtxD5is8dzk3y4u+8eK9vM75k9HhIX75mJn2n+cUafU+b7XAObwXJ9R9yQhitL0933JHlnRv87D/Tz/0azXN+dN7K9++HNHpvlyiuwl42e0P5gkuOGp4c+PKPbHq5Z4zatqGFsnYuT3Nrdvzq26JokZwzTZyS5eqz8JTVyYka3IN6Z5N1JnlNVRwxnlJ4zlE2l7n5ldx/d3Vszeh+8t7tPT/K+JC8Yqu0dlz3xesFQv4fy02r05NljkxyX0cPsplZ335Xk9qr67qHopCSfyCZ/z2R0S9CJVfXI4e9qT1w2/XtmzLK8R4ZlX6yqE4dYv2RsW7AhVNV37Bn/rqqeltFnsM/v439wMv/f2IaxmLjUg8dX/GdJPr6KTV4188VmrMqL89BhNTbte2asykPi4j0z8TPNrcPnlPk+18CGt4zfETecqjqsqr5tz3RGn9s/ngP//L+hLON3541s7354s8dmufIK7K3XwZMpV/KV0ZNT/yrJp5L8+7Vuzyoc7w9kdAvHx5LcNLxOyWjMnfck+WSSP07ymKF+JfmNIT43J9k2tq2fyOgBdtuTvHStj20ZYzSbbz3B+h9klFzcntEtMYcM5Y8Y5rcPy//B2Pr/fojXXyZ57lofzzLF5IQkHxreN7+f5AjvmU6SX0ryFxl9eHtrkkM263smow8ldyb5ekZXJpy1nO+RJNuGOH8qya8nqbU+Zi+vA3nN8zfy00l+elj+siS3JPlokg8k+cdj6z7kf/BQPvFvbJpeKxSXtw7/Wz6W0Yf+I9f6ONcgNodllKh89F7b3Ozvmfni4j0z4TPNUD7xc42X12Z7ZYnfETfaa4jBR4fXLRlyKfP1M9nH5/+N9pr0+URcHojNQ/phsVm+vILXg181BAwAAAAAANa1jT7kCAAAAAAAG4SENgAAAAAAU0FCGwAAAACAqSChDQAAAADAVJDQBgAAYMGq6i1VdU9VfXwBdS+sqpuG119V1X2r0EQAYAOr7l7rNgAAADAlquoHk+xKcll3f98BrPczSZ7S3T+xYo0DADY8V2gDAACwYN39p0m+MF5WVf+wqv6wqm6sqv9RVd8zYdUXJ3nbqjQSANiwtqx1AwAAAJh6FyX56e7+ZFU9Pcl/TvKsPQur6juTHJvkvWvUPgBgg5DQBgAAYNGq6lFJ/nGS362qPcWH7FXttCRXdfc3VrNtAMDGI6ENAADAUhyU5L7uPmEfdU5Lcs7qNAcA2MiMoQ0AAMCidfcXk3y6ql6YJDXy5D3Lh/G0j0jyZ2vURABgA5HQBgAAYMGq6m0ZJae/u6ruqKqzkpye5Kyq+miSW5KcOrbKaUmu6O5e/dYCABtN+UwBAAAAAMA0cIU2AAAAAABTQUIbAAAAAICpIKENAAAAAMBUkNAGAAAAAGAqSGgDAAAAADAVJLQBAAAAAJgKEtoAAAAAAEwFCW0AAAAAAKaChDYAAAAAAFNBQhsAAAAAgKkgoQ0AAAAAwFSQ0AYAAAAAYCpIaAMAAAAAMBUktAEAAAAAmAoS2gAAAAAATAUJbQAAAAAApoKENgAAAAAAU0FCGwAAAACAqSChDQAAAADAVJDQBgAAAABgKkhoAwAAAAAwFSS0AQAAAACYChLaAAAAAABMBQltAAAAAACmgoQ2AAAAAABTQUIbAAAAAICpIKENAAAAAMBUkNAGAAAAAGAqSGgDAAAAADAVJLQBAAAAAJgKEtoAAAAAAEwFCW0AAAAAAKaChDYAAAAAAFNBQhsAAAAAgKkgoQ0AAAAAwFSQ0AYAAAAAYCpIaAMAAAAAMBUktAEAAAAAmAoS2gAAAACwD1W1taq6qrasdVtgs5PQhjVWVXNVdW9VHbLWbQEAJquqXWOvb1bV347Nn77W7QMAFqaqdoz14/dW1bVVdcxatwtYOAltWENVtTXJ/5Gkk/zY2rYGAJhPdz9qzyvJXyf50bGyy1d6/64GA4Bl9aNDn35kkruTvGmN2wMcAAltWFsvSfKBJJckOWNPYVU9tqr+e1V9sao+WFWvqar3jy3/nqq6vqq+UFV/WVUvWv2mAwBVdVBVnVdVn6qqz1fVlVX1mGHZnluTz6iqv66qz1XVvx9b95Kqes3Y/GxV3TE2v6OqXlFVH0vy5araUlUnVtX/qqr7quqjVTW7iocLABtKd381yVVJnpQkVfW8qvrI8F389qp69XzrVtVLq+rWqvpSVd1WVT81tmy2qu6oqnOr6p6qurOqXjq2/NCquqCqPlNV91fV+6vq0GGZvh72Q0Ib1tZLklw+vH64qmaG8t9I8uUk35FRons82X1YkuuT/E6Sv5fktCT/uaqetIrtBgBGfibJ85P8kyRPSHJvRv34uB9I8t1JTkryH6vqfzuA7b84yfOSHJ5kJsm1SV6T5DFJfiHJ71XV4xfffADYvKrqkUn+eUYXmiWj7+EvyajffV6S/7uqnj/P6vck+ZEk357kpUkurKqnji3/jiSPTnJUkrOS/EZVHTEse32S70/yjzPq0/9dkm9W1VHR18N+SWjDGqmqH0jynUmu7O4bk3wqyb+oqoOT/F9JXtXdX+nuTyS5dGzVH0myo7t/u7t3d/dHkvxekheu8iEAAMlPJ/n33X1Hd38tyauTvGCvIUJ+qbv/trs/muSjSZ58ANt/Y3ff3t1/m+RfJrmuu6/r7m929/VJPpTklOU5FADYNH6/qu5Lcn+SZyf5/yVJd891981DP/uxJG/L6KT1Q3T3td39qR75kyR/lNGQont8Pckvd/fXu/u6JLuSfHdVHZTkJ5L8XHfv7O5vdPf/Gj5H6OthASS0Ye2ckeSPuvtzw/zvDGWPT7Ilye1jdcenvzPJ04fbj+4bOuHTMzr7CwCsru9M8s6xPvnWJN/I6GrqPe4am/5KkkcdwPb3/gzwwr0+A/xARuN/AgAL9/zuPjzJI5K8LMmfVNV3VNXTq+p9VfXZqro/oxPXj5u0gap6blV9YBgK9L6Mks7jdT/f3bvH5vd8BnjcsN9PTdisvh4WwMNlYA0MY2O9KMnBVbXnS+4h+dbtxLuTHJ3kr4Zl409cvj3Jn3T3s1entQDAPtye5Ce6+3/uvWB4+PO+fDnJI8fmJ52c7r329dbu/skDbSQA8FDd/Y0k76iq/5pR4vh1SX49yXO7+6tV9WuZkNCuqkMyulP6JUmu7u6vV9XvJ6kF7PZzSb6a5B9mdOfWOH09LIArtGFtPD+jq7eelOSE4fW/JfkfGXWI70jy6qp6ZFV9z1C2x7uSfFdV/XhVPWx4/aMDHI8TAFge/yXJa6vqO5Okqh5fVacucN2bkpxSVY+pqu9I8vL91P9vSX60qn64qg6uqkcMD506erGNB4DNrEZOTXJERndZfVuSLwzJ7Kcl+RfzrPrwjC5K+2yS3VX13CTPWcg+u/ubSd6S5Fer6glDn/6MIUmur4cFkNCGtXFGkt/u7r/u7rv2vDI6E3x6Rrc8PTqjW5TfmtG4XV9Lku7+UkYd5WlJ/mao87qMOlMAYHW9Ick1Sf6oqr6U0UOlnr7Add+a0ZVZOzIad/Pt+6rc3bcnOTXJL2b0Bfr2JP82PtMDwIH671W1K8kXk7w2yRndfUuSf53kl4c+/T8muXLSysP38p8dlt+bUeL7mgPY/y8kuTnJB5N8IaPv9Afp62Fhqrv3XwtYU1X1uiTf0d1nrHVbAAAAAGCtOMMD61BVfU9V/e/D7U9PS3JWkneudbsAAAAAYC15KCSsT9+W0TAjT0hyd5ILkly9pi0CAAAAgDVmyBEAAAAAAKaCIUcAAAAAAJgK63rIkcc97nG9devWVdvfl7/85Rx22GGrtr/lNM1tT6a7/dPc9kT719I0tz1Z3fbfeOONn+vux6/Kzlhxy9W/T/vf0EoRl/mJzWTiMj+xmWy54qJ/31j076tHjBZGnBZGnBZGnBZmtfr2dZ3Q3rp1az70oQ+t2v7m5uYyOzu7avtbTtPc9mS62z/NbU+0fy1Nc9uT1W1/VX1mVXbEqliu/n3a/4ZWirjMT2wmE5f5ic1kyxUX/fvGon9fPWK0MOK0MOK0MOK0MKvVtxtyBAAAADaAqnpEVf15VX20qm6pql8ayo+tqhuqantVvb2qHj6UHzLMbx+Wbx3b1iuH8r+sqh9eo0MCgIeQ0AYAAICN4WtJntXdT05yQpKTq+rEJK9LcmF3PzHJvUnOGuqfleTeofzCoV6q6klJTkvyvUlOTvKfq+rg1TwQAJiPhDYAAABsAD2ya5h92PDqJM9KctVQfmmS5w/Tpw7zGZafVFU1lF/R3V/r7k8n2Z7kaSt/BACwfxLaAAAAsEFU1cFVdVOSe5Jcn+RTSe7r7t1DlTuSHDVMH5Xk9iQZlt+f5LHj5RPWAYA1ta4fCgkAAAAsXHd/I8kJVXV4kncm+Z6V2ldVnZ3k7CSZmZnJ3Nzckre5a9euZdnORiZGCyNOCyNOCyNO64uENgAAAGww3X1fVb0vyTOSHF5VW4arsI9OsnOotjPJMUnuqKotSR6d5PNj5XuMrzO+j4uSXJQk27Zt69nZ2SW3e25uLsuxnY1MjBZGnBZGnBZGnNYXQ44AAADABlBVjx+uzE5VHZrk2UluTfK+JC8Yqp2R5Oph+pphPsPy93Z3D+WnVdUhVXVskuOS/PmqHAQA7IcrtAEAAGBjODLJpVV1cEYXsF3Z3e+qqk8kuaKqXpPkI0kuHupfnOStVbU9yReSnJYk3X1LVV2Z5BNJdic5ZxjKBADWnIQ2AAAAbADd/bEkT5lQfluSp00o/2qSF86zrdcmee1ytxEAlsqQIwAAAAAATAUJbQAAAAAApoKENgAAAAAAU8EY2izK1vOuXdR6O85/3jK3BAAYd/PO+3PmIvppfTQA64F+DID9kdAGAABWjQsjAABYCkOOAAAAAAAwFSS0AQAAAACYCoYcAQBg0RY7fERiCAkAAODASWhvckv5EgoAAAAAsJoktAEAFsCD7AAAANaehDYAAMAEN++8P2cu4mSWE1kAACvHQyEBAAAAAJgKrtAGAIAVYJgaAABYfq7QBgAAAABgKkhoAwAAAAAwFSS0AYAHqap/U1W3VNXHq+ptVfWIqjq2qm6oqu1V9faqevhQ95BhfvuwfOsaNx8AAIANTEIbAHhAVR2V5GeTbOvu70tycJLTkrwuyYXd/cQk9yY5a1jlrCT3DuUXDvUAAABgRXgoJACwty1JDq2qryd5ZJI7kzwryb8Yll+a5NVJ3pzk1GE6Sa5K8utVVd3dq9lggI3Ag0QBAPZPQhsAeEB376yq1yf56yR/m+SPktyY5L7u3j1UuyPJUcP0UUluH9bdXVX3J3lsks+Nb7eqzk5ydpLMzMxkbm5uyW3dtWvXsmxnoc49fvf+K02wmm1MkplDF9fWxbZzsXFZyj4Xy3tmMnGZ37T8PW309wwAwDgJbQDgAVV1REZXXR+b5L4kv5vk5KVut7svSnJRkmzbtq1nZ2eXusnMzc1lObazUGcu9srJ02eXtyH78abLr84FNx/4R7zFtnOxcVnKPhfLe2YycZnftPw9bfT3DADAOGNoAwDjfijJp7v7s9399STvSPLMJIdX1Z6sztFJdg7TO5MckyTD8kcn+fzqNhkAAIDNQkIbABj310lOrKpHVlUlOSnJJ5K8L8kLhjpnJLl6mL5mmM+w/L3GzwYAAGClSGgDAA/o7hsyerjjh5PcnNFnhYuSvCLJz1fV9ozGyL54WOXiJI8dyn8+yXmr3mgAAAA2DWNoAwAP0t2vSvKqvYpvS/K0CXW/muSFq9EuAAAAkNAGAIBN7Oad9y/qYYQ7zn/eCrQGAAD2zZAjAAAAAABMBQltAAAAAACmgoQ2AAAAAABTQUIbAAAAAICpsN+HQlbVW5L8SJJ7uvv7hrLHJHl7kq1JdiR5UXffW1WV5A1JTknylSRndveHh3XOSPIfhs2+prsvXd5DAQCA+Xn4IQAATL+FXKF9SZKT9yo7L8l7uvu4JO8Z5pPkuUmOG15nJ3lz8kAC/FVJnp7kaUleVVVHLLXxAAAAAABsHvtNaHf3nyb5wl7FpybZc4X1pUmeP1Z+WY98IMnhVXVkkh9Ocn13f6G7701yfR6aJAcAAAAAgHktdgztme6+c5i+K8nMMH1UktvH6t0xlM1XDgAAAAAAC7LfMbT3p7u7qno5GpMkVXV2RsOVZGZmJnNzc8u16f3atWvXqu5vOS227ecev3v5G7MP87VxM8Z+vdD+tTPNbU+mv/0AAADA9FlsQvvuqjqyu+8chhS5ZyjfmeSYsXpHD2U7k8zuVT43acPdfVGSi5Jk27ZtPTs7O6naipibm8tq7m85Lbbti3kw0pLc/OWJxece/41c8P7Jy5L1/TCmaX7fJNq/lqa57cn0tx8AAACYPosdcuSaJGcM02ckuXqs/CU1cmKS+4ehSd6d5DlVdcTwMMjnDGUAAAAAALAg+71Cu6reltHV1Y+rqjuSvCrJ+UmurKqzknwmyYuG6tclOSXJ9iRfSfLSJOnuL1TVryT54FDvl7t77wdNAgAAAADAvPab0O7uF8+z6KQJdTvJOfNs5y1J3nJArQMAAAD2q6qOSXJZkpkkneSi7n5DVb06yU8m+exQ9Re7+7phnVcmOSvJN5L8bHe/eyg/Ockbkhyc5Le6+/zVPBYA2JclPxQSAAAAWHO7k5zb3R+uqm9LcmNVXT8su7C7Xz9euaqelOS0JN+b5AlJ/riqvmtY/BtJnp3kjiQfrKpruvsTq3IUALAfEtoAAAAw5YbnV905TH+pqm5NctQ+Vjk1yRXd/bUkn66q7UmeNizb3t23JUlVXTHUldAGYF2Q0AYAAIANpKq2JnlKkhuSPDPJy6rqJUk+lNFV3PdmlOz+wNhqd+RbCfDb9yp/+jz7OTvJ2UkyMzOTubm5Jbd95tDk3ON3H/B6y7HvabFr165NdbyLJU4LI04LI07ri4Q2AAAAbBBV9agkv5fk5d39xap6c5JfyWhc7V9JckGSn1iOfXX3RUkuSpJt27b17Ozskrf5psuvzgU3H3iqYsfpS9/3tJibm8tyxHqjE6eFEaeFEaf1RUIbAAAANoCqelhGyezLu/sdSdLdd48t/80k7xpmdyY5Zmz1o4ey7KMcANbcQWvdAAAAAGBpqqqSXJzk1u7+1bHyI8eq/bMkHx+mr0lyWlUdUlXHJjkuyZ8n+WCS46rq2Kp6eEYPjrxmNY4BABbCFdoAAAAw/Z6Z5MeT3FxVNw1lv5jkxVV1QkZDjuxI8lNJ0t23VNWVGT3scXeSc7r7G0lSVS9L8u4kByd5S3ffsnqHAQD7JqG9AWw979qce/zunHnetWvdFAAAANZAd78/SU1YdN0+1nltktdOKL9uX+sBwFoy5AgA8ICq+u6qumns9cWqenlVPaaqrq+qTw4/jxjqV1W9saq2V9XHquqpa30MAAAAbFwS2gDAA7r7L7v7hO4+Icn3J/lKkncmOS/Je7r7uCTvGeaT5LkZjbl5XJKzk7x51RsNAADApiGhDQDM56Qkn+ruzyQ5NcmlQ/mlSZ4/TJ+a5LIe+UCSw/d6+BQAAAAsG2NoAwDzOS3J24bpme6+c5i+K8nMMH1UktvH1rljKLtzrCxVdXZGV3BnZmYmc3NzS27crl27lmU7C3Xu8bsXtd5qtjFJZg5dXFsX287FxmUp+1ysaYmNuCzv/pZCbCZb7f+/AADjJLQBgIeoqocn+bEkr9x7WXd3VfWBbK+7L0pyUZJs27atZ2dnl9zGubm5LMd2FmqxD1/ecfrs8jZkP950+dW54OYD/4i32HYu5aHUYrO8+1sscZmf2Ey22v9/AQDGGXIEAJjkuUk+3N13D/N37xlKZPh5z1C+M8kxY+sdPZQBAADAspPQBgAmeXG+NdxIklyT5Ixh+owkV4+Vv6RGTkxy/9jQJAAAALCsDDkCADxIVR2W5NlJfmqs+PwkV1bVWUk+k+RFQ/l1SU5Jsj3JV5K8dBWbCgAAwCYjoQ0APEh3fznJY/cq+3ySkybU7STnrFLTAAAA2OQMOQIAAAAAwFSQ0AYAAAAAYCpIaAMAAAAAMBUktAEAAAAAmAoS2gAAAAAATAUJbQAAAAAApoKENgAAAAAAU0FCGwAAAACAqSChDQAAAADAVJDQBgAAAABgKkhoAwAAAAAwFSS0AQAAAACYChLaAAAAAABMBQltAAAAAACmgoQ2AAAAAABTQUIbAAAAAICpIKENAAAAAMBUkNAGAAAAAGAqSGgDAAAAADAVJLQBAAAAAJgKEtoAAAAAAEyFJSW0q+rfVNUtVfXxqnpbVT2iqo6tqhuqantVvb2qHj7UPWSY3z4s37osRwAAAAAAwKaw6IR2VR2V5GeTbOvu70tycJLTkrwuyYXd/cQk9yY5a1jlrCT3DuUXDvUAgHWmqg6vqquq6i+q6taqekZVPaaqrq+qTw4/jxjqVlW9cThh/bGqeupatx8AAICNa6lDjmxJcmhVbUnyyCR3JnlWkquG5Zcmef4wfeown2H5SVVVS9w/ALD83pDkD7v7e5I8OcmtSc5L8p7uPi7Je4b5JHlukuOG19lJ3rz6zQUAAGCzWHRCu7t3Jnl9kr/OKJF9f5Ibk9zX3buHanckOWqYPirJ7cO6u4f6j13s/gGA5VdVj07yg0kuTpLu/rvuvi8PPjG99wnry3rkA0kOr6ojV7XRAAAAbBpbFrvicKvxqUmOTXJfkt9NcvJSG1RVZ2d0hVdmZmYyNze31E0u2K5du1Z1f8vl3ON3Z+bQ0c9ptb/2r+ffy7S+b/bQ/rUzzW1Ppr/9zOvYJJ9N8ttV9eSMTlb/XJKZ7r5zqHNXkplh+oET1oM9J7PvHCtbkf59td+Di+1nV/vvZLGfCRbbzqV8/hCb5d3fYonL/MRmMp8BAIC1tOiEdpIfSvLp7v5sklTVO5I8M6Mrs7YMV2EfnWTnUH9nkmOS3DEMUfLoJJ/fe6PdfVGSi5Jk27ZtPTs7u4QmHpi5ubms5v6Wy5nnXZtzj9+dC25eyq9zbe2v/TtOn129xhygaX3f7KH9a2ea255Mf/uZ15YkT03yM919Q1W9Id8aXiRJ0t1dVX0gG12J/n2134NnnnftotZb7T7sTZdfvajPBItt52LjspR9Lta0xEZclnd/SyE2k/kMAACspaWMof3XSU6sqkcOY2GflOQTSd6X5AVDnTOSXD1MXzPMZ1j+3u4+oC/DAMCKuyPJHd19wzB/VUYJ7rv3DCUy/LxnWL7nhPUe4yezAYBVVFXHVNX7quoTVXVLVf3cUH7AD3euqjOG+p+sqjPm2ycArLaljKF9Q0Zfcj+c5OZhWxcleUWSn6+q7RmNkX3xsMrFSR47lP989rraCwBYe919V5Lbq+q7h6I9J6zHT0zvfcL6JcMX4hOT3D82NAkAsLp2Jzm3u5+U5MQk51TVk3KAD3euqsckeVWSpyd5WpJX7UmCA8BaW9IYFd39qow6uXG3ZdTh7V33q0leuJT9AQCr4meSXF5VD8+oX39pRieur6yqs5J8JsmLhrrXJTklyfYkXxnqAgBrYDipfOcw/aWqujWjZ1ucmmR2qHZpkrmMLkZ74OHOST5QVXse7jyb5Pru/kKSVNX1GT0z622rdjAAMI/pHXQZAFgR3X1Tkm0TFp00oW4nOWel2wQAHJiq2prkKUluyIE/3Hm+cgBYcxLaAAAAsIFU1aOS/F6Sl3f3F0ePvRpZzMOd97GfszMaqiQzMzOZm5tb8jZnDk3OPX73Aa+3HPueFrt27dpUx7tY4rQw4rQw4rS+SGgDAADABlFVD8somX15d79jKL67qo7s7jsX+HDnnfnWECV7yuf23ld3X5TRs7Sybdu2np2d3bvKAXvT5VfngpsPPFWx4/Sl73tazM3NZTlivdGJ08KI08KI0/qy6IdCAgAAAOtHjS7FvjjJrd39q2OLDvThzu9O8pyqOmJ4GORzhjIAWHOu0AYAAICN4ZlJfjzJzVV101D2i0nOzwE83Lm7v1BVv5Lkg0O9X97zgEgAWGsS2gAAALABdPf7k9Q8iw/o4c7d/ZYkb1m+1gHA8jDkCAAAAAAAU0FCGwAAAACAqSChDQAAAADAVJDQBgAAAABgKkhoAwAAAAAwFSS0AQAAAACYChLaAAAAAABMBQltAAAAAACmgoQ2AAAAAABTQUIbAAAAAICpsGWtGwALsfW8axe13o7zn7fMLQEAAAAA1oortAEAAAAAmAoS2gAAAAAATAUJbQAAAAAApoKENgAAAAAAU0FCGwAAAACAqSChDQAAAADAVJDQBgAepKp2VNXNVXVTVX1oKHtMVV1fVZ8cfh4xlFdVvbGqtlfVx6rqqWvbegAAADayLWvdAL5l63nXrnUTAGCPf9rdnxubPy/Je7r7/Ko6b5h/RZLnJjlueD09yZuHnwAAALDsXKENACzEqUkuHaYvTfL8sfLLeuQDSQ6vqiPXoH0AAABsAhLaAMDeOskfVdWNVXX2UDbT3XcO03clmRmmj0py+9i6dwxlAAAAsOwMOQIA7O0HuntnVf29JNdX1V+ML+zurqo+kA0OifGzk2RmZiZzc3NLbuSuXbuWZTsLde7xuxe13mq2MUlmDl1cWxfbzsXGZSn7XKxpiY24LO/+lkJsJlvt/78AAOMktAGAB+nuncPPe6rqnUmeluTuqjqyu+8chhS5Z6i+M8kxY6sfPZTtvc2LklyUJNu2bevZ2dklt3Nubi7LsZ2FOnORz7rYcfrs8jZkP950+dW54OYD/4i32HYuNi5L2ediTUtsxGV597cUYjPZav//BQAYZ8gRAOABVXVYVX3bnukkz0ny8STXJDljqHZGkquH6WuSvKRGTkxy/9jQJAAAALCsXKENAIybSfLOqkpGnxN+p7v/sKo+mOTKqjoryWeSvGiof12SU5JsT/KVJC9d/SYDAACwWUhoAwAP6O7bkjx5Qvnnk5w0obyTnLMKTQMAAABDjgAAAAAAMB0ktAEAAAAAmAoS2gAAAAAATAUJbQAAAAAApoKENgAAAAAAU0FCGwAAAACAqbCkhHZVHV5VV1XVX1TVrVX1jKp6TFVdX1WfHH4eMdStqnpjVW2vqo9V1VOX5xAAAAAAANgMlnqF9huS/GF3f0+SJye5Ncl5Sd7T3cclec8wnyTPTXLc8Do7yZuXuG8AAAAAADaRRSe0q+rRSX4wycVJ0t1/1933JTk1yaVDtUuTPH+YPjXJZT3ygSSHV9WRi90/AAAAAACby5YlrHtsks8m+e2qenKSG5P8XJKZ7r5zqHNXkplh+qgkt4+tf8dQdudYWarq7Iyu4M7MzEzm5uaW0MQDs2vXrlXd397OPX73otedOXRp66+1lWr/avw+1/p9s1Tav3amue3J9LcfAAAAmD5LSWhvSfLUJD/T3TdU1RvyreFFkiTd3VXVB7LR7r4oyUVJsm3btp6dnV1CEw/M3NxcVnN/ezvzvGsXve65x+/OBTcv5de5tlaq/TtOn132be5trd83S6X9a2ea255Mf/sBADaaqnpLkh9Jck93f99Q9uokP5nRBWlJ8ovdfd2w7JVJzkryjSQ/293vHspPzmiI0YOT/FZ3n7+axwEA+7KUMbTvSHJHd98wzF+VUYL77j1DiQw/7xmW70xyzNj6Rw9lAAAAwNJdkuTkCeUXdvcJw2tPMvtJSU5L8r3DOv+5qg6uqoOT/EZGz8F6UpIXD3UBYF1YdEK7u+9KcntVffdQdFKSTyS5JskZQ9kZSa4epq9J8pIaOTHJ/WNDkwAAAABL0N1/muQLC6x+apIruvtr3f3pJNuTPG14be/u27r775JcMdQFgHVhqWM8/EySy6vq4UluS/LSjJLkV1bVWUk+k+RFQ93rkpySUSf5laEuAAAAsLJeVlUvSfKhJOd2970ZPdPqA2N19jznKnno86+eviqtBIAFWFJCu7tvSrJtwqKTJtTtJOcsZX8AAADAAXlzkl9J0sPPC5L8xHJsuKrOTnJ2kszMzCzLA8NnDh09Y+lAbaaHlXs4+8KI08KI08KI0/oyvU8RBAAAAPapu+/eM11Vv5nkXcPsvp5ztaDnX3X3RUkuSpJt27b1cjww/E2XX50Lbj7wVMWO05e+72nh4ewLI04LI04LI07ry1IeCgkAAACsY1V15NjsP0vy8WH6miSnVdUhVXVskuOS/HmSDyY5rqqOHYYXPW2oCwDrgiu0AQAAYAOoqrclmU3yuKq6I8mrksxW1QkZDTmyI8lPJUl331JVVyb5RJLdSc7p7m8M23lZkncnOTjJW7r7ltU9EgCYn4Q2AAAAbADd/eIJxRfvo/5rk7x2Qvl1Sa5bxqYBwLIx5AgAAAAAAFNBQhsAAAAAgKkgoQ0AAAAAwFSQ0AYAHqKqDq6qj1TVu4b5Y6vqhqraXlVvr6qHD+WHDPPbh+Vb17ThAAAAbGgS2gDAJD+X5Nax+dclubC7n5jk3iRnDeVnJbl3KL9wqAcAAAArQkIbAHiQqjo6yfOS/NYwX0meleSqocqlSZ4/TJ86zGdYftJQHwAAAJadhDYAsLdfS/LvknxzmH9skvu6e/cwf0eSo4bpo5LcniTD8vuH+gAAALDstqx1AwCA9aOqfiTJPd19Y1XNLuN2z05ydpLMzMxkbm5uydvctWvXsmxnoc49fvf+K02wmm1MkplDF9fWxbZzsXFZyj4Xa1piIy7Lu7+lEJvJVvv/LwDAOAltAGDcM5P8WFWdkuQRSb49yRuSHF5VW4arsI9OsnOovzPJMUnuqKotSR6d5PN7b7S7L0pyUZJs27atZ2dnl9zQubm5LMd2FurM865d1Ho7Tp9d3obsx5suvzoX3HzgH/EW287FxmUp+1ysaYmNuCzv/pZio8dm6yL3d8nJj1rV/78AAOMMOQIAPKC7X9ndR3f31iSnJXlvd5+e5H1JXjBUOyPJ1cP0NcN8huXv7e5exSYDAACwiUhoAwAL8YokP19V2zMaI/viofziJI8dyn8+yXlr1D4AAAA2AUOOsKEt9jbKHec/b5lbAjB9unsuydwwfVuSp02o89UkL1zVhgEAALBpuUIbAAAAAICpIKENAAAAAMBUkNAGAAAAAGAqSGgDAAAAADAVJLQBAAAAAJgKEtoAAAAAAEwFCW0AAAAAAKaChDYAAAAAAFNBQhsAAAAAgKkgoQ0AAAAAwFSQ0AYAAAAAYCpIaAMAAAAAMBUktAEAAAAAmAoS2gAAAAAATIUta90AAIDFuHnn/TnzvGsPeL0d5z9vBVoDAADAanCFNgAAAAAAU0FCGwAAAACAqSChDQAAAADAVJDQBgAAAABgKkhoAwAAAAAwFSS0AQAAAACYChLaAAAAAABMhSUntKvq4Kr6SFW9a5g/tqpuqKrtVfX2qnr4UH7IML99WL51qfsGAJZXVT2iqv68qj5aVbdU1S8N5fp3AAAA1txyXKH9c0luHZt/XZILu/uJSe5NctZQflaSe4fyC4d6AMD68rUkz+ruJyc5IcnJVXVi9O8AAACsA0tKaFfV0Umel+S3hvlK8qwkVw1VLk3y/GH61GE+w/KThvoAwDrRI7uG2YcNr47+HQAAgHVgyxLX/7Uk/y7Jtw3zj01yX3fvHubvSHLUMH1UktuTpLt3V9X9Q/3PjW+wqs5OcnaSzMzMZG5ubolNXLhdu3at6v72du7xu/dfaR4zhy5t/bW23tp/IO+DtX7fLJX2r51pbnsy/e1nflV1cJIbkzwxyW8k+VSW2L8DACuvqt6S5EeS3NPd3zeUPSbJ25NsTbIjyYu6+97hBPQbkpyS5CtJzuzuDw/rnJHkPwybfU13XxoAWCcWndCuqj2d5I1VNbtcDerui5JclCTbtm3r2dll2/R+zc3NZTX3t7czz7t20euee/zuXHDzUs9PrJ311v4dp88uuO5av2+WSvvXzjS3PZn+9jO/7v5GkhOq6vAk70zyPUvd5kqcsF7sydDF7nuxJ15X+8TPtMRlKftcrGmJjbgs7/6WYqPHZrH7c1J7Xbskya8nuWys7Lwk7+nu86vqvGH+FUmem+S44fX0JG9O8vQhAf6qJNsyukvrxqq6prvvXbWjAIB9WEoG8ZlJfqyqTknyiCTfntHZ3cOrastwFdfRSXYO9XcmOSbJHVW1Jcmjk3x+CfsHAFZQd99XVe9L8owssX9fiRPWb7r86kWdDD2Qk5bjFnviebH7W6xpictS9rlY0xIbcVne/S3FRo/NYvd3ycmHOam9TnX3n054QPOpSWaH6UuTzGWU0D41yWXd3Uk+UFWHV9WRQ93ru/sLSVJV1yc5OcnbVrr9ALAQi05od/crk7wySYYrtH+hu0+vqt9N8oIkVyQ5I8nVwyrXDPN/Nix/79BxAgDrRFU9PsnXh2T2oUmendGDHt8X/TsATKOZ7r5zmL4rycww/cCwYYM9Q4rNV/4QG+EOrGnkLomFEaeFEaeFEaf1ZSXGeHhFkiuq6jVJPpLk4qH84iRvrartSb6Q5LQV2DcAsDRHJrl0GEf7oCRXdve7quoT0b8DwFTr7q6qZTvxvBHuwJpGhv5bGHFaGHFaGHFaX5Ylod3dcxndtpTuvi3J0ybU+WqSFy7H/ta7rUu49RYA1lJ3fyzJUyaUb/r+HQCm1N1VdWR33zkMKXLPUL5n2LA99gwptjPfGqJkT/ncKrQTABbkoLVuAAAAALBi9gwPljx02LCX1MiJSe4fhiZ5d5LnVNURVXVEkucMZQCwLqzEkCMAAADAKquqt2V0dfXjquqOJK9Kcn6SK6vqrCSfSfKiofp1SU5Jsj3JV5K8NEm6+wtV9StJPjjU++U9D4gEgPVAQhsAAAA2gO5+8TyLTppQt5OcM8923pLkLcvYNABYNoYcAQAAAABgKkhoAwAAAAAwFQw5AhNsPe/aBdc99/jdOXOov+P8561UkwAAAABg03OFNgAAAAAAU0FCGwAAAACAqSChDQAAAADAVJDQBgAAAABgKkhoAwAAAAAwFSS0AQAAAACYChLaAAAAAABMBQltAAAAAACmgoQ2AAAAAABTQUIbAAAAAICpIKENAAAAAMBUkNAGAAAAAGAqSGgDAAAAADAVJLQBAAAAAJgKEtoAAAAAAEwFCW0AAAAAAKaChDYA8ICqOqaq3ldVn6iqW6rq54byx1TV9VX1yeHnEUN5VdUbq2p7VX2sqp66tkcA/3/27j/etrquE//rLfiD0AR/dAeBxJJ+WIw/5g7St6a5SSpiE85MGWYK5gwzDVbO0A9s+o6mOUPzTcusLFICDUPSDFLKCD3j1AQqiiCSeUMM7oCUIHq1rGvv7x97Xdwe97n33HPOPeess5/Px2M/zlqf9euzPnuf/VnrvT+fzwIAALYyAW0AYNqeJOd092OSnJTk7Kp6TJJzk1zV3ccnuWqYT5KnJTl+eJ2V5DXrn2UAAADmhYA2AHCv7r69u98/TH8myU1Jjk5yWpKLhtUuSvKMYfq0JK/viauTHFFVR61vrgEAAJgXAtoAwExVdVySxye5Jsm27r59WHRHkm3D9NFJbp3a7LYhDQAAANbcoRudAQBg86mqByZ5S5IXdvenq+reZd3dVdUHuL+zMhmSJNu2bcvCwsKq87jtsOScE/Yc8HYrPfZKjrWa463UWMplNcdcqbGUjXJZ2+OtxlYvm5Ueb/fu3RvyfgAAJALaAMAiVXXfTILZF3f37w7Jn6iqo7r79mFIkTuH9F1Jjp3a/Jgh7Ut09/lJzk+S7du3944dO1adz1dffFleccOBX8rc8uyVHfvMc9++ou1WeryVGku5rOaYKzWWslEua3u81djqZbPS4114yuFZi+9xgDE6bqXf1ec9fY1zAvNLQBsAuFdNmmK/LslN3f3KqUWXJzkjyXnD38um0l9QVZckeWKSe6aGJgEAACIQDmtJQBsAmPatSZ6T5Iaqum5I+6lMAtmXVtXzk3w8yTOHZVckOTXJziSfS/K8dc0tAAAAc0VAGwC4V3f/SZJaYvHJM9bvJGcf1EwBAMAaW2mLaWDjCWjDGtKFCAAAAAAOnvtsdAYAAAAAAGA5BLQBAAAAABgFAW0AAAAAAEZBQBsAAAAAgFEQ0AYAAAAAYBQOXemGVXVsktcn2Zakk5zf3a+qqockeVOS45LckuSZ3X13VVWSVyU5NcnnkpzZ3e9fXfYBAAAAmEfHnfv2JZedc8KenLmP5cB4raaF9p4k53T3Y5KclOTsqnpMknOTXNXdxye5aphPkqclOX54nZXkNas4NgAAAAAAc2bFLbS7+/Yktw/Tn6mqm5IcneS0JDuG1S5KspDkJ4f013d3J7m6qo6oqqOG/QAAAAAHSVXdkuQzSb6QZE93b9fDGja/fbVC35dbznv6GucENo81GUO7qo5L8vgk1yTZNhWkviOTIUmSSbD71qnNbhvSAAAAgIPvO7r7cd29fZjXwxqA0VlxC+29quqBSd6S5IXd/enJD7kT3d1V1Qe4v7MyqTCzbdu2LCwsrDaLy7Z79+41Od45J+xZfWYO0LbDNua4a2XM+V+LvK/n53yxtfrcb5Qx53/MeU/Gn38AAPSwBmB8VhXQrqr7ZhLMvri7f3dI/sTeiq6qjkpy55C+K8mxU5sfM6R9ie4+P8n5SbJ9+/besWPHarJ4QBYWFrIWx9uIhw6cc8KevOKGVf8+sWHGnP81yfsNn13RZmvRhWitPvcbZcz5H3Pek/HnHwBgznSSPxoanf36cO99oD2svySgfTAapK20wdA8NbTQsOSL9vVZGXPDubWw3M+Iz9PyKKfNZcVRuGFMrdcluam7Xzm16PIkZyQ5b/h72VT6C6rqkiRPTHKPX3cBAABgXXxbd++qqq9KcmVV/fn0wpX0sD4YDdJeffFlK2owdMuzV3/ssdCw5Iv21aBwzA3n1sJy/yd8npZHOW0uq/nP/tYkz0lyQ1VdN6T9VCaB7Eur6vlJPp7kmcOyKzJ5oMTOTB4q8bxVHBsAAABYpu7eNfy9s6remuTErLKHNQBshBUHtLv7T5LUEotPnrF+Jzl7pcfbCCt9kiwAAABsFlV1eJL7dPdnhumnJHlp9LAGYITmt+8FAAAAzIdtSd46GTk0hyZ5Y3f/YVW9N3pYAzAyAtoAAACwhXX3zUkeOyP9k9kiPawBmB8C2jBiKx0W55bznr7GOQEAAACAg+8+G50BAAAAAABYDi20AQAAAGALWW6P7nNO2JMzp9bVo5sx0EIbAAAAAIBRENAGAAAAAGAUBLQBgHtV1QVVdWdVfWgq7SFVdWVVfXT4e+SQXlX1S1W1s6qur6onbFzOAQAAmAcC2gDAtAuTnLIo7dwkV3X38UmuGuaT5GlJjh9eZyV5zTrlEQAAgDnloZAAwL26+91Vddyi5NOS7BimL0qykOQnh/TXd3cnubqqjqiqo7r79nXKLgAAW8ByH2AIkAhow1yavlhY/ETjffG0Y5hb26aC1Hck2TZMH53k1qn1bhvSBLQBAAA4KAS0AYBl6+6uqj7Q7arqrEyGJcm2bduysLCw6rxsO2zyo9yBWumxV3Ks1RxvpcZSLqs55kqNpWyUy9oebzW2etms9Hi7d+/ekPcDACAR0AYA9u8Te4cSqaqjktw5pO9KcuzUescMaV+mu89Pcn6SbN++vXfs2LHqTL364svyihsO/FLmlmev7NjL7c2yVsdbqbGUy2qOuVJjKRvlsrbHW42tXjYrPd6FpxyetfgeBwBYCQ+FBAD25/IkZwzTZyS5bCr9uTVxUpJ7jJ8NAADAwaSFNgBwr6r67UweAPmwqrotyYuTnJfk0qp6fpKPJ3nmsPoVSU5NsjPJ55I8b90zDAAAwFwR0AYA7tXdz1pi0ckz1u0kZx/cHAEAAMAXCWgDAAAAsGrHreLZGgDLZQxtAAAAAABGQQttAAAAAGDFrexvOe/pa5wTWJoW2gAAAAAAjIKANgAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCh4KCSzbSh8OkXhABAAAAACrJ6ANrAtPSgYAAABgtQS0AQAAAIAV04iN9TQXAe3l/lOdc8KenLmKIRUAAAAAADh45iKgDQAAAMDyrOb5SQAH2302OgMAAAAAALAcWmgDm9r+WgYsNVSQcbgAAAAAth4ttAEAAAAAGAUBbQAAAAAARsGQI8CWtNKHmBiqBAAA2Co83JHNzr07K6GFNgAAAAAAoyCgDQAAAADAKBhyBAAAAAAYDUOVzDcBbYApKkUAAACAzcuQIwAAAAAAjMK6t9CuqlOSvCrJIUle293nrXceAIC1pX4HgK1F3X5wrLRH6LRzTtiTM9dgPwBjta4B7ao6JMmvJHlyktuSvLeqLu/uD69nPgDW2kouTM85YU92rH1WYN2p3wFga1G3A7CZrXcL7ROT7Ozum5Okqi5JcloSlSIAjJf6HQC2FnU7sCWttJfEanpGeObW2lvvgPbRSW6dmr8tyRPXOQ8Am8ZadDncKMup0FXcc0P9DgBby+jq9jFfVwNb20q/n9xPL626e/0OVvU9SU7p7n83zD8nyRO7+wVT65yV5Kxh9uuTfGTdMpg8LMnfrOPx1tKY856MO/9jznsi/xtpzHlP1jf/j+zuh6/TsThAG1i/j/1/6GBRLktTNrMpl6Upm9nWqlzU75vUcur2IV39vjGU0fIop+VRTsujnJbn67v7QQf7IOvdQntXkmOn5o8Z0u7V3ecnOX89M7VXVb2vu7dvxLFXa8x5T8ad/zHnPZH/jTTmvCfjzz9rakPqd5/B2ZTL0pTNbMplacpmNuUyF/Zbtyfq942ijJZHOS2Pcloe5bQ8VfW+9TjOfdbjIFPem+T4qnpUVd0vyelJLl/nPAAAa0v9DgBbi7odgE1rXVtod/eeqnpBknckOSTJBd1943rmAQBYW+p3ANha1O0AbGbrPeRIuvuKJFes93GXaUOGOlkjY857Mu78jznvifxvpDHnPRl//llDG1S/+wzOplyWpmxmUy5LUzazKZc5sIH37j5f+6eMlkc5LY9yWh7ltDzrUk7r+lBIAAAAAABYqfUeQxsAAAAAAFZkLgPaVXVsVb2rqj5cVTdW1Y8O6Q+pqiur6qPD3yM3Oq+zVNUDquo9VfXBIf8/M6Q/qqquqaqdVfWm4eEdm1JVHVJVH6iqtw3zY8r7LVV1Q1Vdt/fprSP67BxRVW+uqj+vqpuq6ltGlPevH8p87+vTVfXCseQ/SarqPw//sx+qqt8e/pdH8dmvqh8d8n1jVb1wSBtN2bO1VNUpVfWR4f/m3I3Oz2ax1PUNE4uvPZiYdW2w0XnaDGbV2Rudp41SVRdU1Z1V9aGpNNcArLl5rd8P5P6yJn5pKKPrq+oJU/s5Y1j/o1V1xlT6Pxv2v3PYttb/LA/cgXz3rGW5jOn7bYkyeklV7Zq6bz51atmLhvP9SFU9dSp95v/eUveqVXX/YX7nsPy4dTrlFVnqGtnn6Uvto5w252equ+fuleSoJE8Yph+U5C+SPCbJ/0xy7pB+bpKf2+i8LpH/SvLAYfq+Sa5JclKSS5OcPqT/WpIf2ui87uMc/kuSNyZ52zA/przfkuRhi9LG8tm5KMm/G6bvl+SIseR90XkckuSOJI8cS/6THJ3kY0kOG+YvTXLmGD77Sb45yYeSfEUmz1744ySPHkvZe22t1/D//5dJvmb4HvtgksdsdL42w2up65uNztdmeS2+9vC6t1y+7Npgo/O00a+l6uyNztcGlse3J3lCkg9NpbkG8FrT1zzX7wdyf5nk1CR/kElM4KQk1wzpD0ly8/D3yGH6yGHZe4Z1a9j2aRt9zsssl2V/96xluYzp+22JMnpJkh+bse5jhv+r+yd51PD/dsi+/veyxL1qkv+U5NeG6dOTvGmjy2I/5XRAMcA5/jwtVU6b8jM1ly20u/v27n7/MP2ZJDdlcuF6WiYX9Rn+PmNDMrgfPbF7mL3v