Churn_TeoMeWhy_MeD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "9eb12ab1-f0d2-493a-8cd0-ff5f4b665c2f",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "# Objetivo da análise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "854dbee1-8575-4083-98fe-a8c55b8ee8fa",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "O objetivo deste projeto é analisar o perfil dos assinantes de realizam do canal educativo Teo Me Why na Twitch e treinar um modelo de machine learning que estime a probabilidade de churn dos inscritos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "04bc04e3-6efa-4e74-8ae1-326f93b2cf3a",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "##Preparando o ambiente\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "9aaa9186-68b0-49a1-a666-ab0cdcc167af",
     "showTitle": true,
     "title": "Instalar pacotes necessários"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\nRequirement already satisfied: scikit-learn in /databricks/python3/lib/python3.10/site-packages (1.1.1)\nRequirement already satisfied: joblib>=1.0.0 in /databricks/python3/lib/python3.10/site-packages (from scikit-learn) (1.2.0)\nRequirement already satisfied: numpy>=1.17.3 in /databricks/python3/lib/python3.10/site-packages (from scikit-learn) (1.23.5)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /databricks/python3/lib/python3.10/site-packages (from scikit-learn) (2.2.0)\nRequirement already satisfied: scipy>=1.3.2 in /databricks/python3/lib/python3.10/site-packages (from scikit-learn) (1.10.0)\n\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\n\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\nRequirement already satisfied: matplotlib in /databricks/python3/lib/python3.10/site-packages (3.7.0)\nRequirement already satisfied: scikit-learn in /databricks/python3/lib/python3.10/site-packages (1.1.1)\nCollecting graphviz\n  Downloading graphviz-0.20.3-py3-none-any.whl (47 kB)\n     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 47.1/47.1 kB 1.7 MB/s eta 0:00:00\nRequirement already satisfied: pyparsing>=2.3.1 in /databricks/python3/lib/python3.10/site-packages (from matplotlib) (3.0.9)\nRequirement already satisfied: packaging>=20.0 in /databricks/python3/lib/python3.10/site-packages (from matplotlib) (23.2)\nRequirement already satisfied: kiwisolver>=1.0.1 in /databricks/python3/lib/python3.10/site-packages (from matplotlib) (1.4.4)\nRequirement already satisfied: cycler>=0.10 in /databricks/python3/lib/python3.10/site-packages (from matplotlib) (0.11.0)\nRequirement already satisfied: numpy>=1.20 in /databricks/python3/lib/python3.10/site-packages (from matplotlib) (1.23.5)\nRequirement already satisfied: pillow>=6.2.0 in /databricks/python3/lib/python3.10/site-packages (from matplotlib) (9.4.0)\nRequirement already satisfied: python-dateutil>=2.7 in /databricks/python3/lib/python3.10/site-packages (from matplotlib) (2.8.2)\nRequirement already satisfied: contourpy>=1.0.1 in /databricks/python3/lib/python3.10/site-packages (from matplotlib) (1.0.5)\nRequirement already satisfied: fonttools>=4.22.0 in /databricks/python3/lib/python3.10/site-packages (from matplotlib) (4.25.0)\nRequirement already satisfied: joblib>=1.0.0 in /databricks/python3/lib/python3.10/site-packages (from scikit-learn) (1.2.0)\nRequirement already satisfied: scipy>=1.3.2 in /databricks/python3/lib/python3.10/site-packages (from scikit-learn) (1.10.0)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /databricks/python3/lib/python3.10/site-packages (from scikit-learn) (2.2.0)\nRequirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\nInstalling collected packages: graphviz\nSuccessfully installed graphviz-0.20.3\n\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\n\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\nRequirement already satisfied: plotly in /databricks/python3/lib/python3.10/site-packages (5.9.0)\nRequirement already satisfied: tenacity>=6.2.0 in /databricks/python3/lib/python3.10/site-packages (from plotly) (8.1.0)\n\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\n\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\nRequirement already satisfied: factor_analyzer in /local_disk0/.ephemeral_nfs/envs/pythonEnv-5a9e4130-8e76-478c-b09a-018d2ebc2a58/lib/python3.10/site-packages (0.5.1)\nRequirement already satisfied: pandas in /databricks/python3/lib/python3.10/site-packages (from factor_analyzer) (1.5.3)\nRequirement already satisfied: numpy in /databricks/python3/lib/python3.10/site-packages (from factor_analyzer) (1.23.5)\nRequirement already satisfied: scipy in /databricks/python3/lib/python3.10/site-packages (from factor_analyzer) (1.10.0)\nRequirement already satisfied: scikit-learn in /databricks/python3/lib/python3.10/site-packages (from factor_analyzer) (1.1.1)\nRequirement already satisfied: pytz>=2020.1 in /databricks/python3/lib/python3.10/site-packages (from pandas->factor_analyzer) (2022.7)\nRequirement already satisfied: python-dateutil>=2.8.1 in /databricks/python3/lib/python3.10/site-packages (from pandas->factor_analyzer) (2.8.2)\nRequirement already satisfied: joblib>=1.0.0 in /databricks/python3/lib/python3.10/site-packages (from scikit-learn->factor_analyzer) (1.2.0)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /databricks/python3/lib/python3.10/site-packages (from