Modelo_Classificação_Inadimplencia.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Importar Libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(CVXPY) Apr 03 07:27:06 PM: Encountered unexpected exception importing solver GLOP:\n",
      "RuntimeError('Unrecognized new version of ortools (9.5.2237). Expected < 9.5.0.Please open a feature request on cvxpy to enable support for this version.')\n",
      "(CVXPY) Apr 03 07:27:06 PM: Encountered unexpected exception importing solver PDLP:\n",
      "RuntimeError('Unrecognized new version of ortools (9.5.2237). Expected < 9.5.0.Please open a feature request on cvxpy to enable support for this version.')\n"
     ]
    }
   ],
   "source": [
    "# Importar Libs\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n",
    "from optbinning import OptimalBinning, BinningProcess\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "import pickle\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Carregar Conjunto de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ler os dados\n",
    "data = pd.read_csv('UCI_Credit_Card.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividir os dados em treino e teste\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.drop(['ID', 'default.payment.next.month'], axis=1), \n",
    "                                                    data['default.payment.next.month'], \n",
    "                                                    test_size=0.3, \n",
    "                                                    random_state=42)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Pré-processamento de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Listar nome das variáveis\n",
    "variable_names = X_train.columns.unique().tolist()\n",
    "\n",
    "categorical_variables = X_train.select_dtypes(include='object')\n",
    "categorical_variables = categorical_variables.columns.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Processar bining\n",
    "binning_process = BinningProcess(variable_names, categorical_variables=categorical_variables,\n",
    "                                min_prebin_size = 0.10, max_n_bins=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BinningProcess(categorical_variables=[], max_n_bins=7, min_prebin_size=0.1,\n",
       "               variable_names=[&#x27;LIMIT_BAL&#x27;, &#x27;SEX&#x27;, &#x27;EDUCATION&#x27;, &#x27;MARRIAGE&#x27;,\n",
       "                               &#x27;AGE&#x27;, &#x27;PAY_0&#x27;, &#x27;PAY_2&#x27;, &#x27;PAY_3&#x27;, &#x27;PAY_4&#x27;,\n",
       "                               &#x27;PAY_5&#x27;, &#x27;PAY_6&#x27;, &#x27;BILL_AMT1&#x27;, &#x27;BILL_AMT2&#x27;,\n",
       "                               &#x27;BILL_AMT3&#x27;, &#x27;BILL_AMT4&#x27;, &#x27;BILL_AMT5&#x27;,\n",
       "                               &#x27;BILL_AMT6&#x27;, &#x27;PAY_AMT1&#x27;, &#x27;PAY_AMT2&#x27;, &#x27;PAY_AMT3&#x27;,\n",
       "                               &#x27;PAY_AMT4&#x27;, &#x27;PAY_AMT5&#x27;, &#x27;PAY_AMT6&#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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BinningProcess</label><div class=\"sk-toggleable__content\"><pre>BinningProcess(categorical_variables=[], max_n_bins=7, min_prebin_size=0.1,\n",
       "               variable_names=[&#x27;LIMIT_BAL&#x27;, &#x27;SEX&#x27;, &#x27;EDUCATION&#x27;, &#x27;MARRIAGE&#x27;,\n",
       "                               &#x27;AGE&#x27;, &#x27;PAY_0&#x27;, &#x27;PAY_2&#x27;, &#x27;PAY_3&#x27;, &#x27;PAY_4&#x27;,\n",
       "                               &#x27;PAY_5&#x27;, &#x27;PAY_6&#x27;, &#x27;BILL_AMT1&#x27;, &#x27;BILL_AMT2&#x27;,\n",
       "                               &#x27;BILL_AMT3&#x27;, &#x27;BILL_AMT4&#x27;, &#x27;BILL_AMT5&#x27;,\n",
       "                               &#x27;BILL_AMT6&#x27;, &#x27;PAY_AMT1&#x27;, &#x27;PAY_AMT2&#x27;, &#x27;PAY_AMT3&#x27;,\n",
       "                               &#x27;PAY_AMT4&#x27;, &#x27;PAY_AMT5&#x27;, &#x27;PAY_AMT6&#x27;])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "BinningProcess(categorical_variables=[], max_n_bins=7, min_prebin_size=0.1,\n",
       "               variable_names=['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE',\n",
       "                               'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4',\n",