8OokT0ry5iF90+a/qo5J8vQkrx3mKyPJ+z5s+s9OVT04kwrvdUnS3X/f3Z/KCPI+w8lJ/rK7P55x5f/QJIdV1aGZBIdvzzg++9+YSSX+ue7ek+R/Jfk3GVfZs3WcmGRnd9/c3X+f5JJMPotzbx/XN3Nv8bUHE/u4NuDL6+z/u8H52TDd/e4kdy1Kdg3AWlO/f6ml/sdOS/L6ISZwdZIjquqoJE9NcmV339Xddye5Mskpw7Kv7O6rexIpen1G8v96gN89a1kuo/l+W6KMlnJakku6+/Pd/bEkOzP5v5v5v7efOM10Gb05ycl7WyRvRiuIAc7r5+lA7yU29DM1lwHtaUMz9sdn0sp5W3ffPiy6I8m2jcrX/tSk2+x1Se7M5J/oL5N8agg2Jclt2bw3sb+Y5CeS/OMw/9CMJ+/J5MeDP6qqa6vqrCFtDJ+dRyX56yS/WZMu16+tqsMzjrwvdnqS3x6mR5H/7t6V5OeT/FUmgex7klybcXz2P5TkX1TVQ6vqKzL5xfrYjKTs2XKOTnLr1Pxm/b/ZUIuub/jyaw8mlro2mGuz6uzu/qONzdWm4xqAtTbP9fuB3F8uVU77Sr9tRvpYrUe5bIXvtxcMQ2VcMDXExYGW0b7iNPduMyy/Z1h/01tmDHDuP08z7iU23WdqrgPaVfXAJG9J8sLu/vT0suFXld6QjC1Dd3+hux+X5JhMfv34ho3N0fJU1XclubO7r93ovKzCt3X3E5I8LcnZVfXt0ws38Wfn0Ey6I72mux+f5LOZdHm51ybO+72GMZa+O8nvLF62mfM/fOmflknw4BFJDk9yyoZmapm6+6YkP5fkj5L8YZLrknxh0Tqbtuxh3uzr+mYebZFrj4Nlv9cG82hWnV1VP7Cxudq8XAPAqo31/nJDrUe5jLTsX5Pka5M8LpMfZV+xobnZRDY6BjiWz9OMctqUn6m5DWhX1X0zeYMu7u7fHZI/MXQVyPD3zo3K33IN3ULfleRbMukGceiw6JgkuzYqX/vwrUm+u6puyaTbwZOSvCrjyHuSe1vtpLvvTPLWTH5QGMNn57Ykt3X33l/Y3pzJTewY8j7taUne392fGObHkv/vTPKx7v7r7v6HJL+byf/DKD773f267v5n3f3tSe7OZDytsZQ9W8uuTHoI7LVp/282whLXN/Puy649quq3NjZLm8ZS1wbzblad/f9scJ42G9cArLW5rd8P8P5yqXLaV/oxM9LHaj3KZdTfb939iaEB5D8m+Y1MPk/JgZfRJ7P0veq92wzLHzysv2kdYAxwbj9Ps8pps36m5jKgPYzD8rokN3X3K6cWXZ7kjGH6jCSXrXfelqOqHl5VRwzThyV5ciZj27wryfcMq23K/Hf3i7r7mO4+LpNhI97Z3c/OCPKeJFV1eFU9aO90kqdkMhzDpv/sdPcdSW6tqq8fkk5O8uGMIO+LPCtfHG4kGU/+/yrJSVX1FcN30N7yH8tn/6uGv1+dyfjZb8x4yp6t5b1Jjh+ekH2/TOqSyzc4T5vCPq5v5toS1x5a22af1wbzbladfdMG52mzcQ3AWpvL+n0F95eXJ3luTZyUyZBItyd5R5KnVNWRQy+TpyR5x7Ds01V10vB99tyM+/91Pcpl1N9ve4Ong3+dyecpmZzX6VV1/6p6VJLjM3mQ4cz/vaE18VL3qtNl9D2ZXFtt2pbHK4gBzuXnaaly2rSfqd4ET9Jc71eSb8ukmf/1mXSdvy6TMWEfmuSqJB9N8sdJHrLReV0i//80yQeG/H8oyX8b0r9m+PDszGQ4hvtvdF73cx47krxtTHkf8vnB4XVjkv86pI/ls/O4JO8bPju/l8mTeUeR9yH/h2fyK92Dp9LGlP+fSfLnw//tGzJ5GvBYPvv/O5MgxweTnDy2svfaWq+hzv6LTJ4f8V83Oj+b5bXU9c1G52szvaavPbzuLZMvuzbY6DxthtesOnuj87SBZfHbmXQx/odMWvU/3zWA18F4zWP9fqD3l0kqya8MZXRDku1T+/rB4Z5iZ5LnTaVvH77L/jLJLyepjT7vZZbNsr971rJcxvT9tkQZvWEog+szCRIeNbX+fx3O9yNJnjaVPvN/L0vcqyZ5wDC/c1j+NRtdFvsppwOKAc7x52mpctqUn6m9BQwAAAAAAJvaXA45AgAAAADA+AhoAwAAAAAwCgLaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAKAtoAAAAAAIyCgDYAAAAAAKMgoA0AAAAAwCgIaAMAAAAAMAoC2gAAAAAAjIKANgAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCgLaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAKAtoAAAAAAIyCgDYAAAAAAKMgoA0AAAAAwCgIaAMAAAAAMAoC2gAAAAAAjIKANgAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCgLaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAKAtoAAAAAAIyCgDYAAAAAAKMgoA0AAAAAwCgIaAMAAAAAMAoC2gAAAAAAjIKANgAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCgLaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAKAtoAAAAAAIyCgDYAAAAAAKMgoA0AAAAAwCgIaAMAAAAAMAoC2gAAAAAAjIKANgAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCgLaAAAAAACMgoA2bLCq+qGq+kRV7a6qhx7kY91SVd95MI8BAGxOVbVQVf9uo/MBAJtJVf1BVZ2x0flYiap6SVX91kbnA9abgDZzZQjo3llVh0+l/buqWjhIx/t/quqdVfWZqrqnqn6/qh4ztfy+SV6Z5Cnd/cDu/mRVdVV9dghw76qqV1bVIQcjfytRVWdW1Z9sdD4AYLVm/dC7FvVcTfxIVX1oqNNvq6rfqaoT9rPdiVV1RVV9qqruqqr3VNXzVpMXABib5TbEGurbm6vqwwew7y8LAHf307r7opXkddG+b6mqv6+qhy1K/8Bwn3/cao8BTAhoM48OSfKjB/sgVfUtSf4oyWVJHpHkUUk+mORPq+prhtW2JXlAkhsXbf7Y7n5gkpOTfH+Sfz9j/4cepKwDAKvzqkyuNX4kyUOSfF2S30vy9FkrV9Uhw3XDO5P8rySPTvLQJD+U5GkHevDhBt91PgBb3bcn+aokX1NV/3yjMzP4WJJn7Z0Zfsz+io3LzvKJMTAmLnSZR/9fkh+rqiOmE6vquOFX00On0u7tmju02PrTqvqFoeXUzUML7DOr6tah5fd0N6X/meT13f2q7v5Md9/V3T+d5OokL6mqr0vykWHdT1XVOxdntLv/PMn/TvLNU/l7flX9VZJ3VtV9quqnq+rjw/FfX1UPnsr/c4Zln6yq/7rofC+sqp+dmt9RVbdNzR9bVb9bVX89bP/LVfWNSX4tybcMLcg/Nax7alV9eGiJvquqfmz5bwcAbE5VdW5V/eVQv324qv711LJHV9X/Gnpg/U1VvWlIPz7J2Ume1d3v7O7Pd/fnuvvi7j5vWOfCqnrN0Br7s0m+I5Prk4u6++e6+2964trufuawzZFV9bahXr57mD5mKj8LVfXyqvrTJJ/L5Ob+yVX150MefzlJrVfZAcBqLVXXTjkjkwZkVwzT09t+U1VdOfR4+kRV/VRVnZLkp5J833A/+8Fh3YWa9Ny+/3Cv/81T+3l4Vf1tVX3VMP9dVXXdsN7/qap/uihPb0jy3EV5fP2ivN2/qn6+qv5qyNuvVdVhw7IdNenZ9RPDPf7tVfWM4Z77L4bz+alFx3xAVb1puF55f1U9dupYj6iqtwzXDx+rqh+ZWvaSqnpzVf1WVX06yZn7fENgExHQZh69L8lCkpUEXZ+Y5PpMWk29McklSf55Ji2pfiDJL1fVA6vqK5L8P0l+Z8Y+Lk3y5O7+iyTfNKQd0d1PWrxiTYYn+RdJPjCV/C+TfGOSp2ZS4ZyZyY3w1yR5YJJfntr2NUmek0kL8YcmOSbLUJMhTt6W5ONJjktydJJLuvumJP8xyZ8NQ6QcMWzyuiT/obsflOSbM2lhBgBj95eZ1MMPTvIzSX6rqo4alr0sk55YR2ZSv756SD85yW3d/Z797Pv7k7w8yYOS/J8k35LkzftY/z5JfjPJI5N8dZK/zVDnT3lOkrOGfd6T5HeT/HSShw3n8q37yRMAbCZL1bUZ7rm/J8nFw+v0qrrfsOxBSf44yR9mci/86CRXdfcfJvnvSd403M8+dupY6e7PZ1J3Pmsq+ZlJ/ld331lVj09yQZL/kMn99a8nubyq7j+1/tVJvrKqvnG4rz49yeIxrs/LpPfW44a8HZ3kv00t/yeZ9OTem/4bmcQb/lkm1yX/b1U9amr90zKJPTwkkzjF71XVfWvSW+v3M+kpfnQm1ygvrKqnLtr2zUmOGMoRRkFAm3n135L8cFU9/AC3+1h3/2Z3fyHJm5Icm+SlQ+urP0ry95lUSA/J5P/r9hn7uD2TG8t9eX9V3Z1J5fPaTG5g93pJd3+2u/82ybOTvLK7b+7u3UlelElFfmgmlfvbuvvdQ8X8/yb5x2We54mZVPw/Phzr77p7X+OJ/kOSx1TVV3b33d39/mUeBwA22u8Nraw+NfQ8+tW9C7r7d7r7/3b3P3b3m5J8NJM6MpnUfY9M8ohF9eRDM7v+X+yy7v7T7v7HTG7Ul7pu2JuXT3b3W4bW3p/JJBj+LxetdmF339jdezIZquTG7n5zd/9Dkl9Mcscy8gUAm8VSdW2S/Jskn88k4P32JPfNF4f2+q4kd3T3K4btPtPd1yzzmG/MJAi91/cPacnkR+Nf7+5ruvsLw7jbn09y0qJ97G2l/eQkNyXZtXdBVdWwn/889OL+TCZB9ulj/kOSlw/19yWZxA/29vy+McmHk0wH46+dqu9fmUkw/KRMGt89vLtf2t1/3903ZxIcnz7Wn3X37w3XOn+7zDKCDSegzVzq7g9l0gL53APc9BNT03877Gtx2gOT3J1J8PiofLmjkvzNfo7zhO4+sru/trt/erjZ3evWqelHZNKKeq+PJzk0k7G5HzG9bnd/Nskn93PcvY5N8vHhhng5/m2SU5N8fOgS9i3L3A4ANtozuvuIva8k/2nvgqp67lS34k9l0gtp74/SP5HJEB7vqaobq+oHh/RPZnb9v9h0fb6v64a9efmKqvr1mgwl9ukk705yRH3pg6MXXyNMXwf0ouUAsNktVdcmk6E8Lu3uPd39d0neki8OO3JsJj2TVuJdSb6iqp5Yk4c4Pi7JW4dlj0xyzqIfwo/NpM6d9oZMAuFnZtFwI0kensmY2tdO7eMPh/S9Pjk0okuGuEO+PBbxwKn56fr+H5PcNuTpkUkesSi/P5VJvODLtoUxMeA78+zFSd6f5BXD/GeHv1+R5NPD9D9ZyY67+7NV9WdJvjeTCnHaM5NctZL97t391PT/zaSS2uurk+zJpLK7PZOhSZLc2yXroVPrfjZf+nCK6XO9NclXV9WhM4LancUJ3e9NclpV3TfJCzIZVuXY5Z4QAGw2VfXITFoxnZxJ66UvVNV1Gcah7u47Mjy0uaq+LckfV9W7M6njf6Wqtnf3+/ZxiHvr0+7+3HDd8G/z5dcNe52T5OuTPLG776iqx2UyJNn0uNjTdfTtmaqLhxZh6mYARmMfde3fJXlSkhOr6t8Oq39FJmNJPyyT+9nTZ+wymXE/u+iYX6iqSzMZduQTmfR6/syw+NZMWk6/fD/7+HhVfSyTRl/PX7T4bzIJSH9Td+/6so1XZrq+v08mw7P830xiAx/r7uP3ld01ygOsKy20mVvdvTOTYUN+ZJj/60y6Av1AVR0y/Pr7tas4xLlJzqiqH6mqB9XkYU4/m8kYmT+zyuzv9dtJ/nNVPaqqHpgvjge2J5NxsL6rqr5tGEvspfnS//nrkpxaVQ+pqn+S5IVTy96TyY3weVV1eFU9oKr2jrv5iSTHTI1Pdr+qenZVPXjo4vTpLH9oEwDYrA7P5Cbvr5Okqp6XSQvtDPPfW198KOPdw7r/2N0fzWTYkt8eHux0v6EePb2q9tUz7CeSnFlVP15VDx2O8diqumRY/qBMboA/VVUPyeSH+X15e5Jvqqp/MwxF9iNZ4Q/1ALARlqprM3lmxF9k8kPv44bX12XSMvlZmfTGPqqqXliTBzA+qKqeOOznE0mOGwK/S3ljku/LZIjPN06l/0aS/zi03q7hXvnpw5jdiz0/yZOGntL3GlpQ/0aSX6gvPmjy6EXjWh+ofzZV378wk2FQrs7kvv4zVfWTVXXYEOf45qr656s4FmwKAtrMu5dmcsO6179P8uOZdBf+pkwe0rQiw/heT81kbK/bMxkO5PFJvm242V0LF2TSnendST6WyS/VPzwc/8YkZ2dSAd+eyQXAbVPbviGTh0Pcksm4Y/c+MXro3vSvMhkP/K+G7b5vWPzOJDcmuaOq9g6d8pwktwxdoP9jJhU/AIxWd384k15cf5bJze8JSf50apV/nuSaqtqd5PIkPzqMTZlMgse/nORXknwqk27P/zqTZ2Msdbz/k0lrsyclubmq7kpyfpIrhlV+MclhmbTsujqT7sn7yv/fZNJT7LxMrmuOX5R/ANjslqprz0jyq919x/Qrya8lOWNoUf3kTO5p78jkGRjfMezzd4a/n6yqmc9+Gsbb/mwmw3b8wVT6+zKJGfxyJvfXOzMZVmTWPv5yHz21fnLY9urhHvqPMwnOr9Rlmdyv353Jvfm/6e5/GO7rvyuTgP/HMrmGeG0mD7uGUavJcHoAAAAAALC5aaENAAAAAMAoCGgDAAAAADAKAtoAMKeq6oiqenNV/XlV3VRV3zI8KPbKqvro8PfIYd2qql+qqp1VdX1VPWFqP2cM63+0qs7YuDMCAABgqxPQBoD59aokf9jd35DksUluSnJukqu6+/gkVw3zSfK0TB4qd3ySs5K8Jkmq6iFJXpzkiUlOTPLivUFwAAAAWGsC2gAwh6rqwUm+PcnrkqS7/767P5XktCQXDatdlOQZw/RpSV7fE1cnOaKqjkry1CRXdvdd3X13kiuTnLJuJwIAAMBcOXSjM7AvD3vYw/q4445b9X4++9nP5vDDD199hkZgXs51Xs4zca5b1byc61qd57XXXvs33f3wNcgSX/SoJH+d5Der6rFJrk3yo0m2dfftwzp3JNk2TB+d5Nap7W8b0pZKX5L6/eBSLktTNrMpl6Upm9nU78yifj+4lMtsymVpymY25bK0tSib9arbN3VA+7jjjsv73ve+Ve9nYWEhO3bsWH2GRmBeznVezjNxrlvVvJzrWp1nVX189blhkUOTPCHJD3f3NVX1qnxxeJEkSXd3VfVaHKyqzspkqJJs27YtP//zP7/qfe7evTsPfOADV72frUa5LE3ZzKZclqZsZlurcvmO7/gO9fsW4v794FIusymXpSmb2ZTL0taibNbr3n1TB7QBgIPmtiS3dfc1w/ybMwlof6Kqjuru24chRe4clu9KcuzU9scMabuS7FiUvrD4YN19fpLzk2T79u29FheRLkZnUy5LUzazKZelKZvZlAsAsJGMoQ0Ac6i770hya1V9/ZB0cpIPJ7k8yRlD2hlJLhumL0/y3Jo4Kck9w9Ak70jylKo6cngY5FOGNAAAAFhzWmgDwPz64SQXV9X9ktyc5HmZ/Nh9aVU9P8nHkzxzWPeKJKcm2Znkc8O66e67quplSd47rPfS7r5r/U4BAACAeSKgDQBzqruvS7J9xqKTZ6zbSc5eYj8XJLlgTTMHAAAAMxhyBAAAAACAURDQBgAAAABgFAS0AQAAAAAYBQFtAAAAAABGQUAbAAAAAIBRENAGAAAAAGAUBLQBAAAAABiFQzc6A+vhhl335Mxz337A291y3tMPQm4AAAA23nEruEdKkgtPOXyNcwIArJV5qN+10AYAAAAAYBQEtAEAAAAAGAUBbQAAAAAARkFAGwAAAACAURDQBgAAAABgFJYd0K6qQ6rqA1X1tmH+UVV1TVXtrKo3VdX9hvT7D/M7h+XHTe3jRUP6R6rqqWt+NgAAAAAAbFkH0kL7R5PcNDX/c0l+obsfneTuJM8f0p+f5O4h/ReG9VJVj0lyepJvSnJKkl+tqkNWl30AAAAAAObFsgLaVXVMkqcnee0wX0melOTNwyoXJXnGMH3aMJ9h+cnD+qcluaS7P9/dH0uyM8mJa3AOAAAAQPSuBmDrO3SZ6/1ikp9I8qBh/qFJPtXde4b525IcPUwfneTWJOnuPVV1z7D+0Umuntrn9Db3qqqzkpyVJNu2bcvCwsIys7i0bYcl55ywZ/8rLrIWx15vu3fvHmW+D9S8nGfiXLeqeTnXeTlPAIBNZG/v6q8c5vf2rr6kqn4tk17Vr8lU7+qqOn1Y7/sW9a5+RJI/rqqv6+4vrPeJAMAs+w1oV9V3Jbmzu6+tqh0HO0PdfX6S85Nk+/btvWPH6g/56osvyytuWG7s/otuefbqj73eFhYWshZlttnNy3kmznWrmpdznZfzBADYDKZ6V788yX+Z6l39/cMqFyV5SSYB7dOG6WTSu/qXF/euTvKxqtrbu/rP1uk0AGCflhPl/dYk311VpyZ5QCa/8r4qyRFVdejQSvuYJLuG9XclOTbJbVV1aJIHJ/nkVPpe09sAAAAAq/OLWafe1QCwUfYb0O7uFyV5UZIMLbR/rLufXVW/k+R7klyS5Iwklw2bXD7M/9mw/J3d3VV1eZI3VtUrM+m2dHyS96zp2QAAAMAcWu/e1cMx13zIUEPWzaZcZlMuS1M2s81Duaxk2OVkXGVz4ONwfNFPJrmkqn42yQeSvG5If12SNwzdku7KZOytdPeNVXVpkg8n2ZPkbGNwAQAAwJpY997VB2PIUEPWzaZcZlMuS1M2s81DuZx57ttXtN2Fpxw+mrK5z4Gs3N0L3f1dw/TN3X1idz+6u793GF8r3f13w/yjh+U3T23/8u7+2u7++u7+g7U9FQAAAJhP3f2i7j6mu4/LpGHZO7v72UnelUnv6WR27+pkqnf1kH56Vd2/qh4VvasB2GRW00IbAAAA2Nz0rgZgSxHQBgAAgC2kuxeSLAzTNyc5ccY6f5fke5fY/uVJXn7wcggAKyegDQAAAGwKN+y6Z0Xjv95y3tMPQm4A2IwOaAxtAAAAAADYKALaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAKAtoAAAAAAIyCgDYAAAAAAKMgoA0AAAAAwCgIaAMAAAAAMAoC2gAAAAAAjIKANgAAAAAAo3DoRmcAluO4c9/+JfPnnLAnZy5Km+WW855+sLIEAAAAAKwzLbQBYE5V1S1VdUNVXVdV7xvSHlJVV1bVR4e/Rw7pVVW/VFU7q+r6qnrC1H7OGNb/aFWdsVHnAwAAwNYnoA0A8+07uvtx3b19mD83yVXdfXySq4b5JHlakuOH11lJXpNMAuBJXpzkiUlOTPLivUFwAAAAWGsC2gDAtNOSXDRMX5TkGVPpr++Jq5McUVVHJXlqkiu7+67uvjvJlUlOWec8AwAAMCcEtAFgfnWSP6qqa6vqrCFtW3ffPkzfkWTbMH10kluntr1tSFsqHQAAANach0ICwPz6tu7eVVVfleTKqvrz6YXd3VXVa3GgIWB+VpJs27YtCwsLq97n7t2712Q/W41yWZqymU25LG2rl805J+xZ0XZbvVwAgM1NQBsA5lR37xr+3llVb81kDOxPVNVR3X37MKTIncPqu5IcO7X5MUPariQ7FqUvzDjW+UnOT5Lt27f3jh07Fq9ywBYWFrIW+9lqlMvSlM1symVpW71szjz37Sva7sJTDt/S5QIAbG6GHAGAOVRVh1fVg/ZOJ3lKkg8luTzJGcNqZyS5bJi+PMlza+KkJPcMQ5O8I8lTqurI4WGQTxnSAAAAYM1poQ0A82lbkrdWVTK5Hnhjd/9hVb03yaVV9fwkH0/yzGH9K5KcmmRnks8leV6SdPddVfWyJO8d1ntpd9+1fqcBAADAPNlvQLuqHpDk3UnuP6z/5u5+cVVdmORfJrlnWPXM7r6uJnfGr8rkpvdzQ/r7h32dkeSnh/V/trsvWsuTAQCWp7tvTvLYGemfTHLyjPROcvYS+7ogyQVrnUcAAABYbDkttD+f5Endvbuq7pvkT6rqD4ZlP97db160/tOSHD+8npjkNUmeWFUPSfLiJNuTdJJrq+ry7r57LU4EAAAAAICtbb9jaPfE7mH2vsOr97HJaUleP2x3dZIjhodKPTXJld191xDEvjLJKavLPgAAAJBMelhX1Xuq6oNVdWNV/cyQfmFVfayqrhtejxvSq6p+qap2VtX1VfWEqX2dUVUfHV5nLHFIAFh3yxpDu6oOSXJtkkcn+ZXuvqaqfijJy6vqvyW5Ksm53f35JEcnuXVq89uGtKXSFx/rrCRnJcm2bduysLBwoOf0ZbYdlpxzwp4D3m4tjr3edu/ePcp878/i92+57+lWKIut+p7O4ly3nnk5TwCATUIPawC2vGUFtLv7C0keV1VHZPIAqW9O8qIkdyS5X5Lzk/xkkpeuNkPdff6wv2zfvr137Nix2l3m1RdfllfccODPv7zl2as/9npbWFjIWpTZZnPmuW//kvlzTtizrPd0jO/hYlv1PZ3FuW4983KeAACbwfDMixX1sE5ydVXt7WG9I0MP6ySpqr09rH/7YOUdAJbrgKK83f2pqnpXklO6++eH5M9X1W8m+bFhfleSY6c2O2ZI25VJpTidvrCCPAMAAAAz6GG9Nen5OJtyWZqymW0eymUl36HJuMpmvwHtqnp4kn8YgtmHJXlykp+rqqO6+/aqqiTPSPKhYZPLk7ygqi7JpMvSPcN670jy36vqyGG9p2TSyhsAAABYA3pYb016Ps6mXJambGabh3JZPMrBcl14yuGjKZvl1BJHJblo+JX3Pkku7e63VdU7h2B3JbkuyX8c1r8iyalJdib5XJLnJUl331VVL0vy3mG9l+7tvgQAAACsHT2sAdiq9hvQ7u7rkzx+RvqTlli/k5y9xLILklxwgHkEAAAA9kMPawDmwYH34wEAAAA2Iz2sAdjyBLQBAABgC9DDGoB5cJ+NzgAAAAAAACyHgDYAAAAAAKMgoA0AAAAAwCgIaAMAAAAAMAoC2gAAAAAAjIKANgAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCgLaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAKAtoAAAAAAIyCgDYAAAAAAKMgoA0AAAAAwCgIaAMAAAAAMAoC2gAAAAAAjIKANgAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCvsNaFfVA6rqPVX1waq6sap+Zkh/VFVdU1U7q+pNVXW/If3+w/zOYflxU/t60ZD+kap66kE7KwAAAAAAtpzltND+fJIndfdjkzwuySlVdVKSn0vyC9396CR3J3n+sP7zk9w9pP/CsF6q6jFJTk/yTUlOSfKrVXXIGp4LAAAAAABb2H4D2j2xe5i97/DqJE9K8uYh/aIkzximTxvmMyw/uapqSL+kuz/f3R9LsjPJiWtxEgAAADDv9LAGYB4sawztqjqkqq5LcmeSK5P8ZZJPdfeeYZXbkhw9TB+d5NYkGZbfk+Sh0+kztgEAAABWRw9rALa8Q5ezUnd/IcnjquqIJG9N8g0HK0NVdVaSs5Jk27ZtWVhYWPU+tx2WnHPCnv2vuMhaHHu97d69e5T53p/F799y39OtUBZb9T2dxbluPfNyngAAm0F3d5Klelh//5B+UZKXJHlNJj2pXzKkvznJLy/uYZ3kY1W1t4f1nx38swCAfVtWQHuv7v5UVb0rybckOaKqDh1aYR+TZNew2q4kxya5raoOTfLgJJ+cSt9repvpY5yf5Pwk2b59e+/YseOATmiWV198WV5xwwGdapLklmev/tjrbWFhIWtRZpvNmee+/Uvmzzlhz7Le0zG+h4tt1fd0Fue69czLeQIAbBZDS+prkzw6ya/kAHpYV9V0D+urp3arhzUAm8Z+I4JV9fAk/zAEsw9L8uRMuiG9K8n3JLkkyRlJLhs2uXyY/7Nh+Tu7u6vq8iRvrKpXJnlEkuOTvGeNzwcAAADmlh7WW5Oej7Mpl6Upm9nmoVxW8h2ajKtsltNs+agkFw2/8t4nyaXd/baq+nCSS6rqZ5N8IMnrhvVfl+QNQ5ekuzIZdyvdfWNVXZrkw0n2JDl7qGgBgA0y1O/vS7Kru7+rqh6VyY/VD82kdddzuvvvq+r+SV6f5J9l0vPq+7r7lmEfL8pkDM4vJPmR7n7H+p8JADBND+utRc/H2ZTL0pTNbPNQLotHOViuC085fDRls9+HQnb39d39+O7+p939zd390iH95u4+sbsf3d3fO4ytle7+u2H+0cPym6f29fLu/tru/vru/oODd1oAwDL9aJKbpuY9NAoARqqqHj60zM5UD+ub8sUe1snsHtbJVA/rIf30qrr/8GO3HtYAbBr7DWgDAFtTVR2T5OlJXjvMVyYPjXrzsMpFSZ4xTJ82zGdYfvLih0Z198eS7H1oFACw/o5K8q6quj7Je5Nc2d1vS/KTSf7L0JP6ofnSHtYPHdL/S5Jzk0kP6yR7e1j/YfSwBmATOfB+PADAVvGLSX4iyYOG+YfGQ6MAYLS6+/okj5+RfnNm/ODc3X+X5HuX2NfLk7x8rfMIAKsloA0Ac6iqvivJnd19bVXtWIfjrflDo8b00JL1pFyWpmxmUy5L2+plMw8PjQIAth4BbQCYT9+a5Lur6tQkD0jylUlelRE9NGoeHuiyEsplacpmNuWytK1eNvPw0CgAYOsxhjYAzKHuflF3H9Pdx2XyUMd3dvez46FRAAAAbGJaaAMA034yySVV9bNJPpAvfWjUG4aHRt2VSRA83X1jVe19aNSeeGgUAAAAB5GANgDMue5eSLIwTHtoFAAAAJuWIUcAAAAAABgFAW0AAAAAAEZBQBsAAAAAgFEQ0AYAAAAAYBQEtAEAAAAAGAUBbQAAAAAARkFAGwAAAACAURDQBgAAAABgFAS0AQAAAAAYBQFtAAAAAABGQUAbAAAAAIBRENAGAAAAAGAUBLQBAAAAABgFAW0AAAAAAEZBQBsAAAAAgFEQ0AYAAAAAYBT2G9CuqmOr6l1V9eGqurGqfnRIf0lV7aqq64bXqVPbvKiqdlbVR6rqqVPppwxpO6vq3INzSgAAAAAAbEWHLmOdPUnO6e73V9WDklxbVVcOy36hu39+euWqekyS05N8U5JHJPnjqvq6YfGvJHlyktuSvLeqLu/uD6/FiQAAAAAAsLXtt4V2d9/e3e8fpj+T5KYkR+9jk9OSXNLdn+/ujyXZmeTE4bWzu2/u7r9PcsmwLgAAALAKelcDMC8OaAztqjouyeOTXDMkvaCqrq+qC6rqyCHt6CS3Tm1225C2VDoAAACwOnt7Vz8myUlJzh56UCeT3tWPG15XJF/Wu/qUJL9aVYdU1SGZ9K5+WpLHJHnW1H4AYMMtZ8iRJElVPTDJW5K8sLs/XVWvSfKyJD38fUWSH1xthqrqrCRnJcm2bduysLCw2l1m22HJOSfsOeDt1uLY62337t2jzPf+LH7/lvueboWy2Krv6SzOdeuZl/MEANho3X17ktuH6c9U1bJ7Vyf5WFXt7V2dDL2rk6Sq9vauNlwoAJvCsgLaVXXfTILZF3f37yZJd39iavlvJHnbMLsrybFTmx8zpGUf6ffq7vOTnJ8k27dv7x07diwni/v06osvyytuWHbs/l63PHv1x15vCwsLWYsy22zOPPftXzJ/zgl7lvWejvE9XGyrvqezONetZ17OEwBgM1nUu/pbM+ld/dwk78ukFffdmQS7r57abLoX9eLe1U9c4jgapK0TDUVmUy5LUzazzUO5rOQ7NBlX2ew3IlhVleR1SW7q7ldOpR81/AKcJP86yYeG6cuTvLGqXpnJQyGPT/KeJJXk+Kp6VCaB7NOTfP9anQgAAADMu/XqXZ1okLaeNBSZTbksTdnMNg/lsrhR6HJdeMrhoymb5dQS35rkOUluqKrrhrSfymQcrcdlUinekuQ/JEl331hVl2bSHWlPkrO7+wtJUlUvSPKOJIckuaC7b1yzMwEAAIA5tp69qwFgo+w3oN3df5JJ6+rFrtjHNi9P8vIZ6VfsazsAAADgwOldDcC8OPB+PAAAAMBmo3c1AHNBQBsAAABGTu9qAObFfTY6AwAAAAAAsBwC2gAAAAAAjIKANgAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCgLaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAKAtoAAAAAAIyCgDYAAAAAAKMgoA0Ac6iqHlBV76mqD1bVjVX1M0P6o6rqmqraWVVvqqr7Den3H+Z3DsuPm9rXi4b0j1TVUzfolAAAAJgDAtoAMJ8+n+RJ3f3YJI9LckpVnZTk55L8Qnc/OsndSZ4/rP/8JHcP6b8wrJeqekyS05N8U5JTkvxqVR2ynicCAADA/BDQBoA51BO7h9n7Dq9O8qQkbx7SL0ryjGH6tGE+w/KTq6qG9Eu6+/Pd/bEkO5OcePDPAAAAgHl06EZnAADYGENL6muTPDrJryT5yySf6u49wyq3JTl6mD46ya1J0t17quqeJA8d0q+e2u30NtPHOivJWUmybdu2LCwsrDr/u3fvXpP9bDXKZWnKZjblsrStXjbnnLBn/yvNsNXLBQDY3AS0AWBOdfcXkjyuqo5I8tYk33AQj3V+kvOTZPv27b1jx45V73NhYSFrsZ+tRrksTdnMplyWttXL5sxz376i7S485fAtXS4AwOZmyBEAmHPd/akk70ryLUmOqKq9P3gfk2TXML0rybFJMix/cJJPTqfP2AYAAADWlIA2AMyhqnr40DI7VXVYkicnuSmTwPb3DKudkeSyYfryYT7D8nd2dw/pp1fV/avqUUmOT/KedTkJAAAA5o4hRwBgPh2V5KJhHO37JLm0u99WVR9OcklV/WySDyR53bD+65K8oap2JrkryelJ0t03VtWlST6cZE+Ss4ehTAAAAGDNCWgDwBzq7uuTPH5G+s1JTpyR/ndJvneJfb08ycvXOo8AAACwmCFHAAAAAAAYhf0GtKvq2Kp6V1V9uKpurKofHdIfUlVXVtVHh79HDulVVb9UVTur6vqqesLUvs4Y1v9oVZ2x1DEBAAAAAGCx5bTQ3pPknO5+TJKTkpxdVY9Jcm6Sq7r7+CRXDfNJ8rRMHgh1fJKzkrwmmQTAk7w4yRMz6cr84r1BcAAAAGB1NEgDYB7sN6Dd3bd39/uH6c8kuSnJ0UlOS3LRsNpFSZ4xTJ+W5PU9cXWSI6rqqCRPTXJld9/V3XcnuTLJKWt5MgAAADDHNEgDYMs7oDG0q+q4TB4gdU2Sbd19+7DojiTbhumjk9w6tdltQ9pS6QAAAMAqaZAGwDw4dLkrVtUDk7wlyQu7+9NVde+y7u6q6rXIUFWdlckvw9m2bVsWFhZWvc9thyXnnLDngLdbi2Ovt927d48y3/uz+P1b7nu6Fcpiq76nszjXrWdezhMAYLPRIA2ArWpZAe2qum8mweyLu/t3h+RPVNVR3X378AvunUP6riTHTm1+zJC2K8mORekLi4/V3ecnOT9Jtm/f3jt27Fi8ygF79cWX5RU3LDt2f69bnr36Y6+3hYWFrEWZbTZnnvv2L5k/54Q9y3pPx/geLrZV39NZnOvWMy/nCQCwmWiQtvVoKDKbclmaspltHsplJd+hybjKZr8RwZrUfK9LclN3v3Jq0eVJzkhy3vD3sqn0F1TVJZmMt3XPEPR+R5L/PjXu1lOSvGhtTgMAAADQIG1r0lBkNuWyNGUz2zyUy+JGoct14SmHj6ZsljOG9rcmeU6SJ1XVdcPr1EwC2U+uqo8m+c5hPkmuSHJzkp1JfiPJf0qS7r4rycuSvHd4vXRIAwAAAFZpGQ3Ski9vkPbcmjgpQ4O0JO9I8pSqOnJolPaUIQ0ANtx+f/bs7j9JUkssPnnG+p3k7CX2dUGSCw4kgwAAAMCy7G2QdkNVXTek/VQmDdAurarnJ/l4kmcOy65IcmomDdI+l+R5yaRBWlXtbZCWaJAGwCZy4P14AAAAgE1HgzQA5sFyhhwBAAAAAIANJ6ANAAAAAMAoCGgDAAAAADAKAtoAAAAAAIyCgDYAAAAAAKMgoA0AAAAAwCgIaAMAAAAAMAoC2gAAAAAAjIKANgAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCgLaAAAAAACMwqEbnQEAgJW4Ydc9OfPctx/wdrec9/SDkBsAAADWgxbaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAKAtoAAAAAAIyCgDYAAAAAAKMgoA0AAAAAwCgIaAMAAAAAMAoC2gAAAAAAjMJ+A9pVdUFV3VlVH5pKe0lV7aqq64bXqVPLXlRVO6vqI1X11Kn0U4a0nVV17tqfCgAAAAAAW9lyWmhfmOSUGem/0N2PG15XJElVPSbJ6Um+adjmV6vqkKo6JMmvJHlaksckedawLgAAALAGNEgDYB7sN6Dd3e9Octcy93dakku6+/Pd/bEkO5OcOLx2dvfN3f33SS4Z1gUAAADWxoXRIA2ALW41Y2i/oKquH34BPnJIOzrJrVPr3DakLZUOAAAArAEN0gCYB4eucLvXJHlZkh7+viLJD65FhqrqrCRnJcm2bduysLCw6n1uOyw554Q9B7zdWhx7ve3evXuU+d6fxe/fct/TrVAWW/U9ncW5bj3zcp5jVFXHJnl9km2Z1Ofnd/erquohSd6U5LgktyR5ZnffXVWV5FVJTk3yuSRndvf7h32dkeSnh13/bHdftJ7nAgDs1wuq6rlJ3pfknO6+O5NGZldPrTPd8Gxxg7QnrksuAWAZVhTQ7u5P7J2uqt9I8rZhdleSY6dWPWZIyz7SF+/7/CTnJ8n27dt7x44dK8nil3j1xZflFTcc+Kne8uzVH3u9LSwsZC3KbLM589y3f8n8OSfsWdZ7Osb3cLGt+p7O4ly3nnk5z5Hak8kN7fur6kFJrq2qK5OcmeSq7j5vGDPz3CQ/mUm34+OH1xMz+XH7iUMA/MVJtmcSGL+2qi4fbpQBgI2nQdoWoKHIbMplacpmtnkol5V8hybjKpsVBbSr6qjuvn2Y/ddJ9j5w4vIkb6yqVyZ5RCY3ve9JUkmOr6pHZRLIPj3J968m4wDAyg31+O3D9Geq6qZMWmWdlmTHsNpFSRYyCWifluT13d1Jrq6qI6rqqGHdK7v7riQZguKnJPntdTsZAGBJGqRtDRqKzKZclqZsZpuHclncKHS5Ljzl8NGUzX5riar67UxuVh9WVbdl0gprR1U9LpNfeG9J8h+SpLtvrKpLk3w4k5ZfZ3f3F4b9vCDJO5IckuSC7r5xrU8GADhwVXVckscnuSbJtqkfre/IZEiSZJXPydCCa/2MqWXFelM2symXpW31spmHFlxokAbA1rPfgHZ3P2tG8uv2sf7Lk7x8RvoVSa44oNwBAAdVVT0wyVuSvLC7Pz0ZKnuiu7uqei2OowXX+pmHVicrpWxmUy5L2+plMw8tuOaNBmkAzIOVPhQSABi5qrpvJsHsi7v7d4fkT+xtyTUMKXLnkL5Ut+Rd+eIQJXvTFw5mvgGA2TRIA2Ae3GejMwAArL+aNMV+XZKbuvuVU4suT3LGMH1Gksum0p9bEycluWfovvyOJE+pqiOr6sgkTxnSAAAAYM1poQ0A8+lbkzwnyQ1Vdd2Q9lNJzktyaVU9P8nHkzxzWHZFklOT7EzyuSTPS5LuvquqXpbkvcN6L937gEgAAABYawLaADCHuvtPMnno0ywnz1i/k5y9xL4uSHLB2uUOAAAAZjPkCAAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCgLaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAKAtoAAAAAAIyCgDYAAAAAAKMgoA0AAAAAwCgIaAMAAAAAMAoC2gAAAAAAjIKANgAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCgLaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAK+w1oV9UFVXVnVX1oKu0hVXVlVX10+HvkkF5V9UtVtbOqrq+qJ0xtc8aw/ker6oyDczoAAAAAAGxVy2mhfWGSUxalnZvkqu4+PslVw3ySPC3J8cPrrCSvSSYB8CQvTvLEJCcmefHeIDgAAACwehqkATAP9hvQ7u53J7lrUfJpSS4api9K8oyp9Nf3xNVJjqiqo5I8NcmV3X1Xd9+d5Mp8eZAcAAAAWLkLo0EaAFvcSsfQ3tbdtw/TdyTZNkwfneTWqfVuG9KWSgcAAADWgAZpAMyDQ1e7g+7uquq1yEySVNVZmfw6nG3btmVhYWHV+9x2WHLOCXsOeLu1OPZ627179yjzvT+L37/lvqdboSy26ns6i3PdeublPAEANrGD1iDN/fv6cV09m3JZmrKZbR7KZSXfocm4ymalAe1PVNVR3X378AvunUP6riTHTq13zJC2K8mORekLs3bc3ecnOT9Jtm/f3jt27Ji12gF59cWX5RU3HPip3vLs1R97vS0sLGQtymyzOfPct3/J/Dkn7FnWezrG93CxrfqezuJct555OU8AgDFY6wZp7t/Xj+vq2ZTL0pTNbPNQLotjaMt14SmHj6ZsVjrkyOVJ9j4Y4owkl02lP3d4uMRJSe4Zfgl+R5KnVNWRw9hbTxnSAAAAgIPnE0NDtBxAg7RZ6QCwKew3oF1Vv53kz5J8fVXdVlXPT3JekidX1UeTfOcwnyRXJLk5yc4kv5HkPyVJd9+V5GVJ3ju8XjqkAQAAAAePBmkAbCn77cfT3c9aYtHJM9btJGcvsZ8LklxwQLkDAAAAlmVokLYjycOq6rYkL86kAdqlQ+O0jyd55rD6FUlOzaRB2ueSPC+ZNEirqr0N0hIN0gDYZFb9UEgAAABg42mQBsA8WOkY2gAAAAAAsK4EtAEAAAAAGAUBbQAAAAAARkFAGwAAAACAURDQBgAAAABgFAS0AQAAAAAYBQFtAAAAAABGQUAbAAAAAIBRENAGgDlUVRdU1Z1V9aGptIdU1ZVV9dHh75FDelXVL1XVzqq6vqqeMLXNGcP6H62qMzbiXAAAAJgfAtoAMJ8uTHLKorRzk1zV3ccnuWqYT5KnJTl+eJ2V5DXJJACe5MVJnpjkxCQv3hsEBwAAgINBQBsA5lB3vzvJXYuST0ty0TB9UZJnTKW/vieuTnJEVR2V5KlJruzuu7r77iRX5suD5AAAALBmDt3oDAAAm8a27r59mL4jybZh+ugkt06td9uQtlT6l6mqszJp3Z1t27ZlYWFh9Zk9LDnnhD0HvN1aHHsz271795Y/x5VSNrMpl6Vt9bJZyXdosvXLBQDY3AS0AYAv091dVb2G+zs/yflJsn379t6xY8eq9/nqiy/LK2448EuZW569+mNvZgsLC1mL8t2KlM1symVpW71szjz37Sva7sJTDt/S5QIAbG6GHAEA9vrEMJRIhr93Dum7khw7td4xQ9pS6QAAAHBQCGgDAHtdnuSMYfqMJJdNpT+3Jk5Kcs8wNMk7kjylqo4cHgb5lCENAAAADgpDjgDAHKqq306yI8nDquq2JC9Ocl6SS6vq+Uk+nuSZw+pXJDk1yc4kn0vyvCTp7ruq6mVJ3jus99LuXvygSQAAAFgzAtoAMIe6+1lLLDp5xrqd5Owl9nNBkgvWMGsAAACwJEOOAAAAAAAwCgLaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAKqwpoV9UtVXVDVV1XVe8b0h5SVVdW1UeHv0cO6VVVv1RVO6vq+qp6wlqcAAAAALBv7t8B2CrWooX2d3T347p7+zB/bpKruvv4JFcN80nytCTHD6+zkrxmDY4NAAAALI/7dwBG72AMOXJakouG6YuSPGMq/fU9cXWSI6rqqINwfAAAAGD/3L8DMDqHrnL7TvJHVdVJfr27z0+yrbtvH5bfkWTbMH10kluntr1tSLt9Ki1VdVYmvwBn27ZtWVhYWGUWk22HJeecsOeAt1uLY6+33bt3jzLf+7P4/Vvue7oVymKrvqezONetZ17OEwBgBNb8/h0ANsJqA9rf1t27quqrklxZVX8+vbC7e6gsl22oVM9Pku3bt/eOHTtWmcXk1RdfllfccOCnesuzV3/s9bawsJC1KLPN5sxz3/4l8+ecsGdZ7+kY38PFtup7Ootz3Xrm5TwBAEZgze/fNUhbPxqKzKZclqZsZpuHclnJd2gyrrJZVUC7u3cNf++sqrcmOTHJJ6rqqO6+feiSdOew+q4kx05tfsyQBgAAABxEB+P+XYO09aOhyGzKZWnKZrZ5KJfFjUKX68JTDh9N2ax4DO2qOryqHrR3OslTknwoyeVJzhhWOyPJZcP05UmeOzwt+aQk90x1bQIAAAAOAvfvAGwlq2mhvS3JW6tq737e2N1/WFXvTXJpVT0/yceTPHNY/4okpybZmeRzSZ63imMDAAAAy+P+HYAtY8UB7e6+OcljZ6R/MsnJM9I7ydkrPR4AAABw4Ny/A7CVrHjIEQAAAAAAWE8C2gAAAAAAjIKANgAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCgLaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAKAtoAAAAAAIyCgDYAAAAAAKMgoA0AAAAAwCgcutEZAObDcee+fb/rnHPCnpy5aL1bznv6wcoSAAAAACOjhTYAAAAAAKMgoA0AAAAAwCgIaAMAAAAAMAoC2gAAAAAAjIKANgAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCgLaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoHLreB6yqU5K8KskhSV7b3eetdx4AODDHnfv2FW134SmHr3FO2KzU7wCwtajbAdis1rWFdlUdkuRXkjwtyWOSPKuqHrOeeQAA1pb6HQC2FnU7AJvZeg85cmKSnd19c3f/fZJLkpy2znkAANaW+h0AthZ1OwCbVnX3+h2s6nuSnNLd/26Yf06SJ3b3C6bWOSvJWcPs1yf5yBoc+mFJ/mYN9jMG83Ku83KeiXPdqublXNfqPB/Z3Q9fg/1wEKjfNx3lsjRlM5tyWZqymU39vsUtp24f0tXv60e5zKZclqZsZlMuS1uLslmXun3dx9Den+4+P8n5a7nPqnpfd29fy31uVvNyrvNynolz3arm5Vzn5TzZP/X7+lEuS1M2symXpSmb2ZQLe6nf149ymU25LE3ZzKZcljamslnvIUd2JTl2av6YIQ0AGC/1OwBsLep2ADat9Q5ovzfJ8VX1qKq6X5LTk1y+znkAANaW+h0AthZ1OwCb1roOOdLde6rqBUnekeSQJBd0943rcOg17QK1yc3Luc7LeSbOdaual3Odl/Oca+r3TUe5LE3ZzKZclqZsZlMuW9wG1u2Jz9dSlMtsymVpymY25bK00ZTNuj4UEgAAAAAAVmq9hxwBAAAAAIAVEdAGAAAAAGAUtkxAu6ouqKo7q+pDSyyvqvqlqtpZVddX1RPWO49rZRnnuqOq7qmq64bXf1vvPK6Fqjq2qt5VVR+uqhur6kdnrLMl3tdlnutWeV8fUFXvqaoPDuf6MzPWuX9VvWl4X6+pquM2IKursszzPLOq/nrqPf13G5HXtVJVh1TVB6rqbTOWjf49ZeNU1SlV9ZHh83PujOVz+flaRrn8l6Feub6qrqqqR25EPtfb/splar1/W1VdVdvXM38baTllU1XPnLoeeeN653EjLON/6auH67QPDP9Pp25EPtfbPN1fsf7U7UtTv8+mfl+a+n029ftsW6Z+7+4t8Ury7UmekORDSyw/NckfJKkkJyW5ZqPzfBDPdUeSt210PtfgPI9K8oRh+kFJ/iLJY7bi+7rMc90q72sleeAwfd8k1yQ5adE6/ynJrw3Tpyd500bn+yCd55lJfnmj87qG5/xfkrxx1ud0K7ynXhvzyuRBVH+Z5GuS3C/JB2d8P87d52uZ5fIdSb5imP4h5fIl6z0oybuTXJ1k+0bne7OUTZLjk3wgyZHD/FdtdL43Sbmcn+SHhunHJLllo/O9TmUzN/dXXuv7UrevumzU7+r3A/3MqN/V79PnvSXq9y3TQru7353krn2sclqS1/fE1UmOqKqj1id3a2sZ57oldPft3f3+YfozSW5KcvSi1bbE+7rMc90Shvdq9zB73+G1+Om0pyW5aJh+c5KTq6rWKYtrYpnnuWVU1TFJnp7ktUusMvr3lA1zYpKd3X1zd/99kksy+TxNm8fP137Lpbvf1d2fG2avTnLMOudxIyzn85IkL0vyc0n+bj0zt8GWUzb/PsmvdPfdSdLdd65zHjfCcsqlk3zlMP3gJP93HfO3Yebp/op1p25fmvp9NvX70tTvs6nfl7BV6vctE9BehqOT3Do1f1u2aMBw8C01GergD6rqmzY6M6s1dDF7fCatXKdtufd1H+eabJH3tSZDU1yX5M4kV3b3ku9rd+9Jck+Sh65rJtfAMs4zSf7t0I3nzVV17PrmcE39YpKfSPKPSyzfEu8pG2I53/Pz+Pk60Prv+Zm0tNjq9lsuQ7fJY7v77euZsU1gOZ+Zr0vydVX1p1V1dVWdsm652zjLKZeXJPmBqrotyRVJfnh9srbpbbnrcNaNun1p6vfZ1O9LU7/Ppn5fuVHU7/MU0J4n70/yyO5+bJJXJ/m9jc3O6lTVA5O8JckLu/vTG52fg2k/57pl3tfu/kJ3Py6T1gQnVtU3b3CWDoplnOfvJzmuu/9pkivzxVYoo1JV35Xkzu6+dqPzAny5qvqBJNuT/H8bnZeNVlX3SfLKJOdsdF42qUMz6Za8I8mzkvxGVR2xkRnaJJ6V5MLuPiaTbrhvGD5LABtG/f5F6vf9Ur/Ppn4fsXl6o3YlmW79eMyQtuV096f3DnXQ3VckuW9VPWyDs7UiVXXfTAK8F3f3785YZcu8r/s71630vu7V3Z9K8q4ki38hvvd9rapDM+n+88l1zdwaWuo8u/uT3f35Yfa1Sf7ZOmdtrXxrku+uqlsy6cr1pKr6rUXrbKn3lHW1nO/5efx8Lav+q6rvTPJfk3z31PfNVra/cnlQkm9OsjB8Z52U5PI5eXDUcj4ztyW5vLv/obs/lskzPY5fp/xtlOWUy/OTXJok3f1nSR6QZNTXYGtky1yHs+7U7UtTv8+mfl+a+n029fvKjaJ+n6eA9uVJnjs8rfOkJPd09+0bnamDoar+yd7xxarqxEze59FV/sM5vC7JTd39yiVW2xLv63LOdQu9rw/f+2twVR2W5MlJ/nzRapcnOWOY/p4k7+zuUY0/vZzzXDQO1XdnMnb66HT3i7r7mO4+LpOH9ryzu39g0Wqjf0/ZMO9NcnxVPaqq7pfJZ+zyRevM4+drv+VSVY9P8uuZ3OzOw1iJyX7Kpbvv6e6Hdfdxw3fW1ZmUz/s2Jrvrajn/S7+XSeutDD+af12Sm9cxjxthOeXyV0lOTpKq+sZMbnj/el1zuTltietwNoS6fWnq99nU70tTv8+mfl+5UdTvh250BtZKVf12Jv+gDxvGv3lxJg9hS3f/Wibj4ZyaZGeSzyV53sbkdPWWca7fk+SHqmpPkr9NcvpIK/9vTfKcJDcM4xAnyU8l+epky72vyznXrfK+HpXkoqo6JJOg/KXd/baqemmS93X35ZkE999QVTszeVjB6RuX3RVbznn+SFV9d5I9mZznmRuW24NgC76nbIDu3lNVL0jyjkyeVn5Bd98475+vZZbL/5fkgUl+Z/g99K+6+7s3LNPrYJnlMpeWWTbvSPKUqvpwki8k+fHuHt2P5wdimeVyTibds/9zJg+QOnOk12AHZJ7ur1hf6valqd9nU78vTf0+m/p9aVulfq85eK8AAAAAANgC5mnIEQAAAAAARkxAGwAAAACAURDQBgAAAABgFAS0AQAAAAAYBQFtAAAAAABGQUAbAAAAAIBRENAGAAAAAGAUBLQBAAAAABgFAW0AAAAAAEZBQBsAAAAAgFEQ0AYAAAAAYBQEtAEAAAAAGAUBbQAAAAAARkFAGwAAAACAURDQBgAAAABgFAS0AQAAAAAYBQFtAAAAAABGQUAbAAAAAIBRENAGAAAAAGAUBLQBAAAAABgFAW0AAAAAAEZBQBsAAAAAgFEQ0AYAAAAAYBQEtAEAAAAAGAUBbQAAAAAARkFAGwAAAACAURDQBgAAAABgFAS0AQAAAAAYBQFtAAAAAABGQUAbAAAAAIBRENAGAAAAAGAUBLQBAAAAABgFAW0AAAAAAEZBQBsAAAAAgFEQ0AYAAAAAYBQEtAEAAAAAGAUBbQAAAAAARkFAGwAAAACAURDQBgAAAABgFAS0AQAAAAAYBQFtAAAAAABGQUAbAAAAAIBRENAGAAAAAGAUBLQBAAAAABgFAW0AAAAAAEZBQBsAAAAAgFEQ0AYAAAAAYBQEtAEAAAAAGAUBbQAAAAAARkFAGwAAAACAURDQBgAAAABgFAS0YRmq6l9U1Uc2Oh+zVNWOqrptjfZ1S1V951rsCwCYWOvriKp6SVX91lrtDwAAxkRAmy1vCNL+bVXtnnr98n626ap69N757v7f3f31Byl/F1bVz67h/r6tqv5PVd1TVXdV1Z9W1T9fq/0DwDxbyXXF4usIPyADAMDKHbrRGYB18q+6+483OhMHW1V9ZZK3JfmhJJcmuV+Sf5Hk8wf5uId2956DeQwA2ETm4roCAAA2Iy20mVtV9eiq+l9DS+a/qao3DenvHlb54NDq6vsWD+sxtKz68aq6vqo+W1Wvq6ptVfUHVfWZqvrjqjpyav3fqao7hmO9u6q+aUg/K8mzk/zEcKzfH9IfUVVvqaq/rqqPVdWPTO3rsKFV991V9eEk062vvy5Juvu3u/sL3f233f1H3X39sO3XVtU7q+qTwzlfXFVHLFE+J1bVn1XVp6rq9qr65aq639Tyrqqzq+qjST5aVb9SVa9YtI/Lq+o/H/CbAwAjU1Wvqaq3TM3/XFVdVRP3XkdU1RuSfHWS3x/q/p8Y0k8aelh9qqo+WFU7pvb1qOGa5TNVdWWSh63ryQEAwCYioM08e1mSP0pyZJJjkrw6Sbr724flj+3uB3b3m5bY/t8meXImQeR/leQPkvxUkodn8r/1I1Pr/kGS45N8VZL3J7l4ONb5w/T/HI71r6rqPkl+P8kHkxyd5OQkL6yqpw77enGSrx1eT01yxtRx/iLJF6rqoqp62nRQfVBJ/keSRyT5xiTHJnnJEuf3hST/OZOb5m8Z8vGfFq3zjCRPTPKYJBcledaQ/1TVw5J8Z5I3LrF/ANhKzklyQlWdWVX/Isnzk5zR3T29Unc/J8lfZdLK+4Hd/T+r6ugkb0/ys0kekuTHkrylqh4+bPbGJNdmUie/LF9a9wMAwFwR0GZe/N7Q4mnv698n+Yckj0zyiO7+u+7+kwPc56u7+xPdvSvJ/05yTXd/oLv/Lslbkzx+74rdfUF3f6a7P59JAPmxVfXgJfb7z5M8vLtf2t1/3903J/mNJKcPy5+Z5OXdfVd335rkl6aO8+kk35akh23+emglvW1YvrO7r+zuz3f3Xyd5ZZJ/OSsT3X1td1/d3Xu6+5Ykvz5j3f8x5ONvu/s9Se7JJPCdIb8L3f2JfRcjAIzOl11XdPfnkjwnk7r1t5L8cHcv96HNP5Dkiu6+orv/sbuvTPK+JKdW1Vdncm3w/w7197sz+eEbAADmkoA28+IZ3X3E1Os3kvxEJi2W31NVN1bVDx7gPqcDtX87Y/6BSVJVh1TVeVX1l1X16SS3DOss1V34kUkeMX2jnEnL723D8kckuXVq/Y9Pb9zdN3X3md19TJJvHtb/xSEv26rqkqraNeTlt5bKR1V9XVW9bRgq5dNJ/vuMdW9dNH9RJjflGf6+YYlzBIAxm3Vdke6+JsnNmVxfXHoA+3tkku9dVPd/W5KjMqnH7+7uz06t//EZ+wAAgLkgoM3c6u47uvvfd/cjkvyHJL9aVY8+CIf6/iSnZTL8xoOTHDek196sLFr/1iQfW3Sj/KDuPnVYfnsmQ4Xs9dVLHbi7/zzJhZkEtpNJULqTnNDdX5lJ0Llmb53XJPnzJMcP6/7UjHUX5/23kpxWVY/NZEiT31sqbwCw1VTV2Unun+T/ZvLD+VJm1f1vWFT3H97d52VS7x9ZVYdPrb9k3Q8AAFudgDZzq6q+t6qOGWbvzuTm8h+H+U8k+Zo1OtSDknw+ySeTfEUmQeVpi4/1niSfqaqfHB4AeUhVfXNV7X3446VJXlRVRw75/+Gpc/qGqjpn73lV1bFJnpXk6qm87E5yzzBe54/vJ9+fTrK7qr4hyQ/t70SHrtXvzaRl9lu6+2/3tw0AbAVV9XWZjIH9A5kMPfITVfW4JVZfXPf/VpJ/VVVPHer9BwwPkjymuz+eyfAjP1NV96uqb8vk2R0AADCXBLSZF79fVbunXm/NZDzKa6pqd5LLk/zoMF51Mhnn+qKh2+8zV3ns12fSNXhXkg/ni8HlvV6X5DHDsX6vu7+Q5LuSPC7Jx5L8TZLXZtK6O0l+ZtjfxzJ5qOX0sB6fyeQhjddU1WeHY30okwdV7d32CZmMdf32JL+7j3z/WCatyz+TyXjcSz0cc7GLkpwQw40AsHXNuq74rSQ/190f7O6PZtKz6Q1Vdf8Z2/+PJD891P0/NjwT47Rhm7/OpMX2j+eL1+rfn0n9flcmD4d+/UE9OwAA2MRq0YPXAValqr49k5v6R7YvGAAAAADWkBbawJqpqvsm+dEkrxXMBgAAAGCtCWgDa6KqvjHJp5IcleQXNzQzAAAAAGxJhhwBAAAAAGAUtNAGAAAAAGAUDt3oDOzLwx72sD7uuONWvZ/PfvazOfzww1efoXUivweX/B5c8ntwzWt+r7322r/p7oevQZbYBOa1fl8vymVpymY25bI0ZTOb+h0A2EibOqB93HHH5X3ve9+q97OwsJAdO3asPkPrRH4PLvk9uOT34JrX/FbVx1efGzaLea3f14tyWZqymU25LE3ZzKZ+BwA2kiFHAAAAAAAYBQFtAAAAAABGQUAbAAAAAIBRENAGAAAAAGAUBLQBAAAAABgFAW0AAAAAAEZBQBsAAAAAgFEQ0AYAAAAAYBQEtAEAAAAAGAUBbQAAAAAARmHFAe2qekBVvaeqPlhVN1bVzwzpF1bVx6rquuH1uCG9quqXqmpnVV1fVU9Yo3MAAAAAAGAOHLqKbT+f5Endvbuq7pvkT6rqD4ZlP97db160/tOSHD+8npjkNcNfAAAAAADYrxUHtLu7k+weZu87vHofm5yW5PXDdldX1RFVdVR3377SPMD+HHfu278s7ZwT9uTMGenTbjnv6Wt2vOVY6fEA5tkNu+7Z7/f5LL5zAQAAxmtVY2hX1SFVdV2SO5Nc2d3XDItePgwr8gtVdf8h7egkt05tftuQBgAAAAAA+7WaIUfS3V9I8riqOiLJW6vqm5O8KMkdSe6X5PwkP5nkpcvdZ1WdleSsJNm2bVsWFhZWk8Ukye7du9dkP+tFftfOOSfs+bK0bYfNTp+20vPZ335XcrzNXL6zyO/BJb8AAADAPFtVQHuv7v5UVb0rySnd/fND8uer6jeT/NgwvyvJsVObHTOkLd7X+ZkEwrN9+/besWPHqvO3sLCQtdjPepHftTOrK/o5J+zJK27Y90f/lmfvWLPjLce+jreZy3cW+T245BcAAACYZysecqSqHj60zE5VHZbkyUn+vKqOGtIqyTOSfGjY5PIkz62Jk5LcY/xsAAAAAACWazUttI9KclFVHZJJYPzS7n5bVb2zqh6epJJcl+Q/DutfkeTUJDuTfC7J81ZxbAAAAAAA5syKA9rdfX2Sx89If9IS63eSs1d6PAAAAAAA5tuKhxwBAAAAAID1JKANAAAAAMAorGYMbdbYcee+PUlyzgl7cuYwvRy3nPf0g5WlJR03lb8Dye9G5BUAAAAA2Bq00AYAAAAAYBQEtAEAAAAAGAVDjgDLdtwyhpZZaggaw80AAAAAsFpaaAMAAAAAMApaaLOultPCdxatewHWXlUdkeS1Sb45SSf5wSQfSfKmJMcluSXJM7v77qqqJK9KcmqSzyU5s7vfP+znjCQ/Pez2Z7v7ovU7CwAAAOaJFtoAML9eleQPu/sbkjw2yU1Jzk1yVXcfn+SqYT5Jnpbk+OF1VpLXJElVPSTJi5M8McmJSV5cVUeu50kAAAAwPwS0AWAOVdWDk3x7ktclSXf/fXd/KslpSfa2sL4oyTOG6dOSvL4nrk5yRFUdleSpSa7s7ru6++4kVyY5Zd1OBAAAgLkioA0A8+lRSf46yW9W1Qeq6rVVdXiSbd19+7DOHUm2DdNHJ7l1avvbhrSl0gEAAGDNGUObUVjp2NvMNqby3Orjrh/o+Z1zwp6cee7bR3N+bGqHJnlCkh/u7muq6lX54vAiSZLu7qrqtThYVZ2VyVAl2bZtWxYWFla9z22HTf4nDtRaHHsz271795Y/x5VSNrMpl6Upm9mUCwCwkeYioH3Drnty5gqCYgJGAGxhtyW5rbuvGebfnElA+xNVdVR33z4MKXLnsHxXkmOntj9mSNuVZMei9IXFB+vu85OcnyTbt2/vHTt2LF7lgL364svyihsO/FLmlmev/tib2cLCQtaifLciZTObclmasplNuQAAG2kuAtpwoMbUghlgJbr7jqq6taq+vrs/kuTkJB8eXmckOW/4e9mwyeVJXlBVl2TyAMh7hqD3O5L896kHQT4lyYvW81wAAACYHwLaMGJjCryPKa8wR344ycVVdb8kNyd5XibP17i0qp6f5ONJnjmse0WSU5PsTPK5Yd10911V9bIk7x3We2l337V+pwAAAMA8EdAGgDnV3dcl2T5j0ckz1u0kZy+xnwuSXLCmmQMAAIAZ7rPRGQAAAAAAgOXQQnsfVjpEgodJAgAAAACsPQFt2AT29ePJOSfsyZnGnwYAAAAAAW0A9m+lPVYuPOXwNc4JAAAAMM8EtIEtabkB2MUt4A0ZBAAAALB5eSgkAAAAAACjoIU2wBpY6ZAcAAAAACyfgPYWIJAG82el//eGVAEAAADGzJAjAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAKAtoAAAAAAIzCigPaVfWAqnpPVX2wqm6sqp8Z0h9VVddU1c6qelNV3W9Iv/8wv3NYftwanQMAAAAAAHPg0FVs+/kkT+ru3VV13yR/UlV/kOS/JPmF7r6kqn4tyfOTvGb4e3d3P7qqTk/yc0m+b5X535SOO/ftG50FAAAAAIAtZ8UttHti9zB73+HVSZ6U5M1D+kVJnjFMnzbMZ1h+clXVSo8PAAAAAMB8WU0L7VTVIUmuTfLoJL+S5C+TfKq79wyr3Jbk6GH66CS3Jkl376mqe5I8NMnfLNrnWUnOSpJt27ZlYWFhNVlMkmw7LDnnhD37X3GTkN+DS34PrrHnd6XfOet1zqst31dffNmKtjvnhJUdb/fu3WvyPQ4AAACQrDKg3d1fSPK4qjoiyVuTfMNqM9Td5yc5P0m2b9/eO3bsWO0u8+qLL8srbljVqa6rc07YI78HkfweXGPP7y3P3rGi/Zy5TkMNja18Lzzl8KzF9zgAAABAsoohR6Z196eSvCvJtyQ5oqr2RluOSbJrmN6V5NgkGZY/OMkn1+L4AAAAAABsfStu5ldVD0/yD939qao6LMmTM3nQ47uSfE+SS5KckWRv//bLh/k/G5a/s7t7FXkHWHMe6goAAACwea2m3/pRSS4axtG+T5JLu/ttVfXhJJdU1c8m+UCS1w3rvy7JG6pqZ5K7kpy+imMDAAAAADBnVhzQ7u7rkzx+RvrNSU6ckf53Sb53pccDAAAAAGC+rckY2gAAAAAAcLAJaAMAAAAAMAoC2gAAAAAAjIKANgAAAAAAoyCgDQAAAADAKAhoAwAAAAAwCgLaAAAAAACMgoA2AAAAAACjIKANAAAAAMAoCGgDAAAAADAKAtoAMKeq6paquqGqrquq9w1pD6mqK6vqo8PfI4f0qqpfqqqdVXV9VT1haj9nDOt/tKrO2KjzAQAAYOsT0AaA+fYd3f247t4+zJ+b5KruPj7JVcN8kjwtyfHD66wkr0kmAfAkL07yxCQnJnnx3iA4AAAArDUBbQBg2mlJLhqmL0ryjKn01/fE1UmOqKqjkjw1yZXdfVd3353kyiSnrHOeAQAAmBMC2gAwvzrJH1XVtVV11pC2rbtvH6bvSLJtmD46ya1T2942pC2VDgAAAGvu0I3OAACwYb6tu3dV1VclubKq/nx6YXd3VfVaHGgImJ+VJNu2bcvCwsKq97ntsOScE/Yc8HZrcezNbPfu3Vv+HFdK2cymXJambGZTLgDARhLQBoA51d27hr93VtVbMxkD+xNVdVR33z4MKXLnsPquJMdObX7MkLYryY5F6QszjnV+kvOTZPv27b1jx47FqxywV198WV5xw4Ffytzy7NUfezNbWFjIWpTvVqRsZlMuS1M2sykXAGAjGXIEAOZQVR1eVQ/aO53kKUk+lOTyJGcMq52R5LJh+vIkz62Jk5LcMwxN8o4kT6mqI4eHQT5lSAMAAIA1p4U2AMynbUneWlXJ5Hrgjd39h1X13iSXVtXzk3w8yTOH9a9IcmqSnUk+l+R5SdLdd1XVy5K8d1jvpd191/qdBgAAAPNEQBsA5lB335zksTPSP5nk5BnpneTsJfZ1QZIL1jqPAAAAsJghRwAAAAAAGAUBbQAAAAAARkFAGwAAAACAURDQBgAAAABgFAS0AQAAAAAYBQFtAAAAAABGQUAbAAAAAIBRENAGAAAAAGAUBLQBAAAAABiFFQe0q+rYqnpXVX24qm6sqh8d0l9SVbuq6rrhderUNi+qqp1V9ZGqeupanAAAAAAAAPPh0FVsuyfJOd39/qp6UJJrq+rKYdkvdPfPT69cVY9JcnqSb0ryiCR/XFVf191fWEUeAAAAAACYEytuod3dt3f3+4fpzyS5KcnR+9jktCSXdPfnu/tjSXYmOXGlxwcAAAAAYL6syRjaVXVckscnuWZIekFVXV9VF1TVkUPa0Ulundrstuw7AA4AAAAAAPdazZAjSZKqemCStyR5YXd/uqpek+RlSXr4+4okP3gA+zsr/3979x9saV3fB/z9KSihRAVDcocBErAhaUmZKL2DdEwyG0lwoamYxjg4RjeGdvMDOzrZToJxptoYZrQdtJUkmrUwoENEYrTsGFpDkFsnmSImSvgZwopYdovQiKI7NiZrvv3jfJcetvfuj3t+3Pvc+3rNPHOe5/v8+H6ezz3nOWc/+5zvSbYnycLCQpaWliYNMQvHJzvO2T/xceZFvLMl3tkS72wNLd59+/ZN5ToOAAAAkExY0K6qZ2VUzL6htfbRJGmtPT62/v1JPt4X9yY5fWz303rbM7TWdibZmSSLi4tty5Ytk4SYJLn6hptz1T0T1+7nZsc5+8U7Q+KdLfHO1tDivW7rCZnGdRwAAAAgmWDIkaqqJNckeaC19q6x9lPGNvvJJPf2+V1JLq2q46rqzCRnJblztf0DAAAAALC5THKb30uSvDbJPVV1V2/7tSSvrqoXZjTkyCNJfj5JWmv3VdVNSe5Psj/J5a21b03QPwAAAAAAm8iqC9qttT9OUsusuuUQ+1yZ5MrV9gkAAAAAwOa16iFHAAAAAABgnhS0AQAAAAAYBAVtAAAAAAAGQUEbAAAAAIBBUNAGAAAAAGAQFLQBAAAAABgEBW0AAAAAAAZBQRsAAAAAgEFQ0AYAAAAAYBAUtAEAAAAAGAQFbQAAAAAABkFBGwAAAACAQVDQBgAAAABgEBS0AQAAAAAYBAVtANjEquqYqvpcVX28L59ZVZ+uqt1V9eGqenZvP64v7+7rzxg7xpt7+4NV9bI1OhUAAAA2AQVtANjc3pjkgbHldyZ5d2vte5N8Jcllvf2yJF/p7e/u26Wqzk5yaZIfSLI1yW9X1TFzih0AAIBNRkEbADapqjotyT9L8p/7ciV5aZKP9E2uT/KKPn9JX05ff0Hf/pIkN7bWvtla+0KS3UnOm8sJAAAAsOkoaAPA5vUfk/xKkr/ry9+R5Kuttf19eU+SU/v8qUkeTZK+/qm+/dPty+wDAAAAU3XsWgcAAMxfVf1Ekidaa39WVVvm0N/2JNuTZGFhIUtLSxMfc+H4ZMc5+w+/4UGm0fd6tm/fvg1/jqslN8uTl5XJzfLkBQBYSwraALA5vSTJy6vq4iTfluS5Sf5TkhOr6th+F/ZpSfb27fcmOT3Jnqo6Nsnzknx5rP2A8X2e1lrbmWRnkiwuLrYtW7ZMfAJX33Bzrrrn6D/KPPKayftez5aWljKN/G5EcrM8eVmZ3CxPXgCAtWTIEQDYhFprb26tndZaOyOjH3X8ZGvtNUluT/LKvtm2JDf3+V19OX39J1trrbdfWlXHVdWZSc5KcuecTgMAAIBNxh3aAMC4X01yY1X9RpLPJbmmt1+T5INVtTvJkxkVwdNau6+qbkpyf5L9SS5vrX1r/mEDAACwGShoA8Am11pbSrLU5x9Oct4y2/x1kp9eYf8rk1w5uwgBAABgxJAjAAAAAAAMgoI2AAAAAACDoKANAAAAAMAgKGgDAAAAADAICtoAAAAAAAyCgjYAAAAAAIOw6oJ2VZ1eVbdX1f1VdV9VvbG3P7+qbq2qh/rjSb29quo9VbW7qu6uqnOndRIAAAAAAGx8k9yhvT/Jjtba2UnOT3J5VZ2d5Iokt7XWzkpyW19OkouSnNWn7UneO0HfAAAAAABsMqsuaLfWHmutfbbPfz3JA0lOTXJJkuv7ZtcneUWfvyTJB9rIHUlOrKpTVts/AAAAAACby7HTOEhVnZHkRUk+nWShtfZYX/WlJAt9/tQkj47ttqe3PTbWlqrantEd3FlYWMjS0tLE8S0cn+w4Z//Ex5kX8c6WeGdLvLM1tHj37ds3les4AAAAQDKFgnZVfXuS30/yptba16rq6XWttVZV7WiO11rbmWRnkiwuLrYtW7ZMGmKuvuHmXHXPVGr3c7HjnP3inSHxzpZ4Z2to8V639YRM4zoOAAAAkEw2hnaq6lkZFbNvaK19tDc/fmAokf74RG/fm+T0sd1P620AAAAAAHBYqy5o1+hW7GuSPNBae9fYql1JtvX5bUluHmt/XY2cn+SpsaFJAAAAAADgkCb53vpLkrw2yT1VdVdv+7Uk70hyU1VdluSLSV7V192S5OIku5N8I8nrJ+gbAAAAAIBNZtUF7dbaHyepFVZfsMz2Lcnlq+0PAAAAAIDNbaIxtAEAAAAAYF4UtAEAAAAAGAQFbQAAAAAABkFBGwAAAACAQVDQBgAAAABgEBS0AQAAAAAYBAVtAAAAAAAGQUEbAAAAAIBBUNAGAAAAAGAQFLQBAAAAABgEBW0AAAAAAAZBQRsAAAAAgEFQ0AYAAAAAYBAUtAEAAAAAGAQFbQAAAAAABkFBGwAAAACAQVDQBgAAAABgEBS0AWATqqpvq6o7q+rPq+q+qvp3vf3Mqvp0Ve2uqg9X1bN7+3F9eXdff8bYsd7c2x+sqpet0SkBAACwCShoA8Dm9M0kL22t/WCSFybZWlXnJ3lnkne31r43yVeSXNa3vyzJV3r7u/t2qaqzk1ya5AeSbE3y21V1zDxPBAAAgM1DQRsANqE2sq8vPqtPLclLk3ykt1+f5BV9/pK+nL7+gqqq3n5ja+2brbUvJNmd5LzZnwEAAACbkYI2AGxSVXVMVd2V5Ikktyb5fJKvttb29032JDm1z5+a5NEk6eufSvId4+3L7AMAAABTdexaBwAArI3W2reSvLCqTkzysST/cFZ9VdX2JNuTZGFhIUtLSxMfc+H4ZMc5+w+/4UGm0fd6tm/fvg1/jqslN8uTl5XJzfLkBQBYSwraALDJtda+WlW3J/mnSU6sqmP7XdinJdnbN9ub5PQke6rq2CTPS/LlsfYDxvcZ72Nnkp1Jsri42LZs2TJx3FffcHOuuufoP8o88prJ+17PlpaWMo38bkRyszx5WZncLE9eAIC1ZMgRANiEquo7+53Zqarjk/x4kgeS3J7klX2zbUlu7vO7+nL6+k+21lpvv7SqjquqM5OcleTOuZwEAAAAm447tAFgczolyfVVdUxG/8F9U2vt41V1f5Ibq+o3knwuyTV9+2uSfLCqdid5MsmlSdJau6+qbkpyf5L9SS7vQ5kAAADA1CloA8Am1Fq7O8mLlml/OMl5y7T/dZKfXuFYVya5ctoxAgAAwMEMOQIAAAAAwCAoaAMAAAAAMAgTFbSr6tqqeqKq7h1re1tV7a2qu/p08di6N1fV7qp6sKpeNknfAAAAAABsLpPeoX1dkq3LtL+7tfbCPt2SJFV1dkY/IPUDfZ/f7j9EBQAAAAAAhzVRQbu19qkkTx7h5pckubG19s3W2heS7M4yPzoFAAAAAADLmdUY2m+oqrv7kCQn9bZTkzw6ts2e3gYAAAAAAId17AyO+d4kb0/S+uNVSX7uSHeuqu1JtifJwsJClpaWJg5o4fhkxzn7Jz7OvIh3tsQ7W+KdraHFu2/fvqlcxwEAAACSGRS0W2uPH5ivqvcn+Xhf3Jvk9LFNT+ttB++/M8nOJFlcXGxbtmyZOKarb7g5V90zi9r9bOw4Z794Z0i8syXe2RpavNdtPSHTuI4DAAAAJDMYcqSqThlb/Mkk9/b5XUkurarjqurMJGcluXPa/QMAAAAAsDFNdJtfVX0oyZYkJ1fVniRvTbKlql6Y0ZAjjyT5+SRprd1XVTcluT/J/iSXt9a+NUn/AAAAAABsHhMVtFtrr16m+ZpDbH9lkisn6RMAAAAAgM1p6kOOAAAAAADALChoAwAAAAAwCAraAAAAAAAMgoI2AAAAAACDoKANAAAAAMAgKGgDAAAAADAICtoAAAAAAAyCgjYAAAAAAIOgoA0AAAAAwCAoaAMAAAAAMAgK2gAAAAAADIKCNgAAAAAAg6CgDQAAAADAIChoAwAAAAAwCAraAAAAAAAMgoI2AAAAAACDoKANAAAAAMAgKGgDAAAAADAICtoAAAAAAAyCgjYAAAAAAIOgoA0AAAAAwCAoaAPAJlRVp1fV7VV1f1XdV1Vv7O3Pr6pbq+qh/nhSb6+qek9V7a6qu6vq3LFjbevbP1RV29bqnAAAANj4FLQBYHPan2RHa+3sJOcnubyqzk5yRZLbWmtnJbmtLyfJRUnO6tP2JO9NRgXwJG9N8uIk5yV564EiOAAAAEybgjYAbEKttcdaa5/t819P8kCSU5NckuT6vtn1SV7R5y9J8oE2ckeSE6vqlCQvS3Jra+3J1tpXktyaZOv8zgQAAIDNREEbADa5qjojyYuSfDrJQmvtsb7qS0kW+vypSR4d221Pb1upHQAAAKbu2LUOAABYO1X17Ul+P8mbWmtfq6qn17XWWlW1KfWzPaOhSrKwsJClpaWJj7lwfLLjnP1Hvd80+l7P9u3bt+HPcbXkZnnysjK5WZ68AABrSUEbADapqnpWRsXsG1prH+3Nj1fVKa21x/qQIk/09r1JTh/b/bTetjfJloPalw7uq7W2M8nOJFlcXGxbtmw5eJOjdvUNN+eqe47+o8wjr5m87/VsaWkp08jvRiQ3y5OXlcnN8uQFAFhLhhwBgE2oRrdiX5Pkgdbau8ZW7Uqyrc9vS3LzWPvrauT8JE/1oUk+keTCqjqp/xjkhb0NAAAApm6ignZVXVtVT1TVvWNtz6+qW6vqof54Um+vqnpPVe2uqrur6txJgwcAVu0lSV6b5KVVdVefLk7yjiQ/XlUPJfmxvpwktyR5OMnuJO9P8ktJ0lp7Msnbk3ymT7/e2wAAAGDqJh1y5Lokv5nkA2NtVyS5rbX2jqq6oi//apKLkpzVpxcneW9/BADmrLX2x0lqhdUXLLN9S3L5Cse6Nsm104sOAAAAljfRHdqttU8lOfgurEuSXN/nr0/yirH2D7SRO5Kc2MfmBAAAAACAw5rFGNoLfUzNJPlSkoU+f2qSR8e229PbAAAAAADgsCYdcuSQWmutqtrR7FNV25NsT5KFhYUsLS1NHMfC8cmOc/ZPfJx5Ee9siXe2xDtbQ4t33759U7mOAwAAACSzKWg/XlWntNYe60OKPNHb9yY5fWy703rbM7TWdibZmSSLi4tty5YtEwd09Q0356p7Zlq7n6od5+wX7wyJd7bEO1tDi/e6rSdkGtdxAAAAgGQ2Q47sSrKtz29LcvNY++tq5PwkT40NTQIAAAAAAIc00W1+VfWhJFuSnFxVe5K8Nck7ktxUVZcl+WKSV/XNb0lycZLdSb6R5PWT9A0AAAAAwOYyUUG7tfbqFVZdsMy2Lcnlk/QHAAAAAMDmNYshRwAAAAAAYOoUtAEAAAAAGISJhhwBAABgmM644g9Wtd91W0+YciQAAEfOHdoAAAAAAAyCgjYAAAAAAIOgoA0AAAAAwCAoaAMAAAAAMAgK2gAAAAAADIKCNgAAAAAAg6CgDQAAAADAIChoAwAAAAAwCAraAAAAAAAMgoI2AAAAAACDoKANAAAAAMAgKGgDAAAAADAICtoAAAAAAAyCgjYAAAAAAIOgoA0AAAAAwCAoaAMAAAAAMAgK2gAAAAAADIKCNgAAAAAAg6CgDQAAAADAIChoAwAAAAAwCAraAAAAAAAMgoI2AAAAAACDoKANAJtQVV1bVU9U1b1jbc+vqlur6qH+eFJvr6p6T1Xtrqq7q+rcsX229e0fqqpta3EuAAAAbB4K2gCwOV2XZOtBbVckua21dlaS2/pyklyU5Kw+bU/y3mRUAE/y1iQvTnJekrceKIIDAADALChoA8Am1Fr7VJInD2q+JMn1ff76JK8Ya/9AG7kjyYlVdUqSlyW5tbX2ZGvtK0luzf9fJAcAAICpUdAGAA5YaK091ue/lGShz5+a5NGx7fb0tpXaAQAAYCaOndWBq+qRJF9P8q0k+1tri/2ryR9OckaSR5K8qt/RBQCsI621VlVtWserqu0ZDVeShYWFLC0tTXzMheOTHefsP+r9ptH3erZv374Nf46rJTfLk5eVbfTcrOYammz8vAAA69vMCtrdj7bW/mps+cDYnO+oqiv68q/OOAYA4Mg8XlWntNYe60OKPNHb9yY5fWy703rb3iRbDmpfWu7ArbWdSXYmyeLiYtuyZctymx2Vq2+4OVfdc/QfZR55zeR9r2dLS0uZRn43IrlZnrysbKPn5mev+INV7Xfd1hM2dF4AgPVt3kOOrDQ2JwCw9nYl2dbntyW5eaz9dTVyfpKn+tAkn0hyYVWd1H8M8sLeBgAAADMxyzu0W5I/7F9X/p1+Z9ZKY3M+bT19JXmtiHe2xDtb4p2tocXrK8nrV1V9KKO7q0+uqj1J3prkHUluqqrLknwxyav65rckuTjJ7iTfSPL6JGmtPVlVb0/ymb7dr7fWDv6hSQAAAJiaWRa0f6i1treqvivJrVX1F+MrVxqbcz19JXmt7Dhnv3hnSLyzJd7ZGlq8vpK8frXWXr3CqguW2bYluXyF41yb5NophgYAAAArmtmQI621vf3xiSQfS3Je+ticSXLQ2JwAAAAAAHBIMyloV9UJVfWcA/MZjal5b1YemxMAAAAAAA5pVt9bX0jysao60Mfvttb+W1V9JsuPzQkAAAAAAIc0k4J2a+3hJD+4TPuXs8zYnAAAAAAAcDgzG0MbAAAAAACmSUEbAAAAAIBBUNAGAAAAAGAQFLQBAAAAABgEBW0AAAAAAAZBQRsAAAAAgEFQ0AYAAAAAYBAUtAEAAAAAGAQFbQAAAAAABkFBGwAAAACAQVDQBgAAAABgEBS0AQAAAAAYBAVtAAAAAAAGQUEbAAAAAIBBUNAGAAAAAGAQFLQBAAAAABgEBW0AAAAAAAZBQRsAAAAAgEFQ0AYAAAAAYBAUtAEAAAAAGAQFbQAAAAAABkFBGwAAAACAQVDQBgAAAABgEBS0AQAAAAAYBAVtAAAAAAAGQUEbAAAAAIBBUNAGAAAAAGAQFLQBAAAAABiEuRe0q2prVT1YVbur6op59w8ATJ/3dwAAAOZhrgXtqjomyW8luSjJ2UleXVVnzzMGAGC6vL8DAAAwL/O+Q/u8JLtbaw+31v4myY1JLplzDADAdHl/BwAAYC7mXdA+NcmjY8t7ehsAMFze3wEAAJiLaq3Nr7OqVybZ2lr7l335tUle3Fp7w9g225Ns74vfn+TBKXR9cpK/msJx5kW8syXe2RLvbG3WeL+ntfadUzgOM+D9fd2Rl5XJzfLkZWVyszzv7wDAmjl2zv3tTXL62PJpve1prbWdSXZOs9Oq+tPW2uI0jzlL4p0t8c6WeGdLvKxT3t/XEXlZmdwsT15WJjfLkxcAYC3Ne8iRzyQ5q6rOrKpnJ7k0ya45xwAATJf3dwAAAOZirndot9b2V9UbknwiyTFJrm2t3TfPGACA6fL+DgAAwLzMe8iRtNZuSXLLnLud6lec50C8syXe2RLvbImXdcn7+7oiLyuTm+XJy8rkZnnyAgCsmbn+KCQAAAAAAKzWvMfQBgAAAACAVdnQBe2q2lpVD1bV7qq6Ys59n15Vt1fV/VV1X1W9sbe/rar2VtVdfbp4bJ8391gfrKqXHe48+o9vfbq3f7j/ENckMT9SVff0uP60tz2/qm6tqof640m9varqPb3vu6vq3LHjbOvbP1RV28ba/0k//u6+b00Q6/eP5fCuqvpaVb1pPeW3qq6tqieq6t6xtpnnc6U+Vhnvf6iqv+gxfayqTuztZ1TV/xnL8/tWG9ehzn0V8c78719Vx/Xl3X39GRPE++GxWB+pqrvWUX5Xuoat2+cwG9dKr9Ox9at6XQ7dEeTll/tr+O6quq2qvmct4py3w+VlbLufqqpWVYvzjG8tHUluqupVY9f+3513jGvhCF5L393fEz/XX08XL3ecjWa5zy4HrV/VZwwAgIm11jbklNGPUn0+yQuSPDvJnyc5e479n5Lk3D7/nCR/meTsJG9L8m+W2f7sHuNxSc7ssR9zqPNIclOSS/v8+5L84oQxP5Lk5IPa/n2SK/r8FUne2ecvTvJfk1SS85N8urc/P8nD/fGkPn9SX3dn37b6vhdN8W/9pSTfs57ym+RHkpyb5N555nOlPlYZ74VJju3z7xyL94zx7Q46zlHFtdK5rzLemf/9k/xSkvf1+UuTfHi18R60/qok/3Yd5Xela9i6fQ6bNuZ0qNfp2Darel0OeTrCvPxokr/f539RXp6x3XOSfCrJHUkW1zru9ZKbJGcl+dzYdfq71jrudZKXnfl/nwPOTvLIWsc9p9wc7rPLqj5jmEwmk8lkMk06beQ7tM9Lsru19nBr7W+S3Jjkknl13lp7rLX22T7/9SQPJDn1ELtckuTG1to3W2tfSLI7o3NY9jz6nYwvTfKRvv/1SV4xg1O5pB/74D4uSfKBNnJHkhOr6pQkL0tya2vtydbaV5LcmmRrX/fc1todrbWW5ANTjPeCJJ9vrX3xMOcx1/y21j6V5Mll4ph1Plfq46jjba39YWttf1+8I8lphzrGKuNa6dyPOt5DmObff/w8PpLkggN3Fq823r7/q5J86FDHmHN+V7qGrdvnMBvWkbyfr+p1OXCHzUtr7fbW2jf64mGv4RvEkX7+e3tG/1H71/MMbo0dSW7+VZLf6tfrtNaemHOMa+FI8tKSPLfPPy/J/5pjfGvmCD5rreozBgDApDZyQfvUJI+OLe/JoQvKM9O/+vyiJJ/uTW/oX8u7duyr9CvFu1L7dyT56lixcRrn15L8YVX9WVVt720LrbXH+vyXkiysMt5T+/zB7dNwaZ5ZCFyv+U3mk8+V+pjUz2V0F84BZ/av3v73qvrh3raauKb9Wp313//pffr6p/r2k/jhJI+31h4aa1s3+T3oGjbk5zDDdCTP4Vm8Lte7o31tX5ZnXsM3qsPmpQ+LcHpr7Q/mGdg6cCTPme9L8n1V9SdVdUdVbZ1bdGvnSPLytiQ/U1V7ktyS5F/PJ7R1b938ewsA2Fw2ckF7Xaiqb0/y+0ne1Fr7WpL3JvkHSV6Y5LGMhhlYL36otXZukouSXF5VPzK+st9F2dYkshXUaFzjlyf5vd60nvP7DPPI57T6qKq3JNmf5Ibe9FiS726tvSjJLyf53ap67kr7zyquZQzm73+QV+eZ/ymzbvK7zDVsJv2sZD1ed2Boqupnkiwm+Q9rHctaq6q/l+RdSXasdSzr1LEZDTuyJaP3pvdX//2MTe7VSa5rrZ2W0TAbH+zPJQAA1sBG/iC2N8npY8un9ba5qapnZVQIuqG19tEkaa093lr7Vmvt75K8P6OvOR4q3pXav5zR1/qOPah91Vpre/vjE0k+1mN7/MBXB/vjga+eHm28e/PMrzpP6+9xUZLPttYe77Gv2/x288jnSn2sSlX9bJKfSPKaXlxMH7rjy33+zzIae/L7VhnX1F6rc/r7P71PX/+8vv2q9GP8iyQfHjuPdZHf5a5hq+hnzZ/DDN6RPIen+rociCN6bVfVjyV5S5KXt9a+OafY1tLh8vKcJP84yVJVPZLRuL+7NskPQx7Jc2ZPkl2ttb/tQ3T9ZUYF7o3sSPJyWUa/rZHW2v9I8m1JTp5LdOvbmv97CwDYnDZyQfszSc6qqjP7XbyXJtk1r8772J3XJHmgtfausfbxceV+MsmBXw3fleTSqjquqs7M6B8Pd2aF8+iFxduTvLLvvy3JzRPEe0JVPefAfEY/Bnhvj2vbMn3sSvK6/uvm5yd5qg8R8IkkF1bVSX24hwuTfKKv+1pVnd9z87pJ4h3zjDtb12t+x8wjnyv1cdT6V41/JaNCyDfG2r+zqo7p8y/IKJ8PrzKulc59NfHO4+8/fh6vTPLJA4X+VfqxJH/RWnt6+I31kN+VrmGr6GdNn8NsCEfyfj7t1+UQHDYvVfWiJL+T0TV8s/zH0CHz0lp7qrV2cmvtjNbaGRmNLf7y1tqfrk24c3Ukr6X/ktHd2amqkzP6z9SH5xjjWjiSvPzPjH6zJVX1jzIqaP/vuUa5Pk3tMxwAwFFp6+CXKWc1ZfSVwL/M6O7Gt8y57x/K6Gvydye5q08XJ/lgknt6+64kp4zt85Ye64NJLjrceWT0a+x3ZvQDd7+X5LgJ4n1BRr/q/udJ7jvQT0ZjkN6W5KEkf5Tk+b29kvxWj+meJItjx/q5HtPuJK8fa1/MqMD4+SS/maQmzPEJGd2B97yxtnWT34wK7Y8l+duM7ni6bB75XKmPVca7O6OxEQ88h9/Xt/2p/jy5K8lnk/zz1cZ1qHNfRbwz//tn9I/Y3+vtdyZ5wWrj7e3XJfmFg7ZdD/ld6Rq2bp/Dpo07Lfc6TfLrGRUiV/26HPp0BHn5oySPj72Gd611zOshLwdtu3Sk18WNMB3Bc6YyGpLl/n4tv3StY14neTk7yZ9k9Dn5riQXrnXMc8rLcp+1fiH9c8tqP2OYTCaTyWQyTTodKB4AAAAAAMC6tpGHHAEAAAAAYANR0AYAAAAAYBAUtAEAAAAAGAQFbQAAAAAABkFBGwAAAACAQVDQBgAAAABgEBS0AQAAAAAYBAVtAAAAAAAG4f8CNPqaj7dZgekAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1800x1800 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checando as distribuições das variáveis numéricas\n",