scikit-learn->factor_analyzer) (2.2.0)\nRequirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.1->pandas->factor_analyzer) (1.16.0)\n\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\n\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\nRequirement already satisfied: pandas in /databricks/python3/lib/python3.10/site-packages (1.5.3)\nRequirement already satisfied: python-dateutil>=2.8.1 in /databricks/python3/lib/python3.10/site-packages (from pandas) (2.8.2)\nRequirement already satisfied: numpy>=1.21.0 in /databricks/python3/lib/python3.10/site-packages (from pandas) (1.23.5)\nRequirement already satisfied: pytz>=2020.1 in /databricks/python3/lib/python3.10/site-packages (from pandas) (2022.7)\nRequirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn\n",
    "!pip install matplotlib scikit-learn graphviz\n",
    "!pip install plotly\n",
    "!pip install factor_analyzer\n",
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "374a3afb-ab88-4802-a7f3-f460e998cf86",
     "showTitle": true,
     "title": "Carregar pacotes e bibliotecas"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib  import pyplot as plt\n",
    "from sklearn import model_selection\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "import graphviz\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.decomposition  import PCA\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.impute import IterativeImputer\n",
    "import plotly.graph_objects as go\n",
    "import pandas as pd\n",
    "\n",
    "# setando gráficos para aparecerem no jupyter notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "b6d68ca8-6473-4ceb-9ac6-803bad9fcf8f",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "#Análise inicial dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "32735f16-ed17-4137-9962-d178844e6f63",
     "showTitle": true,
     "title": "Importar dados"
    }
   },
   "outputs": [],
   "source": [
    "# Importando a tabela que o Teo criou e transformando em um dataframe\n",
    "#df = spark.table(\"feature_store.med.abt_churn\").toPandas()\n",
    "df = spark.table(\"sandbox.med.Teo_abt_churn\").toPandas()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "collapsed": true,
     "inputWidgets": {},
     "nuid": "466719f3-4362-4fea-980a-b875e8765f19",
     "showTitle": true,
     "title": "Visualizar um amostra dos dados"
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "      <th>dtRef</th>\n",
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      ],
      "text/plain": [
       "        dtRef  ... nrAvgRecorrencia\n",
       "0  2024-06-01  ...             12.0\n",
       "1  2024-08-01  ...              NaN\n",
       "2  2024-07-01  ...              NaN\n",
       "3  2024-02-01  ...              NaN\n",
       "4  2024-08-01  ...              2.0\n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "collapsed": true,