       "                               'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
       "                               'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5',\n",
       "                               'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n",
       "                               'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binning_process.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>BinningProcess(categorical_variables=[], max_n_bins=5,\n",
       "               variable_names=[&#x27;LIMIT_BAL&#x27;, &#x27;SEX&#x27;, &#x27;EDUCATION&#x27;, &#x27;MARRIAGE&#x27;,\n",
       "                               &#x27;AGE&#x27;, &#x27;PAY_0&#x27;, &#x27;PAY_2&#x27;, &#x27;PAY_3&#x27;, &#x27;PAY_4&#x27;,\n",
       "                               &#x27;PAY_5&#x27;, &#x27;PAY_6&#x27;, &#x27;BILL_AMT1&#x27;, &#x27;BILL_AMT2&#x27;,\n",
       "                               &#x27;BILL_AMT3&#x27;, &#x27;BILL_AMT4&#x27;, &#x27;BILL_AMT5&#x27;,\n",
       "                               &#x27;BILL_AMT6&#x27;, &#x27;PAY_AMT1&#x27;, &#x27;PAY_AMT2&#x27;, &#x27;PAY_AMT3&#x27;,\n",
       "                               &#x27;PAY_AMT4&#x27;, &#x27;PAY_AMT5&#x27;, &#x27;PAY_AMT6&#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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BinningProcess</label><div class=\"sk-toggleable__content\"><pre>BinningProcess(categorical_variables=[], max_n_bins=5,\n",
       "               variable_names=[&#x27;LIMIT_BAL&#x27;, &#x27;SEX&#x27;, &#x27;EDUCATION&#x27;, &#x27;MARRIAGE&#x27;,\n",
       "                               &#x27;AGE&#x27;, &#x27;PAY_0&#x27;, &#x27;PAY_2&#x27;, &#x27;PAY_3&#x27;, &#x27;PAY_4&#x27;,\n",
       "                               &#x27;PAY_5&#x27;, &#x27;PAY_6&#x27;, &#x27;BILL_AMT1&#x27;, &#x27;BILL_AMT2&#x27;,\n",
       "                               &#x27;BILL_AMT3&#x27;, &#x27;BILL_AMT4&#x27;, &#x27;BILL_AMT5&#x27;,\n",
       "                               &#x27;BILL_AMT6&#x27;, &#x27;PAY_AMT1&#x27;, &#x27;PAY_AMT2&#x27;, &#x27;PAY_AMT3&#x27;,\n",
       "                               &#x27;PAY_AMT4&#x27;, &#x27;PAY_AMT5&#x27;, &#x27;PAY_AMT6&#x27;])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "BinningProcess(categorical_variables=[], max_n_bins=5,\n",
       "               variable_names=['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE',\n",
       "                               'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4',\n",
       "                               'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
       "                               'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5',\n",
       "                               'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n",
       "                               'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BinningProcess(categorical_variables=categorical_variables,\n",
    "               max_n_bins=5,\n",
    "               variable_names=variable_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>5</th>\n",
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       "      <td>0.881628</td>\n",
       "      <td>0.098239</td>\n",
       "      <td>0.424856</td>\n",
       "      <td>0.240999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PAY_2</td>\n",
       "      <td>numerical</td>\n",
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       "      <th>7</th>\n",
       "      <td>PAY_3</td>\n",
       "      <td>numerical</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.371824</td>\n",
       "      <td>0.043483</td>\n",
       "      <td>0.241605</td>\n",
       "      <td>0.7505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PAY_6</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>0.286857</td>\n",
       "      <td>0.033743</td>\n",
       "      <td>0.207634</td>\n",
       "      <td>0.698827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PAY_AMT1</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>0.185078</td>\n",
       "      <td>0.022713</td>\n",
       "      <td>0.226419</td>\n",
       "      <td>0.597434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>7</td>\n",
       "      <td>0.183408</td>\n",
       "      <td>0.02256</td>\n",
       "      <td>0.237511</td>\n",