    "\n",
    "colunas_numericas.hist(figsize=(25, 25), bins=25);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "320d646f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:51.329680Z",
     "start_time": "2023-06-23T22:35:51.308918Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.5151\n",
       "0    0.4849\n",
       "Name: IsActiveMember, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando quantidade de clientes ativos\n",
    "\n",
    "df_raw['IsActiveMember'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a2f8e615",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:51.354504Z",
     "start_time": "2023-06-23T22:35:51.333375Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.7055\n",
       "0    0.2945\n",
       "Name: HasCrCard, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Contabilizando clientes com e sem cartão de crédito\n",
    "\n",
    "df_raw['HasCrCard'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "90f51137",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:51.390821Z",
     "start_time": "2023-06-23T22:35:51.366949Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.5084\n",
       "2    0.4590\n",
       "3    0.0266\n",
       "4    0.0060\n",
       "Name: NumOfProducts, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantidade de clientes por serviço oferecido\n",
    "\n",
    "df_raw['NumOfProducts'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ecbdf340",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:52.051296Z",
     "start_time": "2023-06-23T22:35:51.401980Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tenure (meses com clientes ativos)\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "# Histograma\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.histplot(data=df_raw['Tenure'])\n",
    "plt.grid()\n",
    "\n",
    "# Boxplot\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.grid()\n",
    "sns.boxplot(data=df_raw['Tenure'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d8170fae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:52.081515Z",
     "start_time": "2023-06-23T22:35:52.062215Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "413"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantos clientes são recém-chegados\n",
    "\n",
    "(df_raw['Tenure'] == 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c3d2be1f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:52.129866Z",
     "start_time": "2023-06-23T22:35:52.106290Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     0.1048\n",
       "1     0.1035\n",
       "7     0.1028\n",
       "8     0.1025\n",
       "5     0.1012\n",
       "3     0.1009\n",
       "4     0.0989\n",
       "9     0.0984\n",
       "6     0.0967\n",
       "10    0.0490\n",
       "0     0.0413\n",
       "Name: Tenure, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Porcentagem de clientes por mês que permaneceu ativo\n",
    "\n",
    "df_raw['Tenure'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b96ac69c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:52.770641Z",
     "start_time": "2023-06-23T22:35:52.138639Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IzqV7pnD8M5X/PmWdA+dltxwREcmntELB3cfFXYiIiORfWqFgZrd0tt7dH8xuOSIikk/pXj66JOVxMXAV8CqgUBARKSDpXj76WuqymQ0H6uIoSERE8ifq1NkfAbrPICJSYNK9p/BjkqONIDkR3meA9XEVJSIi+ZHuPYXalMdHgV3u3hxDPSIikkdpXT4KJsZ7i+RMqSOAwz21MbMHzGyPmW1NWbfMzFrMbEvwdV3KtqVmtt3M3jazL2V+KCIi0lvpfvLajcBm4C+BG4FfmVlPU2f/ELimk/XfcffJwdczwf4nArOAC4M23zUzfV6DiEiOpXv56C7gEnffA2BmZwMvAI931cDdXzGzyjT3PxOoc/dDwA4z2w5MAf4jzfYiIpIF5u49P8nsdXf/bynLA4DXUtd10a4SeNrdJwXLy4C5JD+kpwH4hru3mtka4Jfu/nDwvLXAT939pNAxswXAAoDS0tKqurrcjoxta2ujpKQk43aJRIKKCybFUFHnmt7ZGqm/omOHODJgcE76iiqXx9ab/qIo5GPrTX/95XVZVVWVcbuo7ym9UVNTk3D36s62pRsKq4D/DjwWrLoJ+K2739lDu0o+GQqlwF6SI5n+ARjj7vMyCYVU1dXV3tDQ0GP92VRfX8+0adMybmdmrN70dvYL6sLi6RMi9VfWvoOW4sxGG0ftK6pcHltv+ouikI+tN/31l9dlOu+nJ4r6ntIbZtZlKPT0Gc3jgVJ3/3sz+wvgimDTfwCPZFqIu3+Qsu/vA08Hiy1ARcpTy4N1IiKSQz3daP5nkpd6cPcn3X2xuy8Gngq2ZcTMxqQsXg8cH5m0EZhlZoPNbBxwPskb2yIikkM93WgudffXT1zp7q/3dBPZzB4DpgGjzKwZuBuYZmaTSV4+2gn8bbC/N8xsPfAmyb+DWOju+oQ3EZEc6ykUhnez7fTuGrr77E5Wr+3m+SuAFT3UIyLSN9kAzCzjZrW1tdTU1GTcrrziXJre3ZVxu570FAoNZnabu38/daWZ/Q2QyHo1IiL9lR+LfBM9SrvF0ydk3CYdPYXC14GnzOyv+GMIVAOnkbwnICIiBaTbUAhGC33ezGqA4wN+f+LuP4u9MhERybl0P0/hJeClmGsREZE8i/p5CiIiUoAUCiIiElIoiIhISKEgIiIhhYKIiIQUCiIiElIoiIhISKEgIiKhdD+OU0Sk31m+fHm+S+h3FAoiUrDunnNVzvpa/ItHc9ZXnHT5SEREQjpTyCGdyopIX6dQyCGdyopIX3fKXj6qOHcsZpbxVyKRiNRORKQ/OGXPFJqb3i2IT0kSEcmmU/ZMQURETqZQEBGRkEJBRERCp+w9BZFMaDhx9kT5WS67+Yssf+jBGKqREykURNKQq+HEp8JQ4ig/y3NGnJlxu1PhZxmH2ELBzB4A/gzY4+6TgnVnAf8OVAI7gRvdvdWSYzbvAa4D/guY6+6vxlWbZJ9+kxYpDHGeKfwQWAOknvMtAV5095VmtiRYvhO4Fjg/+LoUuC/4Lr2Qy9N0/WGeSGGILRTc/RUzqzxh9UxgWvB4HVBPMhRmAg+6uwO/NLPhZjbG3XfHVd+pQKfpIpKpXI8+Kk15o38fKA0elwFNKc9rDtaJiEgOWfKX85h2njxTeDrlnsJ+dx+esr3V3UeY2dPASnf/ebD+ReBOd2/oZJ8LgAUApaWlVXV1dZFqSyQSVFwwKeN2RccOcWTA4IzbNb2zlYqxlRm3i6pp185I/RUNHMiRjo6c9BVV066dBftvl5dji9BfVFF/lnpddtLfO1upqqrKuB1ATU1Nwt2rO9uW61B4G5jm7rvNbAxQ7+4TzOx7wePHTnxed/uvrq72hoaTciPd2iJPc9FSPC7jdounT2D1/WszbhfV4gXzI/VXNmIYLa0HctJXVIsXzC/Yf7vFC+Yz7PL/kXG7ZTd/kWUPvZBxuwO/eDTSzzKqqD9LvS476W/6BKK+f5tZl6GQ6yGpG4E5wMrg+4aU9bebWR3JG8wHdD9BulPIY91zdS8IkveDNHJMUsU5JPUxkjeVR5lZM3A3yTBYb2bzgV3AjcHTnyE5HHU7ySGpt8ZVlxSGXL9xFjKNHJNUcY4+mt3FppNegcGoo4Vx1SIiIunR3EciIhJSKIiISEihICIiIYWCiIiEFAoiIhJSKIiISEihICIiIYWCiIiEFAoiIhJSKIiISEihICIiIYWCiIiEFAoiIhJSKIiISEihICIiIYWCiIiEFAoiIhJSKIiISEihICIiIYWCiIiEFAoiIhJSKIiISEihICIiIYWCiIiEFAoiIhIalI9OzWwncBDoAI66e7WZnQX8O1AJ7ARudPfWfNQnInKqyksoBGrcfW/K8hLgRXdfaWZLguU781OaiEjmli9fnnGbZTd/keUPPRhDNdHkMxRONBOYFjxeB9SjUBCRfuTuOVdl3OacEWdGarf4F49m3CYd5u6x7LjbTs12AK2AA99z9/vNbL+7Dw+2G9B6fPmEtguABQClpaVVdXV1kWpIJBJUXDAp43ZFxw5xZMDgjNs1vbOVirGVGbeLqmnXzkj9FQ0cyJGOjpz0FVUuj603/UVRyMfWm/70uuy8v6qqqozbAdTU1CTcvbqzbfkKhTJ3bzGz0cDzwNeAjakhYGat7j6iu/1UV1d7Q0ND1BpYventjNuVte+gpXhcxu0WT5/A6vvXZtwuqsUL5kfqr2zEMFpaD+Skr6hyeWy96S+KQj623vSn12Xn/UV9/zazLkMhL5eP3L0l+L7HzJ4CpgAfmNkYd99tZmOAPXHXUQjX/0REsinnoWBmQ4AB7n4weDwd+D/ARmAOsDL4viHuWgrh+p+ISDbl40yhFHgqeduAQcCj7v6smf0aWG9m84FdwI15qE1E5JSW81Bw998Dn+1k/T4g81/BRUQka/QXzSIiElIoiIhISKEgIiIhhYKIiIQUCiIiElIoiIhISKEgIiIhhYKIiIQUCiIiElIoiIhISKEgIiIhhYKIiIQUCiIiElIoiIhISKEgIiIhhYKIiIQUCiIiElIoiIhISKEgIiIhhYKIiIQUCiIiElIoiIhISKEgIiIhhYKIiIQUCiIiEupzoWBm15jZ22a23cyW5LseEZFTSZ8KBTMbCPwrcC0wEZhtZhPzW5WIyKmjT4UCMAXY7u6/d/fDQB0wM881iYicMszd811DyMxuAK5x978Jlm8GLnX321OeswBYECxOAN7OcZmjgL057jOXCvn4dGz9VyEfXz6Obay7n93ZhkE5LqTX3P1+4P589W9mDe5ena/+41bIx6dj678K+fj62rH1tctHLUBFynJ5sE5ERHKgr4XCr4HzzWycmZ0GzAI25rkmEZFTRp+6fOTuR83sduA5YCDwgLu/keeyTpS3S1c5UsjHp2Prvwr5+PrUsfWpG80iIpJffe3ykYiI5JFCQUREQgqFDBTqFBxmVmFmL5nZm2b2hpktyndN2WZmA83sN2b2dL5ryTYzG25mj5vZW2bWaGaX5bumbDGz/xW8Jrea2WNmVpzvmnrDzB4wsz1mtjVl3Vlm9ryZbQu+j8hnjQqFNBX4FBxHgW+4+0Tgc8DCAjq24xYBjfkuIib3AM+6+58An6VAjtPMyoC/A6rdfRLJwSez8ltVr/0QuOaEdUuAF939fODFYDlvFArpK9gpONx9t7u/Gjw+SPJNpSy/VWWPmZUDfwr8IN+1ZJuZDQO+AKwFcPfD7r4/r0Vl1yDgdDMbBJwBvJfnenrF3V8B/vOE1TOBdcHjdcCXc1nTiRQK6SsDmlKWmymgN87jzKwSuAj4VZ5LyaZ/Bv43cCzPdcRhHPAH4P8Fl8d+YGZD8l1UNrh7C1ALvAvsBg64+6b8VhWLUnffHTx+HyjNZzEKBQmZWQnwBPB1d/8w3/Vkg5n9GbDH3RP5riUmg4CLgfvc/SLgI/J8+SFbgmvrM0kG3znAEDP76/xWFS9P/o1AXv9OQKGQvoKegsPMikgGwiPu/mS+68miy4EZZraT5CW/K83s4fyWlFXNQLO7Hz+ze5xkSBSCLwI73P0P7n4EeBL4fJ5risMHZjYGIPi+J5/FKBTSV7BTcJiZkbwm3ejuq/NdTza5+1J3L3f3SpL/Zj9z94L5bdPd3weazGxCsOoq4M08lpRN7wKfM7MzgtfoVRTITfQTbATmBI/nABvyWEvfmuaiL+snU3BEdTlwM/C6mW0J1n3T3Z/JX0mSga8BjwS/rPweuDXP9WSFu//KzB4HXiU5Qu439LEpITJlZo8B04BRZtYM3A2sBNab2XxgF3Bj/irUNBciIpJCl49ERCSkUBARkZBCQUREQgoFEREJKRRERCSkIakinTCzkSQnJwP4FNBBcjoJgCnB/FciBUdDUkV6YGbLgDZ3r41p/wPdvSOOfYtkSpePRNJkZlVm9rKZJczsuZSpCerN7J/MbLOZvWNmU4P1c81sTUr7p81sWvC4zcz+r5m9BlxmZn8dtN9iZt8LpmoXyTmFgkh6DLgXuMHdq4AHgBUp2we5+xTg6yT/SrUnQ4BfuftngX3ATcDl7j6Z5KWqv8pe6SLp0z0FkfQMBiYBzyen4WEgyemcjzs+iWACqExjfx0kJyCE5Jw+VcCvg32fTp4nRZNTl0JBJD0GvOHuXX3U5aHgewd//H91lE+ejad+lGR7yn0EA9a5+9JsFSsSlS4fiaTnEHD28c8/NrMiM7uwhzY7gclmNsDMKkh+el9nXgRuMLPRwb7PMrOxWapbJCM6UxBJzzHgBuBfgo/AHETyE926myn3F8AOklNZN5Kc7fMk7v6mmX0L2GRmA4AjwEKSM2aK5JSGpIqISEiXj0REJKRQEBGRkEJBRERCCgUREQkpFEREJKRQEBGRkEJBRERC/x8pLzcASztV2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Meses ativos e churn\n",
    "\n",
    "sns.histplot(data=X_train.join(y_train)[X_train['IsActiveMember'] == 0], x='Tenure', hue='Exited', discrete=True)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eb603ae",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Não há diferença."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9b72f45b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:53.749910Z",
     "start_time": "2023-06-23T22:35:52.780636Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlação linear de Pearson\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "cols = colunas_numericas[['Age', 'CreditScore', 'Tenure',\n",
    "                          'Balance', 'EstimatedSalary', 'NumOfProducts']].join(y_train)\n",
    "\n",
    "sns.heatmap(cols.corr(method='pearson'), annot=True)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "109cfa4d",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Existe uma certa correlação linear entre 'Age' e 'Exited'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22f84c9c",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 5.2 Dados categóricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "767c083a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:53.787588Z",
     "start_time": "2023-06-23T22:35:53.766061Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Surname      2642\n",
       "Geography       3\n",
       "Gender          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Valores únicos dos dados categóricos\n",
    "\n",
    "colunas_categoricas.apply(lambda x: x.unique().shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b59b0283",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:54.130041Z",
     "start_time": "2023-06-23T22:35:53.800975Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Verificando a quantidade de clientes em cada país\n",
    "\n",
    "sns.countplot(data=X_train.join(y_train), x='Geography')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "96cadb1f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:54.164504Z",
     "start_time": "2023-06-23T22:35:54.140222Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "France     0.5014\n",
       "Germany    0.2509\n",
       "Spain      0.2477\n",
       "Name: Geography, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checando a quantidade de clientes por país cadastrado\n",
    "\n",
    "df_raw['Geography'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3e2f9075",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:54.375669Z",
     "start_time": "2023-06-23T22:35:54.174725Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quantidade de pessoas por gênero\n",
    "\n",
    "sns.countplot(data=X_train.join(y_train), x='Gender')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9085e05c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:54.403626Z",
     "start_time": "2023-06-23T22:35:54.384986Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      0.5457\n",
       "Female    0.4543\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Contabilizando clientes por gênero\n",
    "\n",
    "df_raw['Gender'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f920f511",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:54.879654Z",
     "start_time": "2023-06-23T22:35:54.416229Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Consumo dos tipos de serviço oferecidos por gênero\n",
    "\n",
    "X_train.join(y_train).groupby(['Gender', 'NumOfProducts']).size().unstack().plot(kind='bar')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90989d3",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Não há diferença de compra dos diferentes serviços entre os gêneros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e985f9f8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:55.325961Z",
     "start_time": "2023-06-23T22:35:54.883349Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Clientes ativos e serviços oferecidos\n",
    "\n",
    "X_train.join(y_train).groupby(['NumOfProducts', 'IsActiveMember']).size().unstack().plot(kind='bar')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b326f6e",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Até a compra de 2 produtos, os membros ativos compram um pouco mais que os clientes não ativos, porém quando chega a 3 produtos, os clientes ativos compram menos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "16c34e1e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:55.755964Z",
     "start_time": "2023-06-23T22:35:55.329741Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Churn por gênero\n",
    "\n",
    "X_train.join(y_train).groupby(['Gender', 'Exited']).size().unstack().plot(kind='bar')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ed595fa2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:55.787997Z",
     "start_time": "2023-06-23T22:35:55.759634Z"
    },
    "hidden": true
   },
   "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>Exited</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2753</td>\n",
       "      <td>924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3697</td>\n",
       "      <td>726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Exited     0    1\n",
       "Gender           \n",
       "Female  2753  924\n",
       "Male    3697  726"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_gender = X_train.join(y_train).groupby(['Gender', 'Exited']).size().unstack()\n",
    "churn_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f72ee973",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:55.814588Z",
     "start_time": "2023-06-23T22:35:55.794054Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Female    0.251292\n",
       "Male      0.164142\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Proporção de gênero por churn\n",
    "\n",
    "churn_gender.iloc[:, 1] / churn_gender.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5317ae1f",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Mulheres dão mais churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "27db9ef1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:56.178252Z",
     "start_time": "2023-06-23T22:35:55.818227Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Uso de cartão de crédito por país\n",
    "\n",
    "X_train.join(y_train).groupby(['Geography', 'HasCrCard']).size().unstack().plot(kind='bar')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "576fe2a3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:56.202061Z",
     "start_time": "2023-06-23T22:35:56.182442Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Geography\n",
       "France     0.706377\n",
       "Germany    0.719961\n",
       "Spain      0.700248\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Proporção de clientes dos diferentes países com o cartão de crédito\n",
    "\n",
    "h5 = X_train.join(y_train).groupby(['Geography', 'HasCrCard']).size().unstack()\n",
    "h5.iloc[:, 1] / h5.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44699ac8",
   "metadata": {
    "hidden": true
   },
   "source": [
    "A proporção é praticamente a mesma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4ee8fb13",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:57.312941Z",
     "start_time": "2023-06-23T22:35:56.205432Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CreditScore dos clientes dos diferentes países\n",
    "\n",
    "sns.histplot(X_train.join(y_train), x='CreditScore', hue='Geography')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cef7c8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:57.369752Z",
     "start_time": "2023-06-23T22:35:57.316917Z"
    },
    "hidden": true
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Geography</th>\n",
       "      <th colspan=\"8\" halign=\"left\">CreditScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\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>0</th>\n",
       "      <td>France</td>\n",
       "      <td>4046.0</td>\n",
       "      <td>650.727138</td>\n",
       "      <td>97.023465</td>\n",
       "      <td>350.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>654.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2039.0</td>\n",
       "      <td>651.898970</td>\n",
       "      <td>97.907702</td>\n",
       "      <td>350.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>723.0</td>\n",
       "      <td>850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>652.482878</td>\n",
       "      <td>94.167035</td>\n",
       "      <td>358.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>653.0</td>\n",
       "      <td>717.0</td>\n",
       "      <td>850.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Geography CreditScore                                                     \\\n",
       "                  count        mean        std    min    25%    50%    75%   \n",
       "0    France      4046.0  650.727138  97.023465  350.0  583.0  654.0  718.0   \n",
       "1   Germany      2039.0  651.898970  97.907702  350.0  585.0  651.0  723.0   \n",
       "2     Spain      2015.0  652.482878  94.167035  358.0  588.0  653.0  717.0   \n",
       "\n",
       "          \n",
       "     max  \n",
       "0  850.0  \n",
       "1  850.0  \n",