     "inputWidgets": {},
     "nuid": "6265a805-5b61-4259-87cc-a5bfa496db14",
     "showTitle": true,
     "title": "Contar valores nulos"
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "dtRef                    0\n",
       "idCliente                0\n",
       "flChurn                  0\n",
       "nrSomaPontos             0\n",
       "nrSomaPontosPos          0\n",
       "nrSomaPontosNeg          0\n",
       "nrTicketMedio            0\n",
       "nrTicketMedioPos         0\n",
       "nrTicketMedioNeg         0\n",
       "nrPontosDia              0\n",
       "nrQtdeTransacoes         0\n",
       "nrQtdeDias               0\n",
       "nrRecenciaDias           0\n",
       "nrQtdeTransacaoDay2      0\n",
       "nrQtdeTransacaoDay3      0\n",
       "nrQtdeTransacaoDay4      0\n",
       "nrQtdeTransacaoDay5      0\n",
       "nrQtdeTransacaoDay6      0\n",
       "nrQtdeDay2               0\n",
       "nrQtdeDay3               0\n",
       "nrQtdeDay4               0\n",
       "nrQtdeDay5               0\n",
       "nrQtdeDay6               0\n",
       "nrAvgRecorrencia       706\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "collapsed": true,
     "inputWidgets": {},
     "nuid": "42c31c36-1de7-4cba-a15e-242d535e101a",
     "showTitle": true,
     "title": "Visualizar informações sobre as variáveis"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1540 entries, 0 to 1539\nData columns (total 24 columns):\n #   Column               Non-Null Count  Dtype  \n---  ------               --------------  -----  \n 0   dtRef                1540 non-null   object \n 1   idCliente            1540 non-null   object \n 2   flChurn              1540 non-null   int32  \n 3   nrSomaPontos         1540 non-null   int64  \n 4   nrSomaPontosPos      1540 non-null   int64  \n 5   nrSomaPontosNeg      1540 non-null   int64  \n 6   nrTicketMedio        1540 non-null   float64\n 7   nrTicketMedioPos     1540 non-null   float64\n 8   nrTicketMedioNeg     1540 non-null   float64\n 9   nrPontosDia          1540 non-null   float64\n 10  nrQtdeTransacoes     1540 non-null   int64  \n 11  nrQtdeDias           1540 non-null   int64  \n 12  nrRecenciaDias       1540 non-null   int32  \n 13  nrQtdeTransacaoDay2  1540 non-null   int64  \n 14  nrQtdeTransacaoDay3  1540 non-null   int64  \n 15  nrQtdeTransacaoDay4  1540 non-null   int64  \n 16  nrQtdeTransacaoDay5  1540 non-null   int64  \n 17  nrQtdeTransacaoDay6  1540 non-null   int64  \n 18  nrQtdeDay2           1540 non-null   int64  \n 19  nrQtdeDay3           1540 non-null   int64  \n 20  nrQtdeDay4           1540 non-null   int64  \n 21  nrQtdeDay5           1540 non-null   int64  \n 22  nrQtdeDay6           1540 non-null   int64  \n 23  nrAvgRecorrencia     834 non-null    float64\ndtypes: float64(5), int32(2), int64(15), object(2)\nmemory usage: 276.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "909877f3-f32c-4d11-ba6c-7aae15924680",