       "      <td>0.453389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PAY_AMT2</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>0.162522</td>\n",
       "      <td>0.019912</td>\n",
       "      <td>0.211861</td>\n",
       "      <td>0.560015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>PAY_AMT3</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>0.133184</td>\n",
       "      <td>0.016427</td>\n",
       "      <td>0.198629</td>\n",
       "      <td>0.4355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PAY_AMT4</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>0.106919</td>\n",
       "      <td>0.013242</td>\n",
       "      <td>0.181324</td>\n",
       "      <td>0.356566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PAY_AMT6</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>0.098568</td>\n",
       "      <td>0.012213</td>\n",
       "      <td>0.172745</td>\n",
       "      <td>0.270371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PAY_AMT5</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>0.088801</td>\n",
       "      <td>0.010998</td>\n",
       "      <td>0.163419</td>\n",
       "      <td>0.231437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PAY_5</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>0.038219</td>\n",
       "      <td>0.004757</td>\n",
       "      <td>0.090776</td>\n",
       "      <td>0.099481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGE</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>7</td>\n",
       "      <td>0.023876</td>\n",
       "      <td>0.002979</td>\n",
       "      <td>0.085762</td>\n",
       "      <td>0.010352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>0.019322</td>\n",
       "      <td>0.002412</td>\n",
       "      <td>0.067231</td>\n",
       "      <td>0.021217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BILL_AMT6</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0147</td>\n",
       "      <td>0.00183</td>\n",
       "      <td>0.046021</td>\n",
       "      <td>0.040887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BILL_AMT5</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>0.012288</td>\n",
       "      <td>0.001533</td>\n",
       "      <td>0.051977</td>\n",
       "      <td>0.035236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BILL_AMT4</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>0.011701</td>\n",
       "      <td>0.00146</td>\n",
       "      <td>0.04815</td>\n",
       "      <td>0.021526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SEX</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>0.010759</td>\n",
       "      <td>0.001344</td>\n",
       "      <td>0.050955</td>\n",
       "      <td>0.02853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BILL_AMT1</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00804</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>0.04849</td>\n",
       "      <td>0.008312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BILL_AMT3</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>0.007542</td>\n",
       "      <td>0.000942</td>\n",
       "      <td>0.036811</td>\n",
       "      <td>0.019883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BILL_AMT2</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>0.006184</td>\n",
       "      <td>0.000772</td>\n",
       "      <td>0.038635</td>\n",
       "      <td>0.012493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MARRIAGE</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>0.004713</td>\n",
       "      <td>0.000589</td>\n",
       "      <td>0.03425</td>\n",
       "      <td>0.013001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name      dtype   status  selected n_bins        iv        js  \\\n",
       "5       PAY_0  numerical  OPTIMAL      True      4  0.881628  0.098239   \n",
       "6       PAY_2  numerical  OPTIMAL      True      4  0.574944  0.066814   \n",
       "7       PAY_3  numerical  OPTIMAL      True      4  0.440111  0.051683   \n",
       "8       PAY_4  numerical  OPTIMAL      True      4  0.371824  0.043483   \n",
       "10      PAY_6  numerical  OPTIMAL      True      4  0.286857  0.033743   \n",
       "17   PAY_AMT1  numerical  OPTIMAL      True      5  0.185078  0.022713   \n",