       "2  850.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[['CreditScore', 'Geography']].groupby('Geography').describe().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3938e2",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Não foi vista nenhuma diferença entre as distribuições."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cc9f7760",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:57.731887Z",
     "start_time": "2023-06-23T22:35:57.374738Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Churn em cada país\n",
    "\n",
    "X_train.join(y_train).groupby(['Geography', 'Exited']).size().unstack().plot(kind='bar')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "06184591",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:57.759455Z",
     "start_time": "2023-06-23T22:35:57.735667Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Geography\n",
       "France     0.162383\n",
       "Germany    0.326140\n",
       "Spain      0.162779\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Proporção do churn por país\n",
    "\n",
    "h7 = X_train.join(y_train).groupby(['Geography', 'Exited']).size().unstack()\n",
    "\n",
    "h7.iloc[:, 1] / h7.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be57fe3",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Não foi notada nenhuma diferença entre a França e Espanha, no entanto, a Alemanha apresenta maior churn."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7fa7eba",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 6. Feature Engineering / Transformação / Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfa200e2",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* Inserindo coluna de gasto por salário\n",
    "\n",
    "* BalancePerSalary = 'Balance' / 'EstimatedSalary'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d767267d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:57.781446Z",
     "start_time": "2023-06-23T22:35:57.764418Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Criando classes para tratar igualmente os dados de treino, teste e validação\n",
    "\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "\n",
    "class Feature_drop(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, cols):\n",
    "        self.cols = cols\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        return X.drop(self.cols, axis=1)\n",
    "\n",
    "\n",
    "class Add_BalancePerSalary(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        X['BalancePerSalary'] = X['Balance']/X['EstimatedSalary']\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "48375e65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:57.796885Z",
     "start_time": "2023-06-23T22:35:57.789837Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Criação do pipeline / transformação\n",
    "\n",
    "fd = Feature_drop(['RowNumber', 'CustomerId', 'Surname']) # Para dropar as colunas\n",
    "\n",
    "ohe = OneHotEncoder(drop='first', sparse_output=False) # Instanciando para o encoder\n",
    "\n",
    "ohe_ct = ColumnTransformer([('ohe_ct', ohe, ['Geography', 'Gender'])], remainder='passthrough')\n",
    "\n",
    "standard_scaler = StandardScaler() # Instanciando para ajustar a escala\n",
    "\n",
    "pipeline = Pipeline([('drop', fd), ('one_hot', ohe_ct),\n",
    "                     ('StanderScaler', standard_scaler)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b207df63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:35:57.841707Z",
     "start_time": "2023-06-23T22:35:57.801683Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Transformando os dados de treino\n",
    "\n",
    "X_train_tr = pipeline.fit_transform(X_train)\n",
    "\n",
    "\n",
    "# Transformando os dados de validação\n",
    "\n",
    "X_val_tr = pipeline.transform(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2f3623d5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:31.228864Z",
     "start_time": "2023-06-23T22:35:57.845971Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Verificando a separabilidade dos dados com o TSNE\n",
    "\n",
    "tsne = TSNE(n_components=2, random_state=42)\n",
    "\n",
    "X_reduced = tsne.fit_transform(X_train_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "370d1294",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:31.694807Z",
     "start_time": "2023-06-23T22:38:31.241062Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.scatter(X_reduced[y_train == 0, 0], X_reduced[y_train == 0, 1], alpha=0.3)\n",
    "plt.scatter(X_reduced[y_train == 1, 0], X_reduced[y_train == 1, 1], alpha=0.3)\n",
    "plt.title('t-SNE para d=2 dimensões')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebdd9fd0",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Percebe-se, acima, que os dados possuem um indicativo de baixa separabilidade, pois, em duas dimensões, é difícil delimitar as regiões da classe 0 e da classe 1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b485d0b3",
   "metadata": {},
   "source": [
    "# 7. Balanceamento das classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20664a11",
   "metadata": {},
   "source": [
    "É possível ver, abaixo, o balanceamento das classes pela remoção dos dados sobrerepresentados (undersampling) e a adição dos dados subamostrados (oversampling)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b8b344be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:31.709012Z",
     "start_time": "2023-06-23T22:38:31.698206Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.796296\n",
       "1    0.203704\n",
       "Name: Exited, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Proporção desbalanceada\n",
    "\n",
    "y_train.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d81de516",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:31.835696Z",
     "start_time": "2023-06-23T22:38:31.712018Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.74      0.81       717\n",
      "           1       0.39      0.66      0.49       183\n",
      "\n",
      "    accuracy                           0.72       900\n",
      "   macro avg       0.64      0.70      0.65       900\n",
      "weighted avg       0.79      0.72      0.74       900\n",
      "\n",
      "AUC:  0.75\n"
     ]
    }
   ],
   "source": [
    "# Verificando perfomace com dados desbalanceados\n",
    "\n",
    "clf = LogisticRegression(class_weight='balanced', random_state=42)\n",
    "clf.fit(X_train_tr, y_train)\n",
    "y_pred = clf.predict(X_val_tr)\n",
    "y_score = clf.predict_proba(X_val_tr)[:,1]\n",
    "\n",
    "print(classification_report(y_val, y_pred))\n",
    "print('AUC: ', np.round(roc_auc_score(y_val, y_score),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e76f3f9",
   "metadata": {},
   "source": [
    "### 7.1 NearMiss (Undersampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "60ea559a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:32.222038Z",
     "start_time": "2023-06-23T22:38:31.846386Z"
    }
   },
   "outputs": [],
   "source": [
    "nm = NearMiss(sampling_strategy=0.5)\n",
    "X_train_balanced, y_train_balanced = nm.fit_resample(X_train_tr, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0e83a91c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:32.239861Z",
     "start_time": "2023-06-23T22:38:32.228709Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.666667\n",
       "1    0.333333\n",
       "Name: Exited, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checando nova proporção com NearMiss\n",
    "\n",
    "y_train_balanced.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cb866d3b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:32.309180Z",
     "start_time": "2023-06-23T22:38:32.244726Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.71      0.78       717\n",
      "           1       0.35      0.61      0.44       183\n",
      "\n",
      "    accuracy                           0.69       900\n",
      "   macro avg       0.61      0.66      0.61       900\n",
      "weighted avg       0.77      0.69      0.71       900\n",
      "\n",
      "AUC:  0.74\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(class_weight='balanced', random_state=42)\n",
    "clf.fit(X_train_balanced, y_train_balanced)\n",
    "y_pred = clf.predict(X_val_tr)\n",
    "y_score = clf.predict_proba(X_val_tr)[:,1]\n",
    "\n",
    "print(classification_report(y_val, y_pred))\n",
    "print('AUC: ', np.round(roc_auc_score(y_val, y_score),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "661548cb",
   "metadata": {},
   "source": [
    "### 7.2 SMOTE (Oversampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "133dfa63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:32.539146Z",
     "start_time": "2023-06-23T22:38:32.336676Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.74      0.81       717\n",
      "           1       0.39      0.65      0.49       183\n",
      "\n",
      "    accuracy                           0.72       900\n",
      "   macro avg       0.64      0.69      0.65       900\n",
      "weighted avg       0.79      0.72      0.74       900\n",
      "\n",
      "AUC:  0.75\n"
     ]
    }
   ],
   "source": [
    "smote = SMOTE(sampling_strategy=0.5)\n",
    "X_train_balanced, y_train_balanced = smote.fit_resample(X_train_tr, y_train)\n",
    "\n",
    "clf = LogisticRegression(class_weight='balanced', random_state=42)\n",
    "clf.fit(X_train_balanced, y_train_balanced)\n",
    "y_pred = clf.predict(X_val_tr)\n",
    "y_score = clf.predict_proba(X_val_tr)[:,1]\n",
    "\n",
    "print(classification_report(y_val, y_pred))\n",
    "print('AUC: ', np.round(roc_auc_score(y_val, y_score),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8e96f27",
   "metadata": {},
   "source": [
    "Os resultados do balanceamento não demonstraram melhora, por esse motivo, seguimos com as classes desbalanceadas. A estratégia será ajustar o `class_weight` nos modelos, para atribuir maior prioridade à classe subamostrada."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8c0f4e9",
   "metadata": {},
   "source": [
    "# 8. Feature selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c2101b0",
   "metadata": {},
   "source": [
    "Foi utilizado o SelectKBest para selecionar as melhores features, utilizando ANOVA como teste estatístico. Além disso, foi feito um loop para verificar o número ideal de k features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7009b1d2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:32.575688Z",
     "start_time": "2023-06-23T22:38:32.549012Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8100, 11)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimensionalidade do X_train_tr\n",
    "\n",
    "X_train_tr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d9708e93",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:32.907687Z",
     "start_time": "2023-06-23T22:38:32.590233Z"
    }
   },
   "outputs": [],
   "source": [
    "# Lista com o número de features\n",
    "k_array = np.arange(1,12)\n",
    "\n",
    "# Lista vazia para armazenar os valores de AUC\n",
    "auc_array = np.zeros(len(k_array))\n",
    "\n",
    "for i in range(len(k_array)):\n",
    "    # Instanciando o SelectKBest\n",
    "    fs = SelectKBest(score_func=f_classif, k=k_array[i])\n",
    "    # Colunas selecionadas do treino:\n",
    "    X_train_selected = fs.fit_transform(X_train_tr, y_train)\n",
    "    # Colunas selecionadas da validação:\n",
    "    X_val_selected = fs.transform(X_val_tr)\n",
    "    \n",
    "    # Treinando o modelo para avaliar o valor de AUC\n",
    "    clf = LogisticRegression(class_weight='balanced')\n",
    "    clf.fit(X_train_selected, y_train)\n",
    "    y_score = clf.predict_proba(X_val_selected)[:,1]\n",
    "    \n",
    "    # AUC das K melhores features\n",
    "    auc_array[i] = roc_auc_score(y_val, y_score)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b21b2a15",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:33.127758Z",
     "start_time": "2023-06-23T22:38:32.910749Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iFc1sJDAaWBgsZwG3A5cDZyZ4y/1m1gI8Bnzb3T3BNq8CrgIoLi6moqLisDsShdra2rSrubNa3Vn7biuvbG1m6fZmGludfziygZdfejHq0pKmJ/w9t6c+p79UmQBYDsx395Zg+RpggbtXJTjPfZm7V5tZAbHguAL4VfuV3P1e4F6A6dOn+8yZM8OqPRQVFRWkW82Hwt1Zs3UPT7xWzW9WbGXH3gYKeudwwdRSxubs4opPnRF1iUmVqX/PHVGf01+YwVENjIhbLg3aEikHro1bPhk4zcyuAfoBvcys1t3nuHs1gLvvNbOHiZ0S+0hwSGqpfG8fTy2v5onXqtm0s47cbGPW2CFcOLWEM8YNIS83O6N+IxPJZGEGx2JgjJmNJhYY5cCn269kZuOAAcArbW3uflnc658Dprv7HDPLAYrcfZeZ5QKfBP4QYh+kC96va+S3q7bx1GvVLHn7fQBmjBrIlacexXmThlLUR3M0RNJRaMHh7s1m9iXgOSAbuM/d15jZzcASd386WLUcmJtonCKB3sBzQWhkEwuNn4VQvhym/Y0t/GHddp5aXk3F+p00tzrHFvfja+eM5YIpwykdoIcwiaS7UMc43H0BsKBd243tlm86yDYeAB4Ifq4DpnVnjdJ1La3Onzft4snXtvLcmneobWhmaP88/unU0VxYVsL4YQWakyGSQVJlcFzSzIEGuc+bNJQLp5Zw4ugjyM5SWIhkIgWHdMqhDHKLSGZTcMhBJRzkHq1BbpGeSsEhCWmQW0QORMEhH7KisoZfvfL2hwa5rzx1NLM1yC0iAQWHfKBmXyOX/PQVemVnaZBbRA5IwSEf+M2KrTQ0t/LYFz/GxJLCqMsRkRSlBznJB+YvrWL8sP4KDRHpkIJDAHhj+15WVO3m4mmlUZciIilOwSEAPLa0ipwsY3bZ8KhLEZEUp+AQmltaefy1amaNG8Kgfr2jLkdEUpyCQ3h5wy527m3QaSoROSQKDmH+0ioG9u3FrLFDoi5FRNKAgqOHq9nXyPNrtzO7bDi9cvTPQUQOTnuKHu43K7bS2NKq01QicsgUHD1c29yN44Zr7oaIHBoFRw+muRsicjgUHD2Y5m6IyOFQcPRQmrshIofrgMFhZp8ws4sTtF9sZmeFW5aETXM3RORwdXTEcSPwYoL2CuDmUKqRpNHcDRE5XB0FR29339m+0d13AX3DK0nCprkbItIVHe01+pvZR57XYWa5QH54JUnYNHdDRLqio+B4HPiZmX1wdGFm/YCfBK9JmtLcDRHpio6C4xvAduBtM1tqZsuAN4GdwWuShjR3Q0S66oCPjnX3ZmCOmf0ncEzQvNHd9yelMgmF5m6ISFcdMDjM7KJ2TQ4Umdlyd98bblkSBs3dEJHucMDgAD6VoG0gMNnMrnT3hSHVJCHR3A0R6Q4dnar6fKJ2MxsJzANODKsoCYfmbohId+j0Rfzu/jaQG0ItEqK2uRsXTNHcDRHpmk7vQcxsHNAQQi0SIs3dEJHu0tHg+G+IDYjHGwgMAy4PsyjpfvOXVjFuaAHHDe8fdSkikuY6Ghz/frtlB94jFh6XA6+EVZR0rw3B3I1vnD8eM4u6HBFJcx0Njn9wg0Mzmwp8Gvg/xCYBPhZ+adJd5i+Lzd24cGpJ1KWISAbo6FTVscClwdcu4FHA3H1WkmqTbtDc0soTy6qZOVZzN0Ske3R0qup14GXgk+6+EcDMrk9KVdJtXt64ix2auyEi3aijq6ouArYBi8zsZ2b2caBTJ8jN7BwzW29mG81sToLX7zCz5cHXG2ZW0+71/mZWZWZ3xbVNM7NVwTZ/aDpp36H5S6sY0CeXM8Zp7oaIdI8DBoe7P+nu5cA4YBHwVWCImd1jZmcfbMNmlg3cDZwLTAAuNbMJ7T7jencvc/cy4Ed89K67twAvtWu7B/gCMCb4OudgtfRUu/c18fya7cwuK9HcDRHpNgfdm7h7nbs/7O6fAkqB14D/OIRtzyB2U8TN7t4IzAVmd7D+pcAjbQtmNg0oBn4f1zYM6O/uf3F3B34FXHgItfRIT6/U3A0R6X4djXF8hLu/D9wbfB1MCVAZt1zFAW5TEtzGZDSwMFjOAm4ndtnvme22WdVumwkvFTKzq4CrAIqLi6moqDiEklNHbW1tl2u+/5X9jCjIYucby6jYkPpn9Lqjz+lGfe4ZMq3PnQqOEJUD8929JVi+Bljg7lWHO4Th7h8E3PTp033mzJndUWfSVFRU0JWaN2zfy+ZnX+Ib549n1mlHdV9hIepqn9OR+twzZFqfwwyOamBE3HJp0JZIOXBt3PLJwGlmdg3QD+hlZrXAncF2DmWbPZrmbohIWMIMjsXAGDMbTWznXk5sEuGHBPe+GkDcTHR3vyzu9c8B0919TrC8x8xOAl4FPkNsUF3iaO6GiIQptEttgicIfgl4DlgHzHP3NWZ2s5ldELdqOTA3GOw+FNcAPwc2ApuAZ7qx7IyguRsiEqZQxzjcfQGwoF3bje2WbzrINh4AHohbXgJM7K4aM5HmbohImHRxf4bR3A0RCZv2LBlGczdEJGwKjgyj526ISNgUHBlkw/a9rKis4eJppXruhoiERsGRQTR3Q0SSQcGRITR3Q0SSRcGRITR3Q0SSRcGRITR3Q0SSRcGRATR3Q0SSSXuZDKC5GyKSTAqODKC5GyKSTAqONKe5GyKSbAqONKe5GyKSbAqONKa5GyISBQVHGtPcDRGJgoIjjWnuhohEQcGRpjR3Q0Sioj1OmtLcDRGJioIjTWnuhohERcGRhjR3Q0SipOBIQ5q7ISJRUnCkGc3dEJGoKTjSjOZuiEjUFBxpRnM3RCRqCo40orkbIpIKtPdJI5q7ISKpQMGRRjR3Q0RSgYIjTWjuhoikCgVHmtDcDRFJFQqONKC5GyKSShQcaeBvczd0tCEi0VNwpIG/zd0ojroUEREFR6rT3A0RSTXaE6U4zd0QkVSj4EhxmrshIqlGwZHCNHdDRFJRqMFhZueY2Xoz22hmcxK8foeZLQ++3jCzmqB9pJktC9rXmNnVce+pCLbZ9r6Mvdvf/GVVZGcZs8t0NZWIpI6csDZsZtnA3cBZQBWw2Myedve1beu4+/Vx618HTA0WtwEnu3uDmfUDVgfv3Rq8fpm7Lwmr9lTQ0uo8sayaWWMHM7hAczdEJHWEecQxA9jo7pvdvRGYC8zuYP1LgUcA3L3R3RuC9t4h15mS1rzbouduiEhKCnOHXAJUxi1XBW0fYWYjgdHAwri2EWa2MtjGbXFHGwD3B6epvmkZevL/j9XNmrshIikptFNVnVQOzHf3lrYGd68EJpvZcOBJM5vv7tuJnaaqNrMC4DHgCuBX7TdoZlcBVwEUFxdTUVGRhG50j7omZ9n2ZmaOyOXPf3wp6nKSpra2Nq3+nrqD+twzZFqfwwyOamBE3HJp0JZIOXBtohfcfauZrQZOIxYu1UH7XjN7mNgpsY8Eh7vfC9wLMH36dJ85c+ZhdiP5bnv2dZp9E1+dfRITSwqjLidpKioqSKe/p+6gPvcMmdbnME9VLQbGmNloM+tFLByebr+SmY0DBgCvxLWVmll+8PMA4FRgvZnlmNmgoD0X+CSwOsQ+JN0L67ZzT8UmTivJ6VGhISLpI7QjDndvNrMvAc8B2cB97r7GzG4Glrh7W4iUA3Pd3ePePh643cwcMOD77r7KzPoCzwWhkQ38AfhZWH1Iti3v7uP6R5dz3PD+XDGhOepyREQSCnWMw90XAAvatd3YbvmmBO97HpicoL0OmNa9VaaG+qYWrn5wKQA/uXwam1b+NeKKREQS63GXuaaqG59azdpte/if8jJGDOwTdTkiIgek4EgBjy7ewrwlVVx3xjG6/FZEUp6CI2Krq3fzzafWcOoxg/jqmcdGXY6IyEEpOCJUs6+Rqx9cyqC+vbizvIzsrIycyygiGSZVJgD2OK2tzvWPLmf7nnrm/fPJHKFniYtImtARR0TuXrSRRet3cuMnJzD1yAFRlyMicsgUHBF4ecNOfvCHN7iwbDiXnzQy6nJERDpFwZFk1TX7+fIjrzFmSD++e9EkPaBJRNKOgiOJGppbuOahZTS1OD+5fBp9emmISUTSj/ZcSfTt365jRWUNP7n8eI4a3C/qckREDouOOJLkideq+PVf3uaq04/inInDoi5HROSwKTiS4PV39nDD46uYMXogX/vE2KjLERHpEgVHyPbWN/HFB5dRkJfLXZdOJSdbf+Qikt40xhEid+ff/3clW97bxyNfOIkh/fOiLklEpMv062+IfvbyZp5d8w43nDuOGaMHRl2OiEi3UHCE5C+b3+W2Z9dz7sShXHnq6KjLERHpNgqOEOzYU8+XHn6NkQP78L2LJ2uSn4hkFI1xdLOmllaufXgZdQ3NPPyFEynIy426JBGRbqXg6Gbfe/Z1Fr/1PneWl3FscUHU5YiIdDudqupGC1Zt42cvv8lnTx7J7LKSqMsREQmFgqObbNpZy9fmr6RsRBFfP39C1OWIiIRGwdEN6hqaufrXS+mVk8WPLzueXjn6YxWRzKU9XBe5Ozc8vopNO2v5YflUhhflR12SiEioFBxd9Ou/vM3TK7byr2eP5dQxg6IuR0QkdAqOLli25X1u+e1aPj5uCF/8u6OjLkdEJCkUHIfp3doGrn1oGUML8/jBJWVkZWmSn4j0DJrHcRhaWp0vz32N9+oaeeyLH6Owjyb5iUjPoeA4DHc8/wZ/2vgu37t4MhNLCqMuR0QkqXSqqpNeWLeduxZtpPyEEVwyfUTU5YiIJJ2CoxO2vLuP6x9dzsSS/tx0wXFRlyMiEgkFxyGqb2rh6geXAnDPZdPIy82OuCIRkWhojOMQ3fjUatZu28N9n5vOiIF9oi5HRCQyOuI4BI8u3sK8JVV8+YxjOGNccdTliIhESsFxEKurd/PNp9Zw2phBfOXMY6MuR0QkcgqODtTsa+TqB5cyqG8v7iyfSrYm+YmIaIzjQFpbnX+Zt4Lte+qZ988nM7Bvr6hLEhFJCQqOA2h1Z8yQfswaO5ipRw6IuhwRkZQR6qkqMzvHzNab2UYzm5Pg9TvMbHnw9YaZ1QTtI81sWdC+xsyujnvPNDNbFWzzh2YWyvmjnOwsbjhvPFecPCqMzYuIpK3QjjjMLBu4GzgLqAIWm9nT7r62bR13vz5u/euAqcHiNuBkd28ws37A6uC9W4F7gC8ArwILgHOAZ8Lqh4iIfFiYRxwzgI3uvtndG4G5wOwO1r8UeATA3RvdvSFo791Wp5kNA/q7+1/c3YFfAReGVL+IiCQQ5hhHCVAZt1wFnJhoRTMbCYwGFsa1jQB+BxwD/Lu7bzWz6cF24rdZcoBtXgVcBVBcXExFRcVhdyQKtbW1aVdzV6nPPYP6nP5SZXC8HJjv7i1tDe5eCUw2s+HAk2Y2vzMbdPd7gXsBpk+f7jNnzuzGcsNXUVFButXcVepzz6A+p78wT1VVA/G3jy0N2hIpJzhN1V4wrrEaOC14f+khblNEREIQZnAsBsaY2Wgz60UsHJ5uv5KZjQMGAK/EtZWaWX7w8wDgVGC9u28D9pjZScHVVJ8BngqxDyIi0k5op6rcvdnMvgQ8B2QD97n7GjO7GVji7m0hUg7MDQa724wHbjczBwz4vruvCl67BngAyCd2NZWuqBIRSaJQxzjcfQGxS2bj225st3xTgvc9D0w+wDaXABO7r0oREekM+/Av+pnJzHYCb0ddRycNAnZFXUSSqc89g/qcPka6++D2jT0iONKRmS1x9+lR15FM6nPPoD6nP90dV0REOkXBISIinaLgSF33Rl1ABNTnnkF9TnMa4xARkU7REYeIiHSKgkNERDpFwZFizGyEmS0ys7XBQ6y+EnVNyWJm2Wb2mpn9NupaksHMisxsvpm9bmbrzOzkqGsKk5ldH/ybXm1mj5hZXtQ1hcHM7jOzHWa2Oq5toJk9b2Ybgu9p/VhRBUfqaQb+1d0nACcB15rZhIhrSpavAOuiLiKJ7gSedfdxwBQyuO9mVgJ8GZju7hOJ3YaoPNqqQvMAsQfMxZsDvODuY4AXguW0peBIMe6+zd2XBT/vJbYzSfjMkUxiZqXA+cDPo64lGcysEDgd+AV88PCymkiLCl8OkG9mOUAfYGvE9YTC3V8C3mvXPBv4ZfDzL0nzB9ApOFKYmY0i9jjdVyMuJRn+B/ga0BpxHckyGtgJ3B+cnvu5mfWNuqiwuHs18H1gC7FHQ+92999HW1VSFQd39wZ4ByiOspiuUnCkqOBZ648BX3X3PVHXEyYz+ySww92XRl1LEuUAxwP3uPtUoI40P33RkeCc/mxigTkc6Gtml0dbVTSCO4Gn9TwIBUcKMrNcYqHxkLs/HnU9SXAKcIGZvUXs2fRnmNmD0ZYUuiqgyt3bjibnEwuSTHUm8Ka773T3JuBx4GMR15RM281sGEDwfUfE9XSJgiPFBA+o+gWwzt1/EHU9yeDuN7h7qbuPIjZgutDdM/q3UXd/B6g0s7FB08eBtRGWFLYtwElm1if4N/5xMvhigASeBj4b/PxZ0vwBdAqO1HMKcAWx37qXB1/nRV2UhOI64CEzWwmUAd+NtpzwBEdW84FlwCpi+56Mug1HGzN7hNgTTceaWZWZXQncCpxlZhuIHX3dGmWNXaVbjoiISKfoiENERDpFwSEiIp2i4BARkU5RcIiISKcoOEREpFMUHJJRzMzN7Pa45X8zs5uCnz9vZvPM7Gkzm3aQ7YyKv7vpIX72l4O73D50GHWPMrNPd/Z9XWVmXzWzzwQ/P2BmFwc/DwxuhfJ5MxtsZs8muzZJXQoOyTQNwEVmNijBa1e6+yXA1YRze49rgLPc/bLDeO8ooNPBYWbZh/FZbe/NAf4JeLhdeyHwHHCvu9/v7juBbWZ2yuF+lmQWBYdkmmZiE8uuT/Catfv+4RfNppnZCjNbAVwb155tZv9tZovNbKWZ/XOC9/4EOAp4JnjuRN/guQx/DX5znx2sN8rMXjazZcFX2203bgVOCyZ8Xm9mnzOzu+K2/1szmxn8XGtmtwd1nmxmlwefs9zMfhrUmx0cQaw2s1VmlujP4wxgmbs3x7X1A54BHnb3e+LanwQOJxAlAyk4JBPdDVwW/OYc7xdm9gSxYLktwfvuB65z9ynt2q8kdjfXE4ATgC+Y2ej4Fdz9amK3CZ/l7ncAXyd265QZwCzgv4O73+4gdlRyPPCPwA+DTcwBXnb3suD9HekLvBrU+W6wnVPcvQxoIbaDLwNK3H2iu08K+tbeKUD7G0v+APhjghqWAKcdpC7pIXKiLkCku7n7HjP7FbEHB+2Pa78PuC/Re8ysCCgKnqUA8Gvg3ODns4HJbef/gUJgDPBmB2WcTezGjf8WLOcBRxILl7vMrIzYTv7YTnUupoXYTTAhds+nacDi2C2gyCcWTr8BjjKzHwG/AxLdwnwYH71f1EJgtpl9393jb8S3g9hdbUUUHJKx/ofYfZE++E3bzL4DHB0s/sbdD3UQ24gdiTzXic834B/cff2HGmMD9duJPfEvC6g/wPub+fAZgfjHrNa7e0vc5/zS3W/4SAFmU4BPEBvTuYTYeEa8/e22C7G7E/8JWGBms4KHibV9/n5E0KkqyVDu/h4wj9hppra2r7t7efD1ULv1a4AaMzs1aIo/n/8c8MXgdveY2bGH8NCl54DrgjvBYmZTg/ZCYJu7txK7mWXb4PZeoCDu/W8BZWaWZWYjgBkH+JwXgIvNbEjwOQPNbGRwcUCWuz8GfIPEt2xfBxzTvjE4TfUC8LiZ9QqajwU6dZWZZC4Fh2Sy24FEV1cdyOeBu81sOR8eQP85sVueLwsu0f0pBz9avwXIBVaa2ZpgGeDHwGeDge1xxB7gBLASaAkG568n9lv/m8Hn/pDY0dNHuPtaYsHw++Auu88TOwVVAlQEfXkQ+MgRCbFB8NMPsN3/IPbMkF+bWRaxcZrfHaTP0kPo7rgiPVhwscDX3H3DQdZ7CZjt7u8npzJJZQoOkR4seJBUcdxFAYnWGUzsqq0nk1aYpDQFh4iIdIrGOEREpFMUHCIi0ikKDhER6RQFh4iIdIqCQ0REOuX/Ax4W6ADEBIcWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(k_array, auc_array)\n",