     "showTitle": true,
     "title": "Visualizar número de assinantes ativos e desistentes"
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Número de contas ativas e inativas entre fevereiro e agosto de 2024')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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f+Y0AAhCrVq1S6nPJkiUCgNi0aZOi7OXLl+Kzzz4TFhYWIjU1Vf2OE0Xf95mZmSrXKd65c0dIpVKlz4fFixcLAGLXrl2KsoyMDFGtWjWl7Xz58qUoV66cqFWrlsjIyFC03bt3rwAgpk2bJoTQ/JjP71gMCgpSGRl7/vy5cHFxEc7OzgVeeyn/3hg2bJhSed++fVU+P4v6ufWmrKwsUaNGDeHi4qJ0rXbHjh1F5cqVVdq/ePFCABCTJk0qsN82bdoIS0tLkZSUpFT+zz//CENDQ3HgwAEhhCjSCKDe3AX8usqVK+Orr77CmjVrkJCQoLN+Bw0apPi/oaEhGjZsCCEEAgICFOXW1tb45JNP8N9//ynKIiIiUL16dVSrVg2JiYmKR6tWrQBA5S8yLy8vpWsSc3NzcfDgQXTr1g2VK1dWlNvb26Nv3744efIkUlNT8417//79MDIywtChQ5XiHzlypFK7Z8+e4ejRo+jVqxeeP3+uiPPp06fw9vbGzZs31d5hLbdjxw5IJBKVv+oBKEaj9u/fDwAYO3asUn1wcDAAYN++fUrlNWrUQPPmzRXPbW1tVfZvQWQymeL/8m1q3rw50tPTce3aNQBQjPAdOHAA6enpGvWrrv8XL14gMTERTZo0gRAC0dHRhepLrnfv3nj8+LHSdBTbt29HXl4eevfurXbdmZmZSExMxKeffgoAuHDhgqLO2toaZ8+exYMHDzSOQQiBHTt2oHPnzhBCKB233t7eSElJUVpHYQQGBiqOBwBo3rw5cnNzcffuXbXblt/rVli9e/fG33//jdu3byvKtm3bBqlUiq5du6pdd36vqUwmg4mJCaKiopCUlKRxDNq8xzTZX28zePBgGBoaKp4fOnQIycnJ8PX1VXptDQ0N4eHhoXaU6G0Kc8x0794dRkZGSqO+ly5dwpUrV5SO8YiICDRv3hylS5dW6q9NmzbIzc3F8ePHlWLo0aMHbG1tFc+L+pkGvLqWdcCAAUpl+/fvh52dHXx9fRVlxsbGGDVqFNLS0nDs2LF8+yvqvpdKpTAwePXVnpubi6dPn8LCwgKffPKJ0nvyf//7HypUqIAuXbooykxNTTF48GCl/s6fP4/Hjx9j2LBhStdAduzYEdWqVVN8Jmt7zMvt378fjRs3VhoVs7CwQGBgIGJjY3HlypUClwWAUaNGKZW/OZpXHJ9bI0aMwJUrV7B8+XIYGf3fSdWMjAy11znL92FGRka+fc6ZMweHDx/GvHnzVK6jHTVqFNq3b4+2bdsWKk519DIBBIApU6YgJycH8+bN01mflSpVUnpuZWUFU1NTlSFcKysrpTfIzZs3cfnyZdja2io93NzcAACPHz9WWt7FxUXp+ZMnT5Ceno5PPvlEJabq1asjLy8PcXFx+cZ99+5d2Nvbq5wee7O/W7duQQiBqVOnqsQqT+rejPV1t2/fhoODA8qUKVNgLAYGBooLaeXs7OxgbW2t8qX25j4HgNKlS2v8AXT58mV88cUXsLKygqWlJWxtbdGvXz8AQEpKCoBX+3vs2LH45ZdfYGNjA29vb/z000+K+oLcu3cP/v7+KFOmDCwsLGBra6s4davJ8uq0a9cOVlZWSl+O27Ztg7u7u+KYAV59uY0ePRrly5eHTCaDra2t4th5fd0LFizApUuX4OjoiMaNG2PGjBlvTaCfPHmC5ORkrFmzRuVYkH8ZFnQsFOTN11R+6vX111ST162wfHx8YGBgoNivQghERESgffv2sLS0VLTT5DWVSqWYP38+/vjjD5QvXx6enp5YsGCB4rROfrR5j2myv97mzc+UmzdvAgBatWqlEsfBgwe1em0Lc8zY2NigdevWCA8PVyy/bds2GBkZoXv37kpx/u9//1PpT37R/Ns+O4v6mQYAFSpUULmR5O7du6hataoiEZOTnw4sKDkv6r7Py8vDDz/8gKpVq0IqlcLGxga2trb4999/ld4bd+/ehaurq9IfDwBUPnvlsar7fqlWrZqiXttj/vX15Pcd9noc+S1rYGAAV1dXpfI3+9P159bChQvx888/49tvv0WHDh2U6mQyGbKyslSWkU/t8vofkq/btm0bpkyZgoCAAKVBGXndqVOnsHjxYo1jLIjeXQMoV7lyZfTr1w9r1qzBpEmTVOrffFPIqZvrR+71v6ALKgNefbnI5eXloXbt2vj+++/VtnV0dFR6nt+BU9zy8vIAAOPGjYO3t7faNm9+eGgrv/3/Jk32b36Sk5Ph5eUFS0tLzJo1C66urjA1NcWFCxcwceJExfYCwOLFi+Hv74/du3fj4MGDGDVqFObOnYszZ86gYsWKavvPzc3F559/jmfPnmHixImoVq0azM3NER8fD39/f6X+C0MqlaJbt27YuXMnVqxYgUePHuHPP//EnDlzlNr16tULp06dwvjx4+Hu7g4LCwvk5eWhXbt2Suvu1asXmjdvjp07d+LgwYNYuHAh5s+fj99++w3t27dXG4N8+X79+qm9VhQA6tSpo9X2ve01LczrVhgODg5o3rw5wsPDMXnyZJw5cwb37t3D/PnzFW0K85oGBQWhc+fO2LVrFw4cOICpU6di7ty5OHr0KOrVq6c2Bm3eY0V5D8i9+ZkijyMsLAx2dnYq7V8f6dBUYY+ZPn36YMCAAYiJiYG7uzvCw8PRunVrpT+o8/Ly8Pnnn2PChAlq+3v9DyIg/+0symearj+Pi7rv58yZg6lTp2LgwIH49ttvUaZMGRgYGCAoKEjr94amtDnm3yVdfm6FhoZi4sSJGDJkCKZMmaJSb29vj8jISAghlL7P5Gcd1d1/cOjQIfTv3x8dO3bEqlWrVOrldw+bmJgo5rVNTk4GAMTFxeHly5eFuq9BbxNA4NUo4KZNm5Q+4OXkf0XLd65cYU6raMrV1RX//PMPWrdurXHi8zpbW1uYmZnh+vXrKnXXrl2DgYGBShL5OicnJxw5cgRpaWlKo4Bv9ic/vWxsbKz2tvS3cXV1xYEDB/Ds2bN8RwGdnJyQl5eHmzdvKl08++jRIyQnJ8PJyanQ681vn0ZFReHp06f47bffFDfkAMCdO3fUtq9duzZq166NKVOm4NSpU2jatClWrVqF7777Tm37ixcv4saNG9iwYQP69++vKD906JDGMeand+/e2LBhA44cOYKrV69CCKF0aiwpKQlHjhzBzJkzMW3aNEW5fHThTfb29hg2bBiGDRuGx48fo379+pg9e3a+CaCtrS1KlSqF3NxcrY6FoijM66bNfh02bBiuX7+Obdu2wczMDJ07d1bUF+Y1BV4d88HBwQgODsbNmzfh7u6OxYsXY9OmTWrbF/U9lp/C7gf5SEq5cuV0Fkdhj5lu3brh66+/VozI3rhxAyEhISpxpqWlaR1jce1vJycn/Pvvv8jLy1MaBZRfnlDQ51hR9/327dvRsmVLrF27Vqk8OTlZKXl2cnLClStXVBKUW7duqWwL8Or7QH5Zktz169dVtuVtx3x+x6KTk1O+32Gvx5Hfsnl5ebh9+7bSqN+b/enqc2v37t0YNGgQunfvjp9++kltG3d3d/zyyy+4evWq0iVbZ8+eVdS/7uzZs/jiiy/QsGFDhIeHq0304+Li8Ouvv+LXX39Vqatfvz7q1q2LmJgYjbdDb08BA68O1H79+mH16tUqw9SWlpawsbFRuYZkxYoVOo+jV69eiI+Px88//6xSl5GRobgrLz+GhoZo27Ytdu/erfRrF48ePcKvv/6KZs2aKZ3CelOHDh2Qk5ODlStXKspyc3OxbNkypXblypVDixYtsHr1arXXTsrn/spPjx49IITAzJkzVerkoxXyYfQ3796Tj46qu6vrbeRzmr2ZzMtHTl4fKXn58qXKa5yamoqcnBylstq1a8PAwEDtEH9B/Qsh1M7ZlF+M+WnTpg3KlCmDbdu2Ydu2bWjcuLHS6S116wZU92tubq7KKdNy5crBwcHhrdvWo0cP7NixA5cuXVKpf9uxUBSavm7Aq/1amFPCPXr0gKGhIbZs2YKIiAh06tRJaU48TV/T9PR0lVn8XV1dUapUqQL3a1HfY/kxNzfX+NgCAG9vb1haWmLOnDnIzs7WSRyFPWasra3h7e2N8PBwbN26FSYmJujWrZtSm169euH06dM4cOCASn/Jyckq79s3Fdf+7tChAx4+fKh0mUZOTg6WLVsGCwsLlTv4X1fUfW9oaKjyvo+IiFC5ltHb2xvx8fHYs2ePoiwzM1Ple6hhw4YoV64cVq1apXTs/vHHH7h69ariM1nTYz6/Y7FDhw44d+4cTp8+rSh78eIF1qxZA2dn5wLn4pX/ofrjjz8qlb/5eaeLz63jx4+jT58+8PT0xObNm1VO88t17doVxsbGSp9LQgisWrUKFSpUUJr3T74fnZ2dsXfv3nxHlXfu3KnykP/hv3HjRsVsB5rS6xFA4NVUEWFhYbh+/brS5I7Aq5s65s2bh0GDBqFhw4Y4fvw4bty4ofMYvvrqK4SHh2PIkCGIjIxE06ZNkZubi2vXriE8PBwHDhx4688offfddzh06BCaNWuGYcOGwcjICKtXr0ZWVhYWLFhQ4LKdO3dG06ZNMWnSJMTGxqJGjRr47bff1H5x/vTTT2jWrBlq166NwYMHo3Llynj06BFOnz6N+/fv459//sl3PS1btsRXX32FH3/8ETdv3lScijxx4gRatmyJESNGoG7duvDz88OaNWsUp/rOnTuHDRs2oFu3bmjZsqVmO/U17u7uMDQ0xPz585GSkgKpVIpWrVqhSZMmKF26NPz8/DBq1ChIJBKEhYWpfHgePXoUI0aMgI+PD9zc3JCTk4OwsDDFh0l+qlWrBldXV4wbNw7x8fGwtLTEjh071F6b1aBBAwCvLvD19vaGoaEh+vTpk2/fxsbG6N69O7Zu3YoXL15g0aJFSvWWlpaKa3Cys7NRoUIFHDx4UGWU7Pnz56hYsSJ69uyJunXrwsLCAocPH8Zff/311utM5s2bh8jISHh4eGDw4MGoUaMGnj17hgsXLuDw4cN49uxZgctrS9PXDXi1X7dt24axY8eiUaNGsLCwUBrRe1O5cuXQsmVLfP/993j+/LnSqCqg+Wt648YNtG7dGr169UKNGjVgZGSEnTt34tGjRwW+rkDR3mP5adCgAVauXInvvvsOVapUQbly5VRGc15naWmJlStX4quvvkL9+vXRp08f2Nra4t69e9i3bx+aNm2qmNy2MAp7zPTu3Rv9+vXDihUr4O3trXJB/Pjx47Fnzx506tRJMf3TixcvcPHiRWzfvh2xsbFv/e324tjfgYGBWL16Nfz9/fH333/D2dkZ27dvx59//oklS5agVKlS+S5b1H3fqVMnzJo1CwMGDECTJk1w8eJFbN68WekGQeDV9FPLly+Hr68vRo8eDXt7e2zevFlxk4J8pM7Y2Bjz58/HgAED4OXlBV9fX8U0MM7OzhgzZgwAzY/5/I7FSZMmYcuWLWjfvj1GjRqFMmXKYMOGDbhz5w527NiRb6IFvPqM9/X1xYoVK5CSkoImTZrgyJEjKqOZQNE+t+7evYsuXbpAIpGgZ8+eStM+Aa9OH8tPIVesWBFBQUFYuHAhsrOz0ahRI+zatQsnTpzA5s2bFX9MPn/+HN7e3khKSsL48eNVbnR0dXXFZ599BgAqfwABUIz4tW/f/q3HuopC3zf8gXp9Gpg3ySfiffOn4NLT00VAQICwsrISpUqVEr169RKPHz/OdxqYN2/D9vPzE+bm5irrU/ezcy9fvhTz588XNWvWFFKpVJQuXVo0aNBAzJw5U6SkpCja4f9PBK3OhQsXhLe3t7CwsBBmZmaiZcuW4tSpU2/dN0K8mljyq6++UkwE/dVXX+U7EfTt27dF//79hZ2dnTA2NhYVKlQQnTp1Etu3b3/renJycsTChQtFtWrVFJMOt2/fXmnS4ezsbDFz5kzh4uIijI2NhaOjY4ETQb/Jy8tLZZqBn3/+WVSuXFkYGhoqTXHw559/ik8//VTIZDLh4OAgJkyYIA4cOKDU5r///hMDBw4Urq6uwtTUVJQpU0a0bNlSHD58+K3be+XKFdGmTRthYWEhbGxsxODBg8U///yjsl9zcnLEyJEjha2trZBIJEpTwrx5vMkdOnRIABASiUTExcWp1N+/f1988cUXwtraWlhZWQkfHx/x4MEDpf6ysrLE+PHjRd26dRUTiNetW1esWLHirdsmxKvJbocPHy4cHR2FsbGxsLOzE61btxZr1qx567L5TQPz5ntU3fQbmrxuQgiRlpYm+vbtK6ytrZUmJC5omqeff/5ZABClSpVSmvZCTpPXNDExUQwfPlxUq1ZNmJubCysrK+Hh4SHCw8Pful+E0Ow9Vpj99fDhQ9GxY0dRqlQppcl3C/pclPfl7e0trKyshKmpqXB1dRX+/v7i/PnzBcaf3zQwQhTumElNTRUymUxlSpXXPX/+XISEhIgqVaoIExMTYWNjI5o0aSIWLVqk+KnBguIRQrP9XdBE0Oo8evRIDBgwQNjY2AgTExNRu3btQv2sprb7PjMzUwQHBwt7e3shk8lE06ZNxenTp9V+Lv7333+iY8eOQiaTCVtbWxEcHCx27NghAIgzZ84otd22bZuoV6+ekEqlokyZMioTQWt6zOd3LArxfxNBW1tbC1NTU9G4cWONJ4LOyMgQo0aNEmXLlhXm5uYFTgSt7eeW/BjI7/HmenJzc8WcOXMUk+zXrFlT5TiWH5v5PV7/jFSnKNPASIQoxJXCRERE9NFasmQJxowZg/v376NChQolHQ4VIyaAREREeigjI0NlvtB69eohNze3WC53oveL3l8DSEREpI+6d++OSpUqwd3dHSkpKdi0aROuXbuGzZs3l3Ro9A4wASQiItJD3t7e+OWXX7B582bk5uaiRo0a2Lp1q8rNT/Rx4ilgIiIiIj2j1/MAEhEREekjJoBEREREeoYJIBEREZGe4U0gJSgvLw8PHjxAqVKltPoNYCIiInr3hBB4/vw5HBwcCvyVkvcZE8AS9ODBAzg6OpZ0GERERKSFuLg4VKxYsaTD0AoTwBIk/z3IuLg4WFpalnA0REREpInU1FQ4OjoW+LvO7zsmgCVIftrX0tKSCSAREdEH5kO+fOvDPHFNRERERFpjAkhERESkZ5gAEhEREekZJoBEREREeoYJIBEREZGeYQJIREREpGeYABIRERHpGSaARERERHqGCSARERGRnmECSERERKRnmAASERER6RkmgERERER6hgkgERERkZ75YBPAtLQ0TJ8+He3atUOZMmUgkUgQGhqqtu3Vq1fRrl07WFhYoEyZMvjqq6/w5MkTlXZ5eXlYsGABXFxcYGpqijp16mDLli1F6pOIiIjofWNU0gFoKzExEbNmzUKlSpVQt25dREVFqW13//59eHp6wsrKCnPmzEFaWhoWLVqEixcv4ty5czAxMVG0/eabbzBv3jwMHjwYjRo1wu7du9G3b19IJBL06dNHqz6JiIrTvYYrSzoEovdOpfNDSzqE994HmwDa29sjISEBdnZ2OH/+PBo1aqS23Zw5c/DixQv8