       "0   LIMIT_BAL  numerical  OPTIMAL      True      7  0.183408   0.02256   \n",
       "18   PAY_AMT2  numerical  OPTIMAL      True      5  0.162522  0.019912   \n",
       "19   PAY_AMT3  numerical  OPTIMAL      True      6  0.133184  0.016427   \n",
       "20   PAY_AMT4  numerical  OPTIMAL      True      6  0.106919  0.013242   \n",
       "22   PAY_AMT6  numerical  OPTIMAL      True      6  0.098568  0.012213   \n",
       "21   PAY_AMT5  numerical  OPTIMAL      True      6  0.088801  0.010998   \n",
       "9       PAY_5  numerical  OPTIMAL      True      3  0.038219  0.004757   \n",
       "4         AGE  numerical  OPTIMAL      True      7  0.023876  0.002979   \n",
       "2   EDUCATION  numerical  OPTIMAL      True      3  0.019322  0.002412   \n",
       "16  BILL_AMT6  numerical  OPTIMAL      True      4    0.0147   0.00183   \n",
       "15  BILL_AMT5  numerical  OPTIMAL      True      4  0.012288  0.001533   \n",
       "14  BILL_AMT4  numerical  OPTIMAL      True      4  0.011701   0.00146   \n",
       "1         SEX  numerical  OPTIMAL      True      2  0.010759  0.001344   \n",
       "11  BILL_AMT1  numerical  OPTIMAL      True      5   0.00804  0.001004   \n",
       "13  BILL_AMT3  numerical  OPTIMAL      True      3  0.007542  0.000942   \n",
       "12  BILL_AMT2  numerical  OPTIMAL      True      4  0.006184  0.000772   \n",
       "3    MARRIAGE  numerical  OPTIMAL      True      2  0.004713  0.000589   \n",
       "\n",
       "        gini quality_score  \n",
       "5   0.424856      0.240999  \n",
       "6   0.312897       0.07106  \n",
       "7    0.27603      0.764083  \n",
       "8   0.241605        0.7505  \n",
       "10  0.207634      0.698827  \n",
       "17  0.226419      0.597434  \n",
       "0   0.237511      0.453389  \n",
       "18  0.211861      0.560015  \n",
       "19  0.198629        0.4355  \n",
       "20  0.181324      0.356566  \n",
       "22  0.172745      0.270371  \n",
       "21  0.163419      0.231437  \n",
       "9   0.090776      0.099481  \n",
       "4   0.085762      0.010352  \n",
       "2   0.067231      0.021217  \n",
       "16  0.046021      0.040887  \n",
       "15  0.051977      0.035236  \n",
       "14   0.04815      0.021526  \n",
       "1   0.050955       0.02853  \n",
       "11   0.04849      0.008312  \n",
       "13  0.036811      0.019883  \n",
       "12  0.038635      0.012493  \n",
       "3    0.03425      0.013001  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iv = binning_process.summary().sort_values(by=['iv'], ascending=False)\n",
    "df_iv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criar uma lista com o nome das variáveis a serem removidas\n",
    "cols_to_drop = df_iv.loc[df_iv['iv'] < 0.02]['name'].tolist()\n",
    "\n",
    "# Dropar as colunas do DataFrame\n",
    "X_train = X_train.drop(cols_to_drop, axis=1)\n",
    "X_test = X_test.drop(cols_to_drop, axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Train Model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construir o modelo\n",
    "model = LogisticRegression()\n",
    "\n",
    "# Treinar o modelo\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia: 0.7822222222222223\n",
      "Precisão: 0.0\n",
      "Recall: 0.0\n",
      "F1-score: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\MateusDeNadaiLofrano\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fazer as previsões nos dados de teste\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Calcular as métricas\n",
    "print('Acurácia:', accuracy_score(y_test, y_pred))\n",
    "print('Precisão:', precision_score(y_test, y_pred))\n",
    "print('Recall:', recall_score(y_test, y_pred))\n",
    "print('F1-score:', f1_score(y_test, y_pred))\n",
    "\n",
    "# Calcular a curva ROC e a AUC\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "# Plotar a curva ROC\n",
    "\n",
    "plt.plot(fpr, tpr, label='AUC = %.2f' % auc)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', color='gray')\n",
    "plt.xlabel('Taxa de Falsos Positivos')\n",
    "plt.ylabel('Taxa de Verdadeiros Positivos')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7040,    0],\n",
       "       [1960,    0]], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Matriz de confusão\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Construir o modelo\n",
    "rf_model = RandomForestClassifier()\n",