    "plt.xlabel('Nº de features (K)')\n",
    "plt.ylabel('AUC')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d982d26",
   "metadata": {},
   "source": [
    "Percebe-se que quanto maior o número de features, maior o AUC. Por esse motivo, foram mantidas as 11 features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393cd4dd",
   "metadata": {},
   "source": [
    "# 8. Treinamento de modelos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "434c07ec",
   "metadata": {},
   "source": [
    "* Dummy (baseline)\n",
    "* Logistic Regression\n",
    "* SVC\n",
    "* KNN\n",
    "* Decision Tree\n",
    "* Random Forest\n",
    "* LGBM\n",
    "\n",
    "\n",
    "Foi utilizado para fins comparativos um modelo dummy, que faz previsões de forma aleatória."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36e6af8b",
   "metadata": {},
   "source": [
    "`Observação`: a métrica priorizada será o recall, considerando que consideraria de todas as pessoas que estão em churn, a detecção da maioria delas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50299de7",
   "metadata": {},
   "source": [
    "## 8.1 Dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "11b72cde",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:33.168021Z",
     "start_time": "2023-06-23T22:38:33.139465Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.48      0.59       717\n",
      "           1       0.18      0.45      0.26       183\n",
      "\n",
      "    accuracy                           0.47       900\n",
      "   macro avg       0.48      0.46      0.42       900\n",
      "weighted avg       0.65      0.47      0.52       900\n",
      "\n",
      "AUC:  0.5\n"
     ]
    }
   ],
   "source": [
    "dmc = DummyClassifier(strategy='uniform', random_state=42)\n",
    "dmc.fit(X_train_tr, y_train)\n",
    "y_pred = dmc.predict(X_val_tr)\n",
    "y_score = dmc.predict_proba(X_val_tr)[:,1]\n",
    "\n",
    "print(classification_report(y_val, y_pred))\n",
    "print('AUC: ', np.round(roc_auc_score(y_val, y_score),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6e903f",
   "metadata": {},
   "source": [
    "- A precisão dos dados deu um valor próximo da proporção original dos dados, por se tratar de um modelo Dummy.\n",
    "- A acurácia deu um valor próximo de 50%\n",
    "- O AUC, como esperado, deu um valor de 50%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca1696d",
   "metadata": {},
   "source": [
    "## 8.2 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "02e6939f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:33.221693Z",
     "start_time": "2023-06-23T22:38:33.179011Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.74      0.81       717\n",
      "           1       0.39      0.66      0.49       183\n",
      "\n",
      "    accuracy                           0.72       900\n",
      "   macro avg       0.64      0.70      0.65       900\n",
      "weighted avg       0.79      0.72      0.74       900\n",
      "\n",
      "AUC:  0.752\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(C=1.5,class_weight='balanced',random_state=42)\n",
    "lr.fit(X_train_tr, y_train)\n",
    "y_pred = lr.predict(X_val_tr)\n",
    "y_score = lr.predict_proba(X_val_tr)[:,1] # Classe 1\n",
    "\n",
    "print(classification_report(y_val, y_pred))\n",
    "print('AUC: ', np.round(roc_auc_score(y_val, y_score),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee337f19",
   "metadata": {},
   "source": [
    "Por ser um modelo linear e simples, é possível dizer que é um modelo que não consegue classificar bem os dados. É importante, também, levar em consideração a possível baixa separabilidade dos dados vista anteriormente com o TSNE."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f0eed40",
   "metadata": {},
   "source": [
    "## 8.3 SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "81fee505",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:56.068572Z",
     "start_time": "2023-06-23T22:38:33.230274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.83      0.87       717\n",
      "           1       0.50      0.67      0.57       183\n",
      "\n",
      "    accuracy                           0.80       900\n",
      "   macro avg       0.70      0.75      0.72       900\n",
      "weighted avg       0.83      0.80      0.81       900\n",
      "\n",
      "AUC: 0.83\n"
     ]
    }
   ],
   "source": [
    "svc = SVC(kernel= 'rbf',class_weight='balanced', probability=True)\n",
    "svc.fit(X_train_tr, y_train)\n",
    "y_pred = svc.predict(X_val_tr)\n",
    "y_score = svc.predict_proba(X_val_tr)[:,1]\n",
    "\n",
    "print(classification_report(y_val, y_pred))\n",
    "print('AUC:', np.round(roc_auc_score(y_val, y_score), 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1433a1c9",
   "metadata": {},
   "source": [
    "## 8.4 KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "855dcd47",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:56.498499Z",
     "start_time": "2023-06-23T22:38:56.072432Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.97      0.90       717\n",
      "           1       0.67      0.21      0.32       183\n",
      "\n",
      "    accuracy                           0.82       900\n",
      "   macro avg       0.75      0.59      0.61       900\n",
      "weighted avg       0.80      0.82      0.78       900\n",
      "\n",
      "AUC:  0.764\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=10)\n",
    "\n",
    "knn.fit(X_train_tr, y_train)\n",
    "y_pred = knn.predict(X_val_tr)\n",
    "y_score = knn.predict_proba(X_val_tr)[:,1]\n",
    "\n",
    "print(classification_report(y_val, y_pred))\n",
    "print('AUC: ', np.round(roc_auc_score(y_val, y_score),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9a37d38",
   "metadata": {},
   "source": [
    "## 8.5 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "238c0074",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:56.595690Z",
     "start_time": "2023-06-23T22:38:56.511638Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.82      0.86       717\n",
      "           1       0.48      0.67      0.56       183\n",
      "\n",
      "    accuracy                           0.79       900\n",
      "   macro avg       0.69      0.74      0.71       900\n",
      "weighted avg       0.82      0.79      0.80       900\n",
      "\n",
      "AUC:  0.822\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(max_depth=5, class_weight='balanced',random_state=42)\n",
    "dt.fit(X_train_tr, y_train)\n",
    "y_pred = dt.predict(X_val_tr)\n",
    "y_score = dt.predict_proba(X_val_tr)[:,1]\n",
    "\n",
    "print(classification_report(y_val, y_pred))\n",
    "print('AUC: ', np.round(roc_auc_score(y_val, y_score),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed0bdfb",
   "metadata": {},
   "source": [
    "## 8.6 Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "68771351",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:58.249659Z",
     "start_time": "2023-06-23T22:38:56.599360Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.97      0.91       717\n",
      "           1       0.76      0.38      0.51       183\n",
      "\n",
      "    accuracy                           0.85       900\n",
      "   macro avg       0.81      0.68      0.71       900\n",
      "weighted avg       0.84      0.85      0.83       900\n",
      "\n",
      "AUC:  0.819\n"
     ]
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(class_weight='balanced',random_state=42)\n",
    "rfc.fit(X_train_tr, y_train)\n",
    "y_pred = rfc.predict(X_val_tr)\n",
    "y_score = rfc.predict_proba(X_val_tr)[:,1]\n",
    "\n",
    "print(classification_report(y_val, y_pred))\n",
    "print('AUC: ', np.round(roc_auc_score(y_val, y_score),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85c86a42",
   "metadata": {},
   "source": [
    "## 8.7 LGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b4cc796b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:38:59.197090Z",
     "start_time": "2023-06-23T22:38:58.255471Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.87      0.88       717\n",
      "           1       0.54      0.58      0.56       183\n",
      "\n",
      "    accuracy                           0.81       900\n",
      "   macro avg       0.72      0.73      0.72       900\n",
      "weighted avg       0.82      0.81      0.82       900\n",
      "\n",
      "AUC:  0.816\n"
     ]
    }
   ],
   "source": [
    "lgbm = LGBMClassifier(n_estimators=200, class_weight='balanced',random_state=42)\n",
    "lgbm.fit(X_train_tr, y_train)\n",
    "y_pred = lgbm.predict(X_val_tr)\n",
    "y_score = lgbm.predict_proba(X_val_tr)[:,1]\n",
    "\n",
    "print(classification_report(y_val, y_pred))\n",
    "print('AUC: ', np.round(roc_auc_score(y_val, y_score),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48f9310d",
   "metadata": {},
   "source": [
    "O modelo escolhido foi o SVC, pois apresentou o maior AUC.No entanto, há modelos mais rápidos, como o LGBM."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2084d4c6",
   "metadata": {},
   "source": [
    "## 8.8 Ajustando os pesos do SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02b7fac9",
   "metadata": {},
   "source": [
    "Para fins investigativos, foi analisado o ajuste de pesos das classes para melhorar o recall, métrica importante para o problema de negócio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1f526756",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:39:23.367608Z",
     "start_time": "2023-06-23T22:38:59.214430Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.72      0.81       717\n",
      "           1       0.41      0.78      0.54       183\n",
      "\n",
      "    accuracy                           0.73       900\n",
      "   macro avg       0.67      0.75      0.67       900\n",
      "weighted avg       0.82      0.73      0.75       900\n",
      "\n",
      "AUC: 0.829\n"
     ]
    }
   ],
   "source": [
    "# Ajustando os pesos das classes no class_weight para melhorar o recall\n",
    "\n",
    "svc = SVC(kernel= 'rbf',class_weight={0:0.15, 1:0.85}, probability=True)\n",
    "svc.fit(X_train_tr, y_train)\n",
    "y_pred = svc.predict(X_val_tr)\n",
    "y_score = svc.predict_proba(X_val_tr)[:,1]\n",
    "\n",
    "print(classification_report(y_val, y_pred))\n",
    "print('AUC:', np.round(roc_auc_score(y_val, y_score), 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c20b71",
   "metadata": {},
   "source": [
    "Percebe-se que quando aumenta o recall, diminui a precisão."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f1650f7",
   "metadata": {},
   "source": [
    "# 9. Otimização de hiperparâmetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1d9bcc80",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:30.772225Z",
     "start_time": "2023-06-23T22:39:23.375196Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "                   n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.  , 1.25, 1.5 , 1.75, 2.  ]),\n",
       "                                        &#x27;class_weight&#x27;: [{0: 0.5, 1: 0.5},\n",
       "                                                         {0: 0.4555555555555556,\n",
       "                                                          1: 0.5444444444444444},\n",
       "                                                         {0: 0.4111111111111111,\n",
       "                                                          1: 0.5888888888888889},\n",
       "                                                         {0: 0.3666666666666667,\n",
       "                                                          1: 0.6333333333333333},\n",
       "                                                         {0: 0.3222222222222222,\n",
       "                                                          1: 0.6777777777777778},\n",
       "                                                         {0: 0.2777777777777778,\n",
       "                                                          1: 0.7222222222222222},\n",
       "                                                         {0: 0.2333333333333334,\n",
       "                                                          1: 0.7666666666666666},\n",
       "                                                         {0: 0.18888888888888888,\n",
       "                                                          1: 0.8111111111111111},\n",
       "                                                         {0: 0.14444444444444438,\n",
       "                                                          1: 0.8555555555555556},\n",
       "                                                         {0: 0.09999999999999998,\n",
       "                                                          1: 0.9}],\n",
       "                                        &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;,\n",
       "                                                   &#x27;sigmoid&#x27;]},\n",
       "                   scoring=&#x27;roc_auc&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=SVC(probability=True, random_state=42),\n",
       "                   n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.  , 1.25, 1.5 , 1.75, 2.  ]),\n",
       "                                        &#x27;class_weight&#x27;: [{0: 0.5, 1: 0.5},\n",
       "                                                         {0: 0.4555555555555556,\n",
       "                                                          1: 0.5444444444444444},\n",
       "                                                         {0: 0.4111111111111111,\n",
       "                                                          1: 0.5888888888888889},\n",
       "                                                         {0: 0.3666666666666667,\n",
       "                                                          1: 0.6333333333333333},\n",
       "                                                         {0: 0.3222222222222222,\n",
       "                                                          1: 0.6777777777777778},\n",
       "                                                         {0: 0.2777777777777778,\n",
       "                                                          1: 0.7222222222222222},\n",
       "                                                         {0: 0.2333333333333334,\n",
       "                                                          1: 0.7666666666666666},\n",
       "                                                         {0: 0.18888888888888888,\n",
       "                                                          1: 0.8111111111111111},\n",
       "                                                         {0: 0.14444444444444438,\n",
       "                                                          1: 0.8555555555555556},\n",
       "                                                         {0: 0.09999999999999998,\n",
       "                                                          1: 0.9}],\n",
       "                                        &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;,\n",
       "                                                   &#x27;sigmoid&#x27;]},\n",
       "                   scoring=&#x27;roc_auc&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=SVC(probability=True, random_state=42),\n",
       "                   n_iter=20,\n",
       "                   param_distributions={'C': array([1.  , 1.25, 1.5 , 1.75, 2.  ]),\n",
       "                                        'class_weight': [{0: 0.5, 1: 0.5},\n",
       "                                                         {0: 0.4555555555555556,\n",
       "                                                          1: 0.5444444444444444},\n",
       "                                                         {0: 0.4111111111111111,\n",
       "                                                          1: 0.5888888888888889},\n",
       "                                                         {0: 0.3666666666666667,\n",
       "                                                          1: 0.6333333333333333},\n",
       "                                                         {0: 0.3222222222222222,\n",
       "                                                          1: 0.6777777777777778},\n",
       "                                                         {0: 0.2777777777777778,\n",
       "                                                          1: 0.7222222222222222},\n",
       "                                                         {0: 0.2333333333333334,\n",
       "                                                          1: 0.7666666666666666},\n",
       "                                                         {0: 0.18888888888888888,\n",
       "                                                          1: 0.8111111111111111},\n",
       "                                                         {0: 0.14444444444444438,\n",
       "                                                          1: 0.8555555555555556},\n",
       "                                                         {0: 0.09999999999999998,\n",
       "                                                          1: 0.9}],\n",
       "                                        'kernel': ['linear', 'poly', 'rbf',\n",
       "                                                   'sigmoid']},\n",
       "                   scoring='roc_auc')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC(probability=True,random_state=42)\n",
    "\n",
    "param = {'class_weight' : [{0:1-x, 1:x} for x in np.linspace(0.5, 0.9, 10)],\n",
    "         'C': np.linspace(1.0, 2.0, 5),\n",
    "         'kernel':['linear', 'poly', 'rbf', 'sigmoid']\n",
    "        }\n",
    "\n",
    "\n",
    "svc_opt = RandomizedSearchCV(estimator=svc, param_distributions= param, n_iter=20, scoring='roc_auc',cv=5)\n",
    "svc_opt.fit(X_train_tr, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c431bff1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:30.792305Z",
     "start_time": "2023-06-23T22:58:30.776410Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1.25, class_weight={0: 0.14444444444444438, 1: 0.8555555555555556},\n",
       "    probability=True, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1.25, class_weight={0: 0.14444444444444438, 1: 0.8555555555555556},\n",
       "    probability=True, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=1.25, class_weight={0: 0.14444444444444438, 1: 0.8555555555555556},\n",
       "    probability=True, random_state=42)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando melhor estimador\n",
    "\n",
    "final_svc = svc_opt.best_estimator_\n",
    "final_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e4830346",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:30.807581Z",
     "start_time": "2023-06-23T22:58:30.796650Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.849365280714118"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando o melhor score (AUC)\n",
    "\n",
    "svc_opt.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "10d1bb6d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:31.748885Z",
     "start_time": "2023-06-23T22:58:30.811491Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.71      0.81       717\n",
      "           1       0.41      0.80      0.55       183\n",
      "\n",
      "    accuracy                           0.73       900\n",
      "   macro avg       0.67      0.76      0.68       900\n",
      "weighted avg       0.83      0.73      0.75       900\n",
      "\n",
      "AUC:  0.829\n"
     ]
    }
   ],
   "source": [
    "# Performance dos dados de validação\n",
    "\n",
    "y_pred = final_svc.predict(X_val_tr)\n",
    "y_score = final_svc.predict_proba(X_val_tr)[:,1]\n",
    "\n",
    "print(classification_report(y_val, y_pred))\n",
    "print('AUC: ', np.round(roc_auc_score(y_val, y_score),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f82f6d4d",
   "metadata": {},
   "source": [
    "Com isso, foi finalizada a avaliação do modelo com os dados de validação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "69ad84cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:35.996396Z",
     "start_time": "2023-06-23T22:58:31.789979Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wqF0rYebT8FRl2LbQvYDi9V5akDOT9Zqn+tK5ngalFXeoWAWCmGZwyQdO78Hb50GFXLOzvnMWjL8Q9qw74cslsGRkZnH+qF+JHT6ZrjWcKwVPXtCC8BDdEiHuUbEKNJUbw7BFcMGbOds2zIDRcTAqDmY+C9a6l09cd8f/Fmff6NuiUjAz7u3B1Z3ruJxKAp2KVaBqcwU8uBXaDcnZtncdzBoJj5eDb2+HzHTX4ok7UtIzmbpiZ/Z6jagg6lWOcjGRiEPFKpCFl4EBo+C/e+HGGc4IGEct/hheaw1ph93LJyXKWkuT/07NXv9t+FmEB2tECvEOBSpWxpi+xpg1xpj1xpjheTxf2xgz0xiz2Biz1BijAcJ8SXAI1GgPV02Eu5c5QzkBHNwGnw52N5uUqO4NKwEQZKBGuVIupxHJkW+xMsYEA2OAfkAz4HJjTLPjdnsE+Nxa2xa4DHijqINKCSlXGx7emTNY7l+/wOvtYJdGw/B3xhiu6FibtrXLsXREH7fjiByjIDcFdwTWW2s3AhhjJgADgZW59rFAWc9yNLC9KENKCQsOcQbLjawCP9wP+zbA+/3g/g0aJNfPZGRm8eT3K/l9w1661q/I//VtQr+WmuFXvE9BilUNYEuu9a3A8YOBjQB+NMbcCUQCvYsknbir003Oda1vboEj++GJCtDtbuj5MIRoziJfZ60l/sUEtu4/AsC6XUls3X+EcUM1wol4H2Pz6aZsjLkY6GutvcGzfjXQyVp7R6597vG810vGmC7AOKCFtTbruPe6CbgJICYmpv2ECRNOOXhSUhJRUeqldDJF1UZV/plFs1UvH7Ntbsc3OFK6xmm/t9sC9efIWsvz81NYte+Y/6I80CGCphVzjp4DtX0KQ22Uv8K0Uc+ePRdaa+OO316QYtUFGGGt7eNZfxDAWvtsrn1W4BS0LZ71jUBna+2uE71vXFycXbBgQYHC5yUhIYH4+PhTfn0gKNI2SkuGmc84QzYd9d89zvxaPizQfo52H0qlw9M/c37r6nRvUIn/+3IpAKuf7EtE6L9P8QZa+5wKtVH+CtNGxpg8i1VBTgPOBxoaY+oC23A6UFxx3D6bgV7AB8aYpkAEsLtAycQ3hEVCn6edYZve7eVs+2IoDP7YGfVdvNq6fw5x9iu/ZK/vOHCESzvU4pK4mlgLQZo0Ubxcvr0BrbUZwB3ANGAVTq+/FcaYJ4wxAzy73QvcaIz5E/gUGGrzO2QT31QzDvp4DqpXf+/cQPzRBbBykpup5CQSD6cfU6gAejWNAZwegCpU4gsKNEWItXYKMOW4bY/mWl4JdCvaaOK1utzmTD0yb6yzvnGm8+j1KHS/191s8i/RpUMZcX4zRnzndOD97o4zaFkz2uVUIoWj+azk1PR/Ac78P1g1CSbf42yb/oQz7UjPB93NJtkysyyrdhzkmq6xDO1W1+04IqdMwy3JqYuqDB2uh0f3QRdP59BZI+HTKyDloLvZBID6D03hvFG/8sXCrW5HETktKlZy+oKC4ZynoHZXZ33NZBhZC5Z+7m6uALbtwBFih0/OXv9t/R4X04icPhUrKRrGwHU/wNVf52z76kYY10dHWSVs/a4kuo2cccy2py9s6VIakaKhYiVFq/5ZcP9GqOqZ/nzLHHihAfzyAsx9Gw5sOfnr5bTlHoC2fuVINo08l6hwXZ4W36afYCl6kRXhltnONCPf3g6ZqTDjKee5H/4PysfC9T9BVBVXY/qbw2kZDPt0MRe1q8mfj53DroMpNIwp43YskSKhIyspPm2vgjsWQK/HnOtZMZ5TUfs3wYsNIVnXUYqKtZZmj07j51W7uO2TRZQKDVahEr+iYiXFq1JD6H6Pcz3r1l9zph4BeKE+LPmfe9n8yOqdh45ZD9WkieJnVKykZJ31CJz/es76N7fC/wZDZoZ7mfzAjyv+AaBq2Qg2jTwXoyGwxM+oWEnJa38NDFuSs752KjxZEb64Fv6aDXs3uBbNF6VlZDFh/mYAnr+4lctpRIqHipW4o0JdGL4FWg2GYM/cWCu+gg/Pg1Ht4KubdLRVQGv/OcSOxBTqVYrkjAaV3I4jUizUG1DcE1EWLhoLF74NG2bAvHdg7Q/Oc0s/cx5troSWFztd4uVf7vx0Mf8kpjA4rha1KpTSoLTit1SsxH3GQINezsNap7v7kk+c55Z84jwanA2XfADhmuTuqM/mb+a7P7cTEmR486p2lC+t2ZvFf+k0oHgXY+CCN+DBrdDx5pzt63+CZ2s4nTEm3QnpR9zL6AWOpGXywJfLAMjIslSMCtdRlfg1FSvxTuFloP/zMCIRLngzZ/vaqbDoI3i6KiybCKmHTvwefuzc12dnL39/5xkuJhEpGSpW4v3aXAGP7IbzXoX6vXK2f3k9PFsTfnzEOX0YIKav+oeNe5IBeHVwG1rU0NxU4v9UrMQ3hIRB3LVw9Vdw6+9QuUnOc7+PcmYsDpBTg7PX5Yz8cV6rai4mESk5Klbie2Kaw+1z4eF/oOn5Odtfa+3XR1hH0jL5atFWqkVHADDt7jMJCdZ/YQkM6g0ovis0AgZ/7MxQPPslSPoHnq8L1/4AVZq6ne6EFmzaR2pGFh3rViC0gMXm773J9HghAYBm1cqyaeS5xZhQxPvozzLxfb0ezTnCOrIfPr/G604J7k1KJXb4ZD6fv4Wh78/nynfnkpRSsJueM7MsF4z5LXu9eq4pQEQChYqV+IfBH8Ndf4IJgj1rnN6C05/0itOCWVmW9k/9DMD/fbkU68mUkZV/tt2HUqn/0BT2H04HYNTlbXlnSPviCyvipVSsxH+Ujz22m/vsF537slwsWEu2HKDeQ1Oy11+7rA2RnokQM/MpVt/9uZ2bxi/IXr+/T2POb11dg9RKQFKxEv/S+jJ4eCc0HeCsr5vmdG13gbXHnr7r3rASA9vUIMRz825GVtYJX/vQ18u489PFbNydzFMXtGDSHd24vWeDYs8s4q1UrMT/hJaCSz6E3o8763+MhldaQFZmicZISs3g3JZO1/L3r+3A+Os7AWSPNHGiWjVh3mb+N9cZRT3xSDqDO9SiVc1yxZ5XxJupWIl/CgqCM+6Glpc464lbnLmzSsD+5DSOpGVSJiKU3YdSef3ytvRsXCX7+eCjxSqP05PpmVkM/2pZ9vpfz/YvcI9BEX+m/wXi3wa9Cw16O8tLP4NJw4r1435Zu5u2T/7EiEkrAPj8li4MaF39mH2CPNecMvMoVq/8tDZ7efb/9dT1KREPFSvxf1d8DlU9kxIu+hDeOgP+GFPkHS/emrWBIe/NA+CzBVuye/0d76PrOpJwXzy1ypc+ZvvhtAwm/bmdIAOf39yFWhVK5/l6kUCkm4LF/wUFwy2z4YWGkLwLdi5zHhkpwOl3A7fWMmbmel78Meeo6P2hHU54VHS0CKWkZzLs08VM+nM7k4edQfPq0Uy+szu/bdhDx7oVTjuXiD9RsZLA8Z/lsOwLZ4oRmwXTn6BRtT4QH39ab7ti+8FjCtWap/oSHhJ8wv0Tj6TT+vEfj4322RJ+/E8PokuH0r+lxvsTOZ5OA0rgCAmHtlfBf/dCLadnXvUd0+C3107rlGDz6mWzl+c+1OukhQr4V6ECePnSNqf8+SKBQMVKAk9QEFw3DZoNdNZ/ehQ+OBf2/VXgt/h13R5ih08mKdUZMmnZiHPYNPJcYspG5Pva/+vbOHt51v3xbBp5rqb5EMmHipUEJmPgkg/ZUG+os/73bzCmI2SknfRlGZlZNHt0KleNmws490RlWSgTEVrgj74tvgGbRp7LppHnUqdi5Kl+ByIBRcVKApcxbKl9oTOmYFgUZKY504zsXPavXSfM20zs8Mk0ePgHDqfl3FxcLbpU9n1TIlJ81MFCpHwsnPEfmPEkHNrudG2PKAcxLaDZAOh4EzPX7DrmJY8PaM41XWPdSCsSkFSsRADOvM+5F+t/nhEvUg7A3786j42zeHXweDo+/TOPnt+M81pVp1TYyTtRiEjRUrESOarROfDYAdizFtKPkDzpPiJ3zoc1k4lY/gnL7u8LUVXyfRsRKXq6ZiWSmzEkl63Pk4vCaL7pPxywTgcIM+lOZzDcnctdDigSmFSsRHI5lJLOFe/OZdyvTjf2i9Me4+/q/SEkAjJTYcr9cHifyylFAo9OA4p4HDicRteRMziclklkWDDvXtOBLvUrAjfDtoXwzlmw+Xd4vi7U6wmDxkFkRbdjiwQEHVmJeMzZuJeMLEuFyDC+vaObp1B51GgP/V/MWd84E16oB2nJJR9UJACpWElAO5JheTNhA4mH0+nbohrPD2rFu9fE0aBKmX/v3PFGZ6imxv1ztj1T3Zl2ZP/fJRdaJACpWEnAWrXjILf+fJjnpq5md1IqABe0rUG72uVP/KLgELj8U+jzbM62RR/Ca61g0fhiTiwSuFSsJOBkZVkGvfk7/V6bnb1tzsa9hXuTLrfBA5sg/iEIDne2TboDpj1cdEFFJJuKlQQUay13fLqIhX/vz9728z09uKpzncK/WanyEP8A3J1reKY/RjsFq4gndhQJdCpWElCmLNvJlGU7ARjaNZY3e5emQZWo03vTMjHwyC5oeamz/sdoWP7laSYVkdzUdV0CSpva5RjerwkVIsO4NK4WCQm7i+aNQ8Jh0DsQXQN+fQW+vB4a94MwjaouUhR0ZCV+LT0zi2krdhI7fDI9X0ygWtkIbulRn0vjahXPB3a+PWf5merw02OQmV48nyUSQHRkJX5pzc5DXPnuHPYk5cxP1TimDEHFPZ1HVGW45AP4Yqiz/tursOJruOpLqNSweD9bxI/pyEr8zmfzN9Pn1V+OKVQA7eucpEt6UWp+Idy/EfqOhOAwOPA3jI6DQztL5vNF/JCOrMTvRJfKmbX3onY1eHJgCyLDS/hHPbIidL4VWg12hmcCeKkx3LFAR1gip0BHVuI3rKe7eLva5bmqc202PNOfly9tU/KFKrfSFeDGGTnro+Ng6kOQesi9TCI+qEDFyhjT1xizxhiz3hgz/AT7XGqMWWmMWWGM+V/RxhQ5uW+XbKPug1N48KulhIUE8dQFLb1nuvka7eHu5RDkOeKbMwbGnaN7sUQKId9iZYwJBsYA/YBmwOXGmGbH7dMQeBDoZq1tDtxd9FFF8jZm5nrumrAEgJmrd7t7JHUi5Wo592Jd9ZWzvmuljq5ECqEg/6s7AuuttRsBjDETgIHAylz73AiMsdbuB7DW7irqoCLHS07N4I7/LWLmmpx7pRLujyc02EvPbgcFQYNeEB4NqYlgM91OJOIzjM3nVIQx5mKgr7X2Bs/61UAna+0dufb5BlgLdAOCgRHW2ql5vNdNwE0AMTEx7SdMmHDKwZOSkoiKOs2RB/ycP7eRtZbRS1JZ+E/OL/wxvUoTGVq4U39utFG3X68kNCOJX7uNJyO0bIl+dmH5889QUVEb5a8wbdSzZ8+F1tq447cX1fmSEKAhEA/UBH4xxrS01h7IvZO1diwwFiAuLs7Gx8ef8gcmJCRwOq8PBP7eRsuy1rFq1ga+u/MM6lc+tV8WrrTR3FDIgDO6dvP6yRv9/WeoKKiN8lcUbVSQYrUNyH27f03Ptty2AnOttenAX8aYtTjFa/5ppRM5TnJqRvY1qWG9GnJuq2qnXKjcc/ToTx0sRAqqICf35wMNjTF1jTFhwGXApOP2+QbnqApjTCWgEbCx6GKKwK6DKTR/bBqxwyezxzP/lO8VKsB4/tupN6BIgeVbrKy1GcAdwDRgFfC5tXaFMeYJY8wAz27TgL3GmJXATOB+a20hJwgSObEDh9O44t252eu5b/z1OcZzZGWz3M0h4kMKdM3KWjsFmHLctkdzLVvgHs9DpMikpGcy/MulfLNkOwBVy0bw5W1dvbfHX4HoNKBIYXnhDSkiji37DtP31V9ITnN6/LWqGc1rl7WlRrlSLic7TdlHVipWIgWlYiVeq0rZ8OxC9ci5TbmuW93iHzW9JGRfs9JpQJGCUrESrxUeEszM++KpUibcO0elOGU6DShSWP70G0B82N97kxkzcz2z1+1hR2IKE2/pQlxsBepW8sOZdnUaUKTQVKzEVSnpmVz57lwWb95PVq7f3VeNm8uqJ/pijB+c9jueTgOKFJqKlbjq3i/+ZOHf+wE4p1kMV3WuA0DnehX9s1ABOg0oUngqVuIaay1/bjkAOB0obuhez91AJSW7VqlYiRSUipWUqKwsy9JtibSpVQ5jDC9c3Jq5f+0NnEIF6MhKpPBUrKTEzFq7m+d+WM3KHQeZ/3BvKpcJp0v9inSp792DuRY5dbAQKTQVKyl2KemZdB05g33Jadnbth84QuUy4S6mcpHx5dE3RNyhYiXFKvFwOl1GTuew5+be7g0r8cyFLalVobTLydyksQFFCkvFSorNL2t3M+S9ednr39zejTa1yrkXyFvoNKBIoalYSZFb+Pc+rIVuDSrRtFpZ1u86xFtXtVehyqYOFiKFpWIlReL3DXsY/uUyNu87DDhd0dvVLs+kO7r5+AjpxUBHViKFpmIlpyUtI4snv1/J+Dl/H7N98ZYDWFChykt2BwsVK5GCUrGSU7Zl32Eufut3/jmYmr3tkXObcmWnOpQKC3YxmbdTBwuRwlKxklNWLTqCBlWiCAkKYtQVbWlXu7zbkXyDTgOKFJqKlRTY9gNHePXntazflcRD/ZsSF1uBN65sT6nQYMJCdLqv4NTBQqSwVKykQDIys+g6ckb2+tEbfKNLhboVyXfpyEqk0FSsJF9Lthzgmcmrstf/07sRvZrGuJjIxxkdWYkUloqVnNTN4xcwbcU/2evD+zXhlh71XUzkD9TBQqSwVKzkpGqUc4ZFqhQVxugr2tG5XoANOlscjh5ZpR12N4eID9FVcTnGyu0H6fliAks880zd0qMeH17XkQWPnK1CVVTCopyvHw9yN4eID1GxkmyfzttM/9dn89eeZB7+ehkAVcpG0KNRZZeT+Znu9zhfM47Awg/dzSLiI1SsBIAxM9fz4FdOgapXKZLPbu7iciI/1qA3dL7dWf5uGHx7u3oGiuRD16wCXEp6Jg9/vZwvF23FGHh8QHOu7lwHk91jTYpF78cgtBT8PgoWfwwNzobmF7idSsRr6cgqwD341TK+XLSVIAPPXtiSIV1iVahKQkg49PovdL7VWf/iGnj3bJj5jLu5RLyUilWAu6VHfRrFRDHpjjO4rGNtt+MEnu73Qni0s7x1Hsx6DlZ9724mES+k04ABxlrL14u3sXjzAW46sx6Nq5Zh6l1nEhSkoylXRJSFu5fC7jWwYQbMGgmT74HYblBKYy2KHKViFUD2J6fR6+VZ2UMlda5XkVoVSqtQua1UOajdCWp2gI0JsGUOTHsELhjjdjIRr6HTgAFi+bZE+r72S3ahqhQVRvdGlVxOJccICoKBoyE4HJZ8DHPHup1IxGvoyMrP7UxM4boP5rN650GyPL2jn7qgBVd1ruNuMMlbpYYQ/wBMfwJ+uB82zYZLPoAgzQ8mgU1HVn6uVFgw/xxMwQJXda7NvId6qVB5u27/gbI1neVVk+DzIboPSwKejqz80Oa9h0nLzASgTHgIt/VsQN1KpTmriUZK9wlBQXDPCpj9knOEtfp7WPgBxF3rdjIR1+jIys/sOpTCRW/+xohJK8nMsgQFGa4/o64KlS/qfi+09xSoqcMhM8PdPCIuUrHyEwv/3s+NHy2g49PT2ZOUxtKtBzii322+r9/zEFUVMlLgyYqwc7nbiURcoWLl4xb+vZ/Y4ZMZ9Obv/LTSmXeqSplwpt8bT1SYuqT7vJAw6PN0zvrE63T9SgKSrln5uB+W7cheblatLFd3qcOFbWsQEareY36j5cVQtjq83w/2rIEVX0OLi9xOJVKiVKx8VHpmFqHBQdwaX58Ff+/n1cFtiK0U6XYsKS51ukKrwbD0M5h4LUTFOKNciAQInQb0QR/P+Zthny4GoGJUON/c3k2FKhD0yTXI7Qfn6nSgBBQVKx9ireW9X//ikW+W89eeZFLSM92OJCUpshLcucizYp0jrKRdrkYSKSkqVj7iYEo6d366mCe+XwnAWU2qEB6if76AU7E+nOGZaXjF1/DRQB1hSUDQNSsf8O7sjTw1eRUAUeEhPH1hCwa2qeFyKnFN78egRjv47CrYtRLWToXG/dxOJVKs9Ke5l/th2Y7sQlUtOoKvbuuqQiXQ9HyoUN9ZnnClu1lESoCKlRfal5zGrkMpAHRt4IyMXqNcKSYP606jmDJuRhNvctVE56vNhDlvuZtFpJipWHmZuRv30uOFmTzxnXNtKrpUKJtGnstvw8+iQmSYy+nEq1SoB+2GOMtTH4ADm93NI1KMVKy8RFaWpf9rsxk8dg6HUjL4fukODqakux1LvN15r0HFBs7yqy1h1vPu5hEpJipWLtt9KJUh782j3kNTWLnjIAC39KjP+qf7UTYi1OV04vWCguD813LWZz4Ne9a7l0ekmKhYueypySv5Ze3u7PU3r2zH8H5NCAnWP40UUOwZ8FDOsFuMbg9rfnAvj0gx0G/EEmatZe7Gvdnr953TmKbVynJ+6+rMe6gX/VpWczGd+Kyw0nBTQs76p5fBt3e4FkekqBWoWBlj+hpj1hhj1htjhp9kv0HGGGuMiSu6iP4jOTWDTs9MZ/DYOUxd7vwlXKtCaX64qzujLm9LlbIRLicUn1a9LVz6Uc764vGwbdGJ9xfxIfkWK2NMMDAG6Ac0Ay43xjTLY78ywF3A3KIO6Q+mr/qH5o9NY9ehVM8WTd8hxaDZQBiRCBHlnPV3esKR/a5GEikKBTmy6gist9ZutNamAROAgXns9yTwHJBShPn8wrKtiVz/4YLs9fHXd6Rvi6ouJhK/d+2UnOVpj7iXQ6SIGJvPuGLGmIuBvtbaGzzrVwOdrLV35NqnHfCwtXaQMSYBuM9auyCP97oJuAkgJiam/YQJE045eFJSElFRUaf8+pLy/YY0Jq7L6YL+du/ShIeUzFGVr7SRm/y5jRqtGU31HT8BsKD9yySVqV/o9/Dn9ikqaqP8FaaNevbsudBa+69LSac9NqAxJgh4GRia377W2rHAWIC4uDgbHx9/yp+bkJDA6by+pJjqu5m4bh6D2tXkqQtaUCqs5CZF9JU2cpNft1G3TvBGF9j/F3EL74GbZkH1NoV6C79unyKiNspfUbRRQU4DbgNq5Vqv6dl2VBmgBZBgjNkEdAYmBXIni7/2JGcv1yxfineHxPHSpa1LtFCJEFoKLs919mJsD0hLPvH+Il6sIMVqPtDQGFPXGBMGXAZMOvqktTbRWlvJWhtrrY0F5gAD8joN6O+stcS/MJOeLyYweanT269+5Sh6N4txOZkErCpNcs2BBfw8wrUoIqcj32Jlrc0A7gCmAauAz621K4wxTxhjBhR3QF9hreWhr5exae9hADbsTnI5kYhHxfo5XdrnjYWM1JPvL+KFCnTNylo7BZhy3LZHT7Bv/OnH8i3LtyVy8/iFbDtwBIBzmsUwrFdDl1OJ5NLk/Jzlnx6DfiPdyyJyCjT54mka/PYfzP1rHwARoUG8fXUcPRpVdjmVyHGCguDqb+DjQTD3TVg+EW6fB6UruJ1MpEA03NJpyMqy1ChXKnv9twfOUqES71W/J/R52llO3g3jzobUQ+5mEikgFavTEBRk6NuiKu9f24FNI8+lYlS425FETq7zrXDjDIisAnvXwwfnwqGdbqcSyZeKVSFlZGbR/fkZDBj9KwDnNK9Kz8ZVXE4lUgg12sPg8c7yjj/hzW4akkm8nopVIexPTuOcV39hy74jLN2ayN4k9aoSH1W7M1zxubN8eI+GZBKvp2JVQCnpmdw0fgEbdzs3VT7cv6lO+4lva9QHrpzoLC/5BDYmuBpH5GRUrAogM8ty9bi5zN+0n2rREcx5sBc3nlnP7Vgip6/h2dDjAcDCRwPh4Ha3E4nkScUqH1lZlls/Xsj8TfsJDTaMu6YDVaM175T4kR4PQHhZZ/nlprDwA1fjiORFxSofQUGGhjFRRJcKZezVcTSrXtbtSCJFKygYrvoyZ/27u2DncvfyiORBxaoA/tO7Ed/feQY9m6jXn/ipWh3h/o05kza+1Q2SdrkaSSQ3FasTmPTndnYddOaRDAkOolaF0i4nEilmkRXh1t9z1l9sSFXPfFgiblOxysMrP61l2KeLOeulWdkFSyQgRNeAwZ9krzZZMxq2zHcxkIhDxeo4t4xfyGvT1wHwQL8mVCmrzhQSYJqeBw9ugyrNnfVxvSEz/eSvESlmKla5zFyzi6krnKFnLmhTnas713E5kYhLwqPg5lmkhUY762+f6W4eCXgqVrmM/+NvAOpViuTVy9q6nEbEZcGhbKk10FnetRJ+fcXdPBLQVKw80jOzmLHa6f30+MDmLqcR8Q5bag+Cc19yVn5+HA79424gCVgqVh6HUjLo0zyGdrXLcUaDSm7HEfEeHW6AOt0AC59cDKmaBVtKnoqVR/nSoZzVpArPDWqFMcbtOCLepf8LEBoJO5fCa61h9ZT8XyNShAK+WKVlZDF3416MMQzuUJuGMWXcjiTifWKaw8XjoEw1Z5T2CZfD8q/cTiUBJOCL1VuzNjB47Bxe/nGN21FEvFvjfnD3cmjU11mfeK2uYUmJCehite6fQ4ya4dxT1bl+RZfTiPiA4BC47FOo1dlZX/Ceu3kkYARsscrMsvzfl0tJz7Rc3rE2XeurU4VIgQQFQatLneVZI2HOm+7mkYAQsMXqoz82sXjzAWLKhvNg/yZuxxHxLe2HQtVWzvLU4TB3rKtxxP8FZLHasu8wz091rlE9dUFLykaEupxIxMcEBcO1U6DhOc76tId0/UqKVUAWq5d+XMOR9EzOa1WNs5vFuB1HxDeFl4Erv4CoqpCVDos+dDuR+LGALFaPD2zB0K6xjBigkSpETlvfZ5yvM5+Gg9vdzSJ+KyCLVXSpUEYMaE6lqHC3o4j4vmYXQtkazvIbXdzNIn4roIrVnI17Sc3IdDuGiH8JCoKrv3GWUw7A623BWjcTiR8KmGK1/cARrh43l54vJHA4LcPtOCL+pXKjnJuF922ELfPczSN+J2CK1dD355GeaWlWPZrSYSFuxxHxP1d8Bk3Pd5Y/vQyWTXQ3j/iVgChWB1PSWfuPM1L0HWc1cDmNiB/r8YDz9cg++PZ22L3W3TziNwKiWH08x5lUsW3tcrSpVc7dMCL+rGpLuOtPZ4T2jBRnhuEj+91OJX4gIIrVjFXOpIo3dq/nchKRAFA+FoYtcpYzjsDvo1yNI/7B74tVcmoGq3YcBNBRlUhJKVMVhnrmvFryP8hUpyY5PX5frPYfTqNzvYp0qluB6uVKuR1HJHDU6QoV6sGhHbBumttpxMf5fbGqWb4044Z24JMbOrkdRSSwGANtr3aWp/yfu1nE5/l9sToqJDhgvlUR71Ev3vl6cCtMe9jVKOLb/Po3+OvT1/H7hj1Y3U0v4o4a7XKOrv4YDau+czeP+Cy/LVbrdx3i5Z/Wct0H8zl4RBd3RVwzcDTUaO8sf3YVTH9CwzFJofltsXr/t00AXNSuJtGlNV+ViKuu/QFaXOwsz34J/vrF3Tzic/yyWKWkZzLpT2eqgmu6xLobRkQgJBwGvQvV2zrrM55yN4/4HL8sVp/M3cyhlAxa14ymcdUybscREXB6B17xhbO8dR7sXOZuHvEpflmsnpmyCoCrdVQl4l2iKkPrK5zl9/pCVpa7ecRn+F2xWr4tkcws5+Jtn+aasl7E6/Qe4XxNS4IF41yNIr7D74pV9XKluO+cRtzfpzFlItSxQsTrlImBfs87yz89ppHZpUD8rlhViAzjjrMacntPTQUi4rXaX+t8TU+GN7vCoX/czSNez++KlYj4gJAwuGups5yVDu/3hfQUdzOJV/ObYmWt5foP5vPyj2tIStVNwCJer3wduP4nZ3nfRljwnrt5xKv5TbH6df0epq/exWcLthAR4jffloh/q9UR+r3gLE97ELIy3c0jXssvfqtnZVle/XkdAFd3rqNBa0V8SdPzc5YXfeheDvFqBfqtbozpa4xZY4xZb4wZnsfz9xhjVhpjlhpjphtj6hR91BP76I9NLPx7P5XLhDOka2xJfrSInK6y1eC8V5zl7/8Dy79yN494pXyLlTEmGBgD9AOaAZcbY5odt9tiIM5a2wqYCDxf1EHzsutgCqOmr2PEdysBeOqCFpRVd3UR39NuKMS0dJYn3QlJu12NI96nIEdWHYH11tqN1to0YAIwMPcO1tqZ1trDntU5QM2ijZm37YkpvPSTc49GvxZV6dO8akl8rIgUtaAguHGGs5yWBGPjYf10VyOJdylIsaoBbMm1vtWz7USuB344nVAF1bBKFN0aVOSh/k0YdXnbkvhIESkuR7uzR1Z2Jmv8+CJYO83tVOIlTH4TExpjLgb6Wmtv8KxfDXSy1t6Rx75XAXcAPay1qXk8fxNwE0BMTEz7CRMmnHLwpKQkoqKiTvn1gUBtlD+10cm50T5hqftovmIk0QfXkFi2MYvblchVhVOmn6H8FaaNevbsudBaG3f89pACvHYbUCvXek3PtmMYY3oDD3OCQgVgrR0LjAWIi4uz8fHxBfj4vCUkJHA6rw8EaqP8qY1OzrX2iT8bnosl+uAa4nd/CJe8X/IZCkg/Q/krijYqyGnA+UBDY0xdY0wYcBkwKfcOxpi2wNvAAGvtrtNKJCISXgbaXeMsr/gKVk9xN4+4Lt9iZa3NwDm1Nw1YBXxurV1hjHnCGDPAs9sLQBTwhTFmiTFm0gneTkSkYM57GdoNcZYnXA7/rHQ3j7iqIKcBsdZOAaYct+3RXMu9iziXiAj0HQl//QL7N8Hke+DaH5xJHCXgaKgHEfFeYZFw7VQIj4bNf8D8d91OJC5RsRIR71a2Ggx4zVn+6TFIS3Y3j7hCxUpEvF/zC6FGnDP/1W+vuZ1GXKBiJSK+4cz7nK+znoOV37qbRUqcipWI+IZGfXO6s38+RAUrwKhYiYhvMMYZnT000lmffC9kprubSUqMipWI+I6gYLh/HUTFQPJu9Q4MICpWIuJbwiLhrP86yz/+F/b95W4eKREqViLie9pcCY37Q1Y6TLwO0o+4nUiKmYqViPieoCAYMArK1Ybti9SdPQCoWImIb4qsBOe+4iwnjFTvQD+nYiUivqtBL+h0K2Dhq5thwwy3E0kxUbESEd9lDPR9Fhr0howjMP5CmPqg26mkGKhYiYhvMwYu+xTirnfW57wBCz9wNZIUPRUrEfF9IWHO/FcdbnTWv7sLdix1N5MUKRUrEfEf/Z6DKs2c5ZnPuJtFipSKlYj4j6BguHIiBIXA2h/g7TPhjzcgK8vtZHKaVKxExL9E13DGEDTBsONPmPYgTBwKGWluJ5PToGIlIv6n3RC4bx10v9dZX/mtc5SVdtjdXHLKVKxExD9FVoRej8IQz83Cu1fBa60hcZu7ueSUqFiJiH+rFw+DP4GoqpC8C/53KWyZ73YqKSQVKxHxf03Pg5tmOgXrn+XwXh+Y947bqaQQVKxEJDCUrQ63/g6tLwebCVPug60L3E4lBaRiJSKBI7IiXPAm1OrkrH88SOMJ+ggVKxEJLMbAVV86BSvlAIy/CP6a7XYqyYeKlYgEnvAycM330PEmwMLHFzmzDuvmYa+lYiUigSkkDPo8A60GQ2Ya/P46fHge7NvodjLJg4qViASu4FC4aCwMfMNZ//s35zpW0m53c8m/hLgdQETEdW2vhIr14fMhzpHViw2gaiuo1RGaXwixZ7idMODpyEpEBKB2Zxg6xel4YYJh51KY/y58cC6M6QQzn4XUQ26nDFgqViIiR1VqANf/CA9ucTpgtLwEwqJg92qYNRJeaQGznoeMVLeTBhyvOg2Ynp7O1q1bSUlJyXff6OhoVq1aVQKpfEdERAQ1a9YkNDTU7Sgivi0sEup2dx4ZqbB6sjMD8db5MPNpmP0S9HgAOtzgdtKA4VXFauvWrZQpU4bY2FiMMSfd99ChQ5QpU6aEknk/ay179+5l69at1K1b1+04Iv4jJBxaXORcu1o/HT67EjJSYPrj8MsL1K45CJJbOjccS7HxqtOAKSkpVKxYMd9CJf9mjKFixYoFOioVkVNgDDTsDXcugoFjoEYcpB+m3l/j4dWW8P09sHeD2yn9llcVK0CF6jSo7URKQHQNaHsV3DgdrvySfeXbQHoyLBgHo+Ng4nWwZqpuMC5iXles3BYcHEybNm1o0aIFl1xyCYcPn/5kbY8++ig///zzCZ9/6623+Oijj077c0SkhDXszdLWj8Otf3gGyM2C5V/Cp4Nhyr1grdsJ/YaK1XFKlSrFkiVLWL58OWFhYbz11lvHPJ+RkVHo93ziiSfo3bv3CZ+/5ZZbGDJkSKHfV0S8REwzuPAt5xRh12HOtgXvwbizIXGru9n8hIrVSXTv3p3169eTkJBA9+7dGTBgAM2aNSMzM5P777+fDh060KpVK95+++3s1zz33HO0bNmS1q1bM3z4cACGDh3KxIkTARg+fDjNmjWjVatW3HfffQCMGDGCF198EYAlS5bQuXNnWrVqxYUXXsj+/fsBiI+P54EHHqBjx440atSI2bM18KaI16lYH855Es57xVnfOh9ebwcbZrqbyw94VW/A48UOn3zC5x7t15Dreji9Af83dzMPfb3shPtuGnluoT87IyODH374gb59+wKwaNEili9fTt26dRk7dizR0dHMnz+f1NRUunXrxjnnnMPq1av59ttvmTt3LqVLl2bfvn3HvOfevXv5+uuvWb16NcYYDhw48K/PHTJkCKNGjaJHjx48+uijPP7447z66qvZmebNm8eUKVN4/PHHT3pqUURcFHcdVGkGk+91JnucdCfcOAOiqridzGfpyOo4R44coU2bNsTFxVG7dm2uv/56ADp27JjdJfzHH3/ko48+ok2bNnTq1Im9e/eybt06fv75Z6699lpKly4NQIUKFY557+joaCIiIrj++uv56quvsvc7KjExkQMHDtCjRw8ArrnmGn755Zfs5y+66CIA2rdvz6ZNm4rl+xeRIlK7M1w3DcrXhcQt8EZnWPmt26l8llcfWZ3siOjQoZxhT67oVJsrOtUuks88es3qeJGRkdnL1lpGjRpFnz59jtln2rRpJ33vkJAQ5s2bx/Tp05k4cSKjR49mxoyCT/wWHh4OOJ1ATuXamYiUsPAouG4qfDTQGQXji2uhTldn1uIy1aBCPWg2AEqVdzup19OR1Sno06cPb775Junp6QCsXbuW5ORkzj77bN5///3sHoTHnwZMSkoiMTGR/v3788orr/Dnn38e83x0dDTly5fPvh41fvz47KMsEfFRZarCrb87HS9sFmyaDUs/g99ehe+GwfP1YXRH+OgCSBgJa3+ErEy3U3sdrz6y8lY33HADmzZtol27dlhrqVy5Mt988w19+/ZlyZIlxMXFERYWRv/+/XnmmWeyX3fo0CEGDhxISkoK1lpefvnlf733hx9+yC233MLhw4epV68e77//fkl+ayJSHIKCnY4XnW+FXavg0A44uMMpXJt+hT1rnMfGXB0xysdCuTrQYpDTLT4kzLX43sBYl+4DiIuLswsWLDhm26pVq2jatGmBXq/hlvKWuw0TEhKIj493N5CXUxudnNonf6fdRikHnWta2xbCzmWw9HNIOXDsPlExzpFZg17OwLrhURAa6czH5QODARSmjYwxC621ccdv15GViIibIspCRHOIae6s930OUhMhea9zpPXHGNj/F/z4sPM4hnHGLgwOd76GhENwGJSu4ByZNe4P9eKddR+nYiUi4k2CgpwOF6XKO1OWdLgBVnwNCz9wTh+mJkFaMqQlgc10BtXNSIHcs5bs2+Dc47XsCwgKgaotoVpraHI+1OvhHJH5GBUrERFvZowz6nuLi/79XGYGZKY605hkpjlfM1Lh8B6nWK37Cf7+DbYvdh4LP4CwMtCoD7S6FBqe4xOnEUHFSkTEdwWHOI+wyOOeaOR0ke92F6Qkws7lTtFaNtHpyLF8ovMoW9MZKqpyE+cm5kqNoFwtiKzsdUVMxUpExJ9FRENsN+fR4/+caUwWfgBLPoGDW53Huh+PfU1IhHMzc7146Pmg8x4uU7ESEQkkR8cv7PUY7NsIu1c53el3rXKudR3Y4vRG3L3KeSz5H7S5Avo841xPc4mK1XGCg4Np2bIlGRkZ1K1bl/Hjx1OuXLkie//Y2FgWLFhApUqViIqKIikpqcjeW0SkwIJDoHIj59Fs4LHPpR6CHUth6nDYuRTmvgkbE6DJudBqsPOaElagMmmM6WuMWWOMWW+MGZ7H8+HGmM88z881xsQWedISknuKkAoVKjBmzBi3I4mIlKzwMs5pw5t/gUHjnPu8dq+C2S/CmA7w/X8gvWRnJc+3WBljgoExQD+gGXC5MabZcbtdD+y31jYAXgGeK+qgbujSpQvbtm0DYMOGDfTt25f27dvTvXt3Vq9eDcA///zDhRdeSOvWrWndujW///47ABdccAHt27enefPmjB071rXvQUTklBkDLS+Gu/6Eq750ZkgGZ66uZ2vAG13hp8dg95pin2iyIKcBOwLrrbUbAYwxE4CBwMpc+wwERniWJwKjjTHGns7wGCNOfkHvlMeuGJFYoN0yMzOZPn169qjrN910E2+99RYNGzZk7ty53HbbbcyYMYNhw4bRo0cPvv76azIzM7NP67333ntUqFCBI0eO0KFDBwYNGkTFihVPNbWIiHtCS0GD3s4j9kz49RVnYN5dK5zHb69C6UrwnxUQGlEsEQpSrGoAW3KtbwU6nWgfa22GMSYRqAjsyb2TMeYm4CaAmJgYEhISjnmT6Ojo7NHUi2sgpdyjteflyJEjtGrViu3bt9O4cWM6d+7Mjh07+P333xk0aFD2fqmpqRw6dIjp06czZsyY7PcNCgri0KFDvPDCC3z//fcAbNmyhSVLltCxY0estSQlJWWPoJ5fnsJKSUnJbtekpKR/tbEcS210cmqf/AVeG8VA85EEZaZQ9uA6qu6cQYV9i0inNPN/m5PnK4qkjay1J30AFwPv5lq/Ghh93D7LgZq51jcAlU72vu3bt7fHW7ly5b+2ncjBgwcLvG9hREZGWmutTU5OtmeccYZ97bXXbGJioq1atWqe+1eqVMmmpKQcs23mzJm2W7duNjk52VprbY8ePezMmTOttdbWqVPH7t69+5jPKkq52/DoZ8qJqY1OTu2TP7WRtTYry9rD+074dGHaCFhg86gZBelgsQ2olWu9pmdbnvsYY0KAaGDvKdZPr1C6dGlef/11XnrpJUqXLk3dunX54osvAKfAH53eo1evXrz55puAc+owMTGRxMREypcvT+nSpVm9ejVz5uT914aIiF8wptjn5CpIsZoPNDTG1DXGhAGXAZOO22cScI1n+WJghqdC+rS2bdvSqlUrPv30Uz755BPGjRtH69atad68Od9+68z4+dprrzFz5kxatmxJ+/btWblyJX379iUjI4OmTZsyfPhwOnfu7PJ3IiLi2/K9ZmWda1B3ANOAYOA9a+0KY8wTOIdrk4BxwHhjzHpgH05B80nH3/f03XffZS9PnTr1X/vHxMRkF67cfvjhhzzfP/d09LrHSkSkYAp0U7C1dgow5bhtj+ZaTgEuKdpoIiIiDk1rLyIiXk/FSkREvJ7XFSs/6JfhGrWdiPgrrypWERER7N27V790T4G1lr179xIRUTx3j4uIuMmrRl2vWbMmW7duZffu3fnum5KSol/Mx4mIiKBmzZpuxxARKXJeVaxCQ0OpW7dugfZNSEigbdu2xZxIRES8gVedBhQREcmLipWIiHg9FSsREfF6xq2ed8aY3cDfp/EWlThuChL5F7VR/tRGJ6f2yZ/aKH+FaaM61trKx290rVidLmPMAmttnNs5vJnaKH9qo5NT++RPbZS/omgjnQYUERGvp2IlIiJez5eL1Vi3A/gAtVH+1EYnp/bJn9oof6fdRj57zUpERAKHLx9ZiYhIgPD6YmWM6WuMWWOMWW+MGZ7H8+HGmM88z881xsS6ENNVBWije4wxK40xS40x040xddzI6Zb82ifXfoOMMdYYE3A9uwrSRsaYSz0/RyuMMf8r6YxuK8D/s9rGmJnGmMWe/2v93cjpFmPMe8aYXcaY5Sd43hhjXve031JjTLtCfYC11msfQDCwAagHhAF/As2O2+c24C3P8mXAZ27n9sI26gmU9izfGkhtVJD28exXBvgFmAPEuZ3b29oIaAgsBsp71qu4ndsL22gscKtnuRmwye3cJdxGZwLtgOUneL4/8ANggM7A3MK8v7cfWXUE1ltrN1pr04AJwMDj9hkIfOhZngj0MsaYEszotnzbyFo701p72LM6BwikodkL8jME8CTwHJBSkuG8REHa6EZgjLV2P4C1dlcJZ3RbQdrIAmU9y9HA9hLM5zpr7S/AvpPsMhD4yDrmAOWMMdUK+v7eXqxqAFtyrW/1bMtzH2ttBpAIVCyRdN6hIG2U2/U4f90Einzbx3M6opa1dnJJBvMiBfkZagQ0Msb8ZoyZY4zpW2LpvENB2mgEcJUxZiswBbizZKL5jML+rjqGV00RIsXLGHMVEAf0cDuLtzDGBAEvA0NdjuLtQnBOBcbjHJn/Yoxpaa094GYoL3M58IG19iVjTBdgvDGmhbU2y+1g/sDbj6y2AbVyrdf0bMtzH2NMCM7h994SSecdCtJGGGN6Aw8DA6y1qSWUzRvk1z5lgBZAgjFmE8659EkB1smiID9DW4FJ1tp0a+1fwFqc4hUoCtJG1wOfA1hr/wAicMbEE0eBflediLcXq/lAQ2NMXWNMGE4HiknH7TMJuMazfDEww3qu5gWIfNvIGNMWeBunUAXatYaTto+1NtFaW8laG2utjcW5pjfAWrvAnbiuKMj/s29wjqowxlTCOS24sQQzuq0gbbQZ6AVgjGmKU6zyn/Y8cEwChnh6BXYGEq21Owr6Yq8+DWitzTDG3AFMw+mN8561doUx5glggbV2EjAO53B7Pc7FvcvcS1zyCthGLwBRwBeeviebrbUDXAtdggrYPgGtgG00DTjHGLMSyATut9YGzBmMArbRvcA7xpj/4HS2GBpIfzgbYz7F+YOmkue63WNAKIC19i2c63j9gfXAYeDaQr1/ALWliIj4KG8/DSgiIqJiJSIi3k/FSkREvJ6KlYiIeD0VKxER8XoqViL5MMZUNMYs8Tx2GmO2eZYPeLpyF/XnjTDG3FfI1ySdYPsHxpiLiyaZiHtUrETyYa3da61tY61tA7wFvOJZbgPkO5SOZ2QVETkNKlYipyfYGPOOZ46nH40xpQCMMQnGmFeNMQuAu4wx7Y0xs4wxC40x046ONm2MGZZrrrEJud63mec9Nhpjhh3daJy5yZZ7HncfH8YzOsBoz7xLPwNVivfbFykZ+otP5PQ0BC631t5ojPkcGAR87HkuzFobZ4wJBWYBA621u40xg4GngeuA4UBda22qMaZcrvdtgjMPWRlgjTHmTaAVzl3/nXDmBJprjJllrV2c63UXAo1x5lOKAVYC7xXHNy5SklSsRE7PX9baJZ7lhUBsruc+83xtjDNY7k+e4a6CgaNjoi0FPjHGfIMz/t5Rkz0DDqcaY3bhFJ4zgK+ttckAxpivgO44kyIedSbwqbU2E9hujJlx+t+iiPtUrEROT+4R7DOBUrnWkz1fDbDCWtslj9efi1NgzgceNsa0PMH76v+qBDRdsxIpfmuAyp45jjDGhBpjmnvm0qplrZ0JPIAzvU3USd5nNnCBMaa0MSYS55Tf7OP2+QUYbIwJ9lwX61nU34yIG/TXmkgxs9amebqPv26Micb5f/cqzpxQH3u2GeB1a+0Bz6nCvN5nkTHmA2CeZ9O7x12vAvgaOAvnWtVm4I8i/nZEXKFR10VExOvpNKCIiHg9FSsREfF6KlYiIuL1VKxERMTrqViJiIjXU7ESERGvp2IlIiJeT8VKRES83v8D/nLcRTo122oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Precision-recall\n",