    "\n",
    "# Treinar o modelo\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "# Fazer as previsões nos dados de teste\n",
    "rf_y_pred = rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest:\n",
      "Acurácia: 0.8136666666666666\n",
      "Precisão: 0.6206308610400681\n",
      "Recall: 0.37142857142857144\n",
      "F1-score: 0.4647302904564315\n"
     ]
    }
   ],
   "source": [
    "# Calcular as métricas\n",
    "\n",
    "# Calcular a curva ROC e a AUC\n",
    "fpr, tpr, thresholds = roc_curve(y_test, rf_y_pred)\n",
    "auc = roc_auc_score(y_test, rf_y_pred)\n",
    "\n",
    "# Plotar a curva ROC\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(fpr, tpr, label='AUC = %.2f' % auc)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', color='gray')\n",
    "plt.xlabel('Taxa de Falsos Positivos')\n",
    "plt.ylabel('Taxa de Verdadeiros Positivos')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "print('Random Forest:')\n",
    "print('Acurácia:', accuracy_score(y_test, rf_y_pred))\n",
    "print('Precisão:', precision_score(y_test, rf_y_pred))\n",
    "print('Recall:', recall_score(y_test, rf_y_pred))\n",
    "print('F1-score:', f1_score(y_test, rf_y_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "# Converter os dados para o formato xgb.DMatrix\n",
    "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
    "dtest = xgb.DMatrix(X_test, label=y_test)\n",
    "\n",
    "# Definir os parâmetros do modelo\n",
    "params = {\n",
    "    'objective': 'binary:logistic',  # classificação binária\n",
    "    'eval_metric': 'auc',\n",
    "    'eta': 0.1,\n",
    "    'max_depth': 6,\n",
    "    'min_child_weight': 1,\n",
    "    'subsample': 0.8,\n",
    "    'colsample_bytree': 0.8,\n",
    "    'scale_pos_weight': 1,\n",
    "    'seed': 42,\n",
    "    'nthread': -1\n",
    "}\n",
    "\n",
    "# Treinar o modelo\n",
    "xgb_model = xgb.train(params, dtrain, num_boost_round=100)\n",
    "\n",
    "# Fazer as previsões nos dados de teste\n",
    "xgb_y_pred = xgb_model.predict(dtest)\n",
    "\n",
    "# Arredondar as previsões para 0 ou 1\n",
    "xgb_y_pred = np.round(xgb_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost:\n",
      "Acurácia: 0.8186666666666667\n",
      "Precisão: 0.6488203266787659\n",
      "Recall: 0.3647959183673469\n",
      "F1-score: 0.4670150228608752\n"
     ]
    }
   ],
   "source": [
    "# Calcular as métricas\n",
    "\n",
    "# Calcular a curva ROC e a AUC\n",
    "fpr, tpr, thresholds = roc_curve(y_test, xgb_y_pred)\n",
    "auc = roc_auc_score(y_test, xgb_y_pred)\n",
    "\n",
    "# Plotar a curva ROC\n",
    "\n",
    "plt.plot(fpr, tpr, label='AUC = %.2f' % auc)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', color='gray')\n",
    "plt.xlabel('Taxa de Falsos Positivos')\n",
    "plt.ylabel('Taxa de Verdadeiros Positivos')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "print('XGBoost:')\n",
    "print('Acurácia:', accuracy_score(y_test, xgb_y_pred))\n",
    "print('Precisão:', precision_score(y_test, xgb_y_pred))\n",
    "print('Recall:', recall_score(y_test, xgb_y_pred))\n",
    "print('F1-score:', f1_score(y_test, xgb_y_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Avaliação de Desempenho do Modelo"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     \n",
    "     Ao comparar as métricas de precisão, recall e F1-score, é possível observar que o modelo de XGBoost obteve melhores resultados do que o modelo de Random Forest em todas as três métricas. Isso sugere que o modelo de XGBoost tem um desempenho melhor em detectar corretamente os casos positivos (por exemplo, clientes que realmente irão inadimplir) e minimizar os casos falsos positivos (clientes que são classificados como inadimplentes erroneamente).\n",
    "\n",
    "     O valor AUC varia de 0 a 1, sendo 1 o melhor valor possível, indicando um modelo perfeito. Portanto, um valor AUC de 0.65 indica que o modelo tem uma capacidade razoável de classificar corretamente as amostras, que unido às demais métricas consideramos um potencial preditivo aceitável.\n",
    "\n",
    "     Após isso calculamos os valores de VIF para verificar se existem problemas de multicolineariedade. Consideramos como problemáticos valores superiores a 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "# Calcular VIF\n",