    "\n",
    "y_train_score = final_svc.predict_proba(X_train_tr)\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train, y_train_score[:,1])\n",
    "\n",
    "plt.figure(figsize=(7,6))\n",
    "plt.plot(thresholds, precisions[:-1], lw=2, ls='--',label='Precision')\n",
    "plt.plot(thresholds, recalls[:-1], lw=2, label='Recall')\n",
    "plt.xlabel('Threshold')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a8baed0",
   "metadata": {},
   "source": [
    "Variação da precisão e do recall com diferentes pontos de corte (thresholds), que representam a probabilidade de o cliente estar em churn ou não. Percebe-se que à medida em que recall aumenta, a precisão cai e vice-versa. Isso é conhecido como tradeoff recall-precision. É importante observar que quando o recall dá 100%, a precisão dá 20%.Esses 20% correspondem à proporção da classe 1 dos dados. Nesse caso, a taxa de falsos positivos será maior. No caso contrário, com a precisão em 100%, o número de falsos negativos aumenta e o de falsos positivos chega a zero.Essa análise pode ser útil para ajustar o recall e precisão, escolhendo o melhor threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "20f59a84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:36.345499Z",
     "start_time": "2023-06-23T22:58:36.007288Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "fpr, tpr, thr = roc_curve(y_train, y_train_score[0:,1])\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr, tpr)\n",
    "plt.ylabel('TPR')\n",
    "plt.xlabel('FPR')\n",
    "plt.grid()\n",
    "plt.xlim(0,1)\n",
    "plt.ylim(0,1)\n",
    "plt.title('Curva ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c82524b7",
   "metadata": {},
   "source": [
    "# 10. Avaliação final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2bea514",
   "metadata": {},
   "source": [
    "Foi realizada a avaliação do modelo com os dados de teste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "16b97925",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:36.365934Z",
     "start_time": "2023-06-23T22:58:36.349252Z"
    }
   },
   "outputs": [],
   "source": [
    "# Transformação dos dados de teste\n",
    "\n",
    "X_test_tr = pipeline.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4814d851",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:37.405918Z",
     "start_time": "2023-06-23T22:58:36.369766Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.68      0.79       796\n",
      "           1       0.40      0.84      0.54       204\n",
      "\n",
      "    accuracy                           0.71      1000\n",
      "   macro avg       0.67      0.76      0.67      1000\n",
      "weighted avg       0.83      0.71      0.74      1000\n",
      "\n",
      "AUC:  0.85\n"
     ]
    }
   ],
   "source": [
    "# Predict dos dados de teste\n",
    "\n",
    "y_pred = final_svc.predict(X_test_tr)\n",
    "y_score = final_svc.predict_proba(X_test_tr)[:,1]\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('AUC: ', np.round(roc_auc_score(y_test, y_score),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6482e65c",
   "metadata": {},
   "source": [
    "O threshold é de 50%, mas se for preciso aumentar mais o recall, é interessante diminuir esse valor. Em compensação, vai diminuir a precisão e ter mais falsos positivos. Aumentando o recall, talvez a empresa entrasse em contato com clientes que não estavam em risco de churn, no entanto, as chances de encontrar os clientes que estão realmente em churn iria aumentar. Como será um inconveniente baixo, é a melhor métrica escolhida para priorizar."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b91986",
   "metadata": {},
   "source": [
    "## 10.1 Matriz de confusão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1b2a5642",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:37.799213Z",
     "start_time": "2023-06-23T22:58:37.412031Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "plt.figure(figsize= (8,6))\n",
    "ConfusionMatrixDisplay(cm).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0915454d",
   "metadata": {},
   "source": [
    "Os falso negativos deixariam de receber a atenção adequada da empresa com relação ao churn (33). Os verdadeiros positivos (171) receberiam a atenção devida."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f5f568",
   "metadata": {},
   "source": [
    "## 10.2  Curva de ganho e Lift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "972a25ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:37.839982Z",
     "start_time": "2023-06-23T22:58:37.818824Z"
    }
   },
   "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>alvo</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>1</td>\n",
       "      <td>0.976230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>1</td>\n",
       "      <td>0.953280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>1</td>\n",
       "      <td>0.940741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>1</td>\n",
       "      <td>0.884966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1</td>\n",
       "      <td>0.877853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     alvo     score\n",
       "215     1  0.976230\n",
       "953     1  0.953280\n",
       "676     1  0.940741\n",
       "548     1  0.884966\n",
       "126     1  0.877853"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Matriz ranqueada pelo score\n",
    "\n",
    "rank = pd.DataFrame(y_score, y_test).reset_index()\n",
    "rank.columns = ['alvo', 'score']\n",
    "rank = rank.sort_values(by='score', ascending=False)\n",
    "rank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "540d2123",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:37.867949Z",
     "start_time": "2023-06-23T22:58:37.843936Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taxa do alvo (20%): 0.59\n"
     ]
    }
   ],
   "source": [
    "# Top 20%\n",
    "\n",
    "decile = 0.2\n",
    "index_decile = int(decile * len(rank))\n",
    "\n",
    "top20 = rank.iloc[:index_decile]\n",
    "print(f\"Taxa do alvo (20%): {(top20['alvo'].sum() / rank['alvo'].sum()):.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1b99954e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:38.701853Z",
     "start_time": "2023-06-23T22:58:37.872522Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (8,6))\n",
    "skplt.metrics.plot_cumulative_gain(y_test, final_svc.predict_proba(X_test_tr))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6448e6e4",
   "metadata": {},
   "source": [
    "Acima, é possível ver no eixo X, as porcentagens totais dos dados ranqueados. A taxa do alvo no segundo decil (20%), por exemplo, conseguiu obter 60% dos casos de churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "85a6874d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:39.562264Z",
     "start_time": "2023-06-23T22:58:38.706279Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (8,6))\n",
    "skplt.metrics.plot_lift_curve(y_test, final_svc.predict_proba(X_test_tr))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99827111",
   "metadata": {},
   "source": [
    "É possível observar em que percentis o modelo performa melhor que a baseline com a curva de Lift. Nesse caso, em 20% da base, o modelo treinado no projeto foi aproximadamente 3 vezes melhor que o modelo aleatório (baseline)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "aacaa97d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T22:58:39.614229Z",
     "start_time": "2023-06-23T22:58:39.566303Z"
    }
   },
   "outputs": [
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6172</th>\n",
       "      <td>15656822</td>\n",
       "      <td>Day</td>\n",
       "      <td>568</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
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       "      <td>107155.40</td>\n",
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       "    <tr>\n",
       "      <th>5010</th>\n",
       "      <td>15719508</td>\n",
       "      <td>Davis</td>\n",
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       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>168080.53</td>\n",
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       "      <th>7435</th>\n",
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       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>130554.51</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>184758.17</td>\n",
       "      <td>0.940741</td>\n",
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       "      <th>9255</th>\n",
       "      <td>15622033</td>\n",
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       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
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       "      <td>101543.51</td>\n",
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       "      <td>16025.17</td>\n",
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       "      <th>1701</th>\n",
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       "      <td>50</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>194700.81</td>\n",
       "      <td>0.877853</td>\n",
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       "    <tr>\n",
       "      <th>6255</th>\n",
       "      <td>15589017</td>\n",
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       "      <td>Germany</td>\n",
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       "      <td>55</td>\n",
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       "      <td>111362.76</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16922.28</td>\n",
       "      <td>0.859260</td>\n",
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       "    <tr>\n",
       "      <th>4869</th>\n",
       "      <td>15781272</td>\n",
       "      <td>Coles</td>\n",
       "      <td>669</td>\n",
       "      <td>France</td>\n",
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       "      <td>149713.61</td>\n",
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       "      <td>1</td>\n",
       "      <td>124872.42</td>\n",
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       "    <tr>\n",
       "      <th>2518</th>\n",
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       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>95813.76</td>\n",
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       "      <th>5082</th>\n",
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       "      <td>41</td>\n",
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       "      <td>9</td>\n",
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       "      <th>2092</th>\n",
       "      <td>15757408</td>\n",
       "      <td>Lo</td>\n",
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       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>250898.09</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.785613</td>\n",
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       "      <th>3175</th>\n",
       "      <td>15764604</td>\n",
       "      <td>Sutherland</td>\n",
       "      <td>586</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>164769.02</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>119814.25</td>\n",
       "      <td>0.778428</td>\n",
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       "    <tr>\n",
       "      <th>7142</th>\n",
       "      <td>15654859</td>\n",
       "      <td>Ngozichukwuka</td>\n",
       "      <td>612</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
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       "      <th>2738</th>\n",
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       "      <td>Palmer</td>\n",
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       "      <td>Male</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>128808.65</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4842</th>\n",
       "      <td>15624820</td>\n",
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       "      <td>683</td>\n",
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       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>50911.21</td>\n",
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       "      <td>0</td>\n",
       "      <td>97629.31</td>\n",
       "      <td>0.770385</td>\n",
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       "    <tr>\n",
       "      <th>1798</th>\n",
       "      <td>15610446</td>\n",
       "      <td>Chinedum</td>\n",
       "      <td>714</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>88308.87</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5862.53</td>\n",
       "      <td>0.761681</td>\n",
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       "      <th>4784</th>\n",
       "      <td>15811588</td>\n",
       "      <td>Eluemuno</td>\n",
       "      <td>664</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>53</td>\n",
       "      <td>7</td>\n",
       "      <td>187602.18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>186392.99</td>\n",
       "      <td>0.761339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7636</th>\n",
       "      <td>15673238</td>\n",
       "      <td>McCarthy</td>\n",
       "      <td>517</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>59</td>\n",
       "      <td>8</td>\n",
       "      <td>154110.99</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>101240.08</td>\n",
       "      <td>0.756899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4732</th>\n",
       "      <td>15653937</td>\n",
       "      <td>McIntyre</td>\n",
       "      <td>638</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>123916.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16657.68</td>\n",
       "      <td>0.755318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      CustomerId        Surname  CreditScore Geography  Gender  Age  Tenure  \\\n",
       "6172    15656822            Day          568   Germany    Male   43       5   \n",
       "5010    15719508          Davis          575   Germany    Male   49       7   \n",
       "7435    15647898        Russell          610     Spain  Female   50       5   \n",
       "9255    15622033   Rapuluchukwu          847   Germany  Female   41       3   \n",
       "1701    15605279        Francis          792    France    Male   50       9   \n",
       "6255    15589017           Chiu          547   Germany    Male   55       4   \n",
       "4869    15781272          Coles          669    France    Male   50       4   \n",
       "2518    15759436        Aksenov          758    France  Female   50       2   \n",
       "5082    15793307      Calabresi          724     Spain  Female   41       4   \n",
       "5950    15806808           Hope          834   Germany  Female   57       8   \n",
       "2977    15655123     Dumetolisa          505     Spain  Female   45       9   \n",
       "2092    15757408             Lo          655     Spain    Male   38       3   \n",
       "3175    15764604     Sutherland          586    France  Female   35       7   \n",
       "7142    15654859  Ngozichukwuka          612     Spain  Female   63       2   \n",
       "2738    15662098         Palmer          650     Spain    Male   41       3   \n",
       "4842    15624820           Ross          683     Spain    Male   56       7   \n",
       "1798    15610446       Chinedum          714    France  Female   51       4   \n",
       "4784    15811588       Eluemuno          664     Spain  Female   53       7   \n",
       "7636    15673238       McCarthy          517   Germany  Female   59       8   \n",
       "4732    15653937       McIntyre          638   Germany  Female   53       1   \n",
       "\n",
       "        Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
       "6172   87612.64              4          1               1        107155.40   \n",
       "5010  121205.15              4          1               1        168080.53   \n",
       "7435  130554.51              3          1               0        184758.17   \n",
       "9255  101543.51              4          1               0         16025.17   \n",
       "1701       0.00              4          1               1        194700.81   \n",
       "6255  111362.76              3          1               0         16922.28   \n",
       "4869  149713.61              3          1               1        124872.42   \n",
       "2518   95813.76              3          1               1         67944.09   \n",
       "5082  142880.28              3          0               0        185541.20   \n",
       "5950  112281.60              3          1               0        140225.14   \n",
       "2977  131355.30              3          1               0        195395.33   \n",
       "2092  250898.09              3          0               1         81054.00   \n",
       "3175  164769.02              3          1               0        119814.25   \n",
       "7142  131629.17              2          1               0        122109.58   \n",
       "2738  128808.65              3          0               0        113677.53   \n",
       "4842   50911.21              3          0               0         97629.31   \n",
       "1798   88308.87              3          0               0          5862.53   \n",
       "4784  187602.18              1          1               0        186392.99   \n",
       "7636  154110.99              2          1               0        101240.08   \n",
       "4732  123916.67              1          1               0         16657.68   \n",
       "\n",
       "         score  \n",
       "6172  0.976230  \n",
       "5010  0.953280  \n",
       "7435  0.940741  \n",
       "9255  0.884966  \n",
       "1701  0.877853  \n",
       "6255  0.859260  \n",
       "4869  0.854216  \n",
       "2518  0.849608  \n",
       "5082  0.846407  \n",
       "5950  0.841067  \n",
       "2977  0.828604  \n",
       "2092  0.785613  \n",
       "3175  0.778428  \n",
       "7142  0.772370  \n",
       "2738  0.770585  \n",
       "4842  0.770385  \n",
       "1798  0.761681  \n",
       "4784  0.761339  \n",
       "7636  0.756899  \n",
       "4732  0.755318  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes = X_test.join(pd.Series(y_score, index=y_test.index, name='score')\n",
    "                      ).sort_values(by='score', ascending=False)\n",
    "\n",
    "clientes = clientes.drop('RowNumber', axis=1)\n",
    "\n",
    "clientes.head(20) # 20 clientes com maior chance de churn"
   ]
  }
 ],
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