    "def calc_vif(X):\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"variables\"] = X.columns\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "    return vif\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variável LIMIT_BAL, VIF: 3.3377568107817934\n",
      "Variável AGE, VIF: 2.7742362487704884\n",
      "Variável PAY_0, VIF: 1.9097323401108852\n",
      "Variável PAY_2, VIF: 3.1571455464452964\n",
      "Variável PAY_3, VIF: 3.7274636796401692\n",
      "Variável PAY_4, VIF: 4.436454434415598\n",
      "Variável PAY_5, VIF: 4.971259844195148\n",
      "Variável PAY_6, VIF: 3.3472939930739454\n",
      "Variável PAY_AMT2, VIF: 1.199713049629571\n",
      "Variável PAY_AMT3, VIF: 1.264167657867389\n",
      "Variável PAY_AMT4, VIF: 1.2465957445043443\n",
      "Variável PAY_AMT5, VIF: 1.2049173894697152\n",
      "Variável PAY_AMT6, VIF: 1.190439621562204\n"
     ]
    }
   ],
   "source": [
    "# VIF Referente às explicativas XGBoost\n",
    "for col in X_train.columns:\n",
    "    if col != 'PAY_AMT1':\n",
    "        X_temp = X_train.drop(['PAY_AMT1'], axis=1)\n",
    "        vif = calc_vif(X_temp)\n",
    "        print(f'Variável {col}, VIF: {vif.loc[vif[\"variables\"]==col, \"VIF\"].values[0]}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     \n",
    "     Todas as variáveis explicativas estão com valores de VIF abaixo de 5, o que sugere que não há um alto grau de correlação entre elas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Criar matriz de correlação\n",
    "corr_matrix = X_train.corr()\n",
    "\n",
    "# Gerar heatmap com a matriz de correlação\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr_matrix, cmap='coolwarm', center=0, square=True, annot=True, fmt='.2f')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Modelo Final - XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from scipy.stats import ks_2samp\n",
    "import shap"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     \n",
    "     Considerando os resultados anteriores, optamos por utilizar XGBoost como modelo preditivo final. Validamos em 5 folds, treinando o modelo 405 vezes para elencar os melhores parâmetros a serem utilizados na validação final."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n",
      "Melhores parâmetros: {'colsample_bytree': 1.0, 'max_depth': 4, 'min_child_weight': 5, 'subsample': 1.0}\n",
      "Melhor score AUC: 0.77934045137088\n"
     ]
    }
   ],
   "source": [
    "# Convertendo os dados para o formato xgb.DMatrix\n",
    "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
    "dtest = xgb.DMatrix(X_test, label=y_test)\n",
    "\n",
    "# Definindo os parâmetros do modelo\n",
    "params = {\n",
    "    'objective': 'binary:logistic',  # classificação binária\n",
    "    'eval_metric': 'auc',\n",
    "    'eta': 0.1,\n",
    "    'max_depth': 6,\n",
    "    'min_child_weight': 1,\n",
    "    'subsample': 0.8,\n",
    "    'colsample_bytree': 0.8,\n",
    "    'scale_pos_weight': 1,\n",
    "    'nthread': -1\n",
    "}\n",
    "\n",
    "# Realizando a validação cruzada para ajustar os hiperparâmetros\n",
    "cv_params = {\n",
    "    'max_depth': [4, 6, 8],\n",
    "    'min_child_weight': [1, 3, 5],\n",
    "    'subsample': [0.6, 0.8, 1.0],\n",
    "    'colsample_bytree': [0.6, 0.8, 1.0]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=xgb.XGBClassifier(**params, random_state=42),\n",
    "    param_grid=cv_params,\n",
    "    scoring='roc_auc',\n",
    "    cv=5,\n",
    "    verbose=3,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print('Melhores parâmetros:', grid_search.best_params_)\n",
    "print('Melhor score AUC:', grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Atualizar parmâmetros com os melhores parâmetros na grade\n",
    "params.update(grid_search.best_params_)\n",
    "\n",
    "# Treinando o modelo com os melhores parâmetros\n",
    "xgb_model = xgb.XGBClassifier(**params, random_state=42, n_jobs=-1)\n",
    "xgb_model.fit(X_train, y_train)\n",
    "\n",
    "# Fazendo as previsões nos dados de teste\n",
    "xgb_y_pred = xgb_model.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost:\n",
      "AUC: 0.7710638914656771\n",
      "Precisão-Recall AUC: 0.533354407265482\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve, auc\n",
    "\n",
    "# Calculando as métricas\n",
    "print('XGBoost:')\n",
    "print('AUC:', roc_auc_score(y_test, xgb_y_pred))\n",
    "precision, recall, _ = precision_recall_curve(y_test, xgb_y_pred)\n",
    "print('Precisão-Recall AUC:', auc(recall, precision))\n",
    "\n",
    "# Plotar ROC e AUC\n",
    "fpr, tpr, _ = roc_curve(y_test, xgb_y_pred)\n",
    "roc_auc = roc_auc_score(y_test, xgb_y_pred)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label='Curva ROC (AUC = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('Taxa de Falsos Positivos')\n",
    "plt.ylabel('Taxa de Verdadeiros Positivos')\n",
    "plt.title('Curva ROC')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Treinar o modelo com todos os dados de treino\n",
    "final_model_all = xgb.train(params, dtrain, num_boost_round=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x511.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predições do modelo por valores SHAP\n",
    "explainer = shap.Explainer(final_model_all)\n",
    "shap_values = explainer(X_test)\n",
    "\n",
    "# Plotar SHAP\n",
    "shap.summary_plot(shap_values, X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     \n",
    "     Os valores SHAP demonstram a probabilidade de classificação do modelo através da consideração das variáveis.\n",
    "\n",
    "     Distribuimos a probabilidade de default para averiguar a classificação do modelo e calculamos KS2 para verificar se o ordenamento é coerente com o esperado em um sistema de classificação de crédito.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribuição das Probabilidades\n",
    "plt.hist(xgb_y_pred, bins=20, density=True, alpha=0.6, color='b')\n",
    "plt.xlabel('Probabilidade de Default')\n",
    "plt.ylabel('Frequência')\n",
    "plt.title('Distribuição das Probabilidades de Default')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS2: 0.7822222222222223\n"
     ]
    }
   ],
   "source": [
    "# Calcular KS2\n",
    "ks_stat, ks_p_value = ks_2samp(y_test, xgb_y_pred)\n",
    "print('KS2:', ks_stat)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     \n",
    "     Transformamos a classificação do modelo em um score de 300 até 1000 (similar as classificações de mercado), e por fim criamos faixas desse score para substituir a máscara de classificação do modelo, tendo por fim um output que tenha entendimento por parte das áreas do negócio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[895.98206 945.7752  912.50525 ... 923.7758  945.4711  955.95636]\n"
     ]
    }
   ],
   "source": [
    "# Transformar Classificação em um SCORE de 300 a 1000\n",
    "\n",
    "def default_prob_to_score(prob):\n",
    "    score = 1000 - (prob * 700)\n",
    "    return score\n",
    "\n",
    "\n",
    "score_predictions = default_prob_to_score(xgb_y_pred)\n",
    "\n",
    "# Para visualizar as previsões de score\n",
    "print(score_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criar faixas de SCORE Crescente\n",
    "bins = [300, 400, 500, 600, 700, 1000]\n",
    "labels = ['Muito Alto Risco', 'Alto Risco', 'Médio Risco', 'Baixo Risco', 'Muito Baixo Risco']\n",
    "\n",
    "# Categorizar score com faixa específica\n",
    "score_categories = pd.cut(score_predictions, bins=bins, labels=labels)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exportação para Pickle (.pkl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Salvar classificação em pickle\n",
    "client_classification = pd.DataFrame({'ID': X_test.index, 'Score': score_predictions, 'Risco': score_categories})\n",
    "\n",
    "with open('client_classification.pickle', 'wb') as f:\n",
    "    pickle.dump(client_classification, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Salvar modelo final e resultados em arquivo pickle\n",
    "xgb_model.save_model('xgb_model.bin')\n",
    "\n",
    "model_results = {'AUC': roc_auc_score(y_test, xgb_y_pred),\n",
    "                 'Precision-Recall AUC': auc(recall, precision),\n",
    "                 'KS2': ks_stat,\n",
    "                 'Score Bins': bins,\n",
    "                 'Score Labels': labels,\n",
    "                  'Model Params': params\n",
    "}\n",
    "\n",
    "with open('model_results.pickle', 'wb') as f:\n",
    "    pickle.dump(model_results, f)"
   ]
  }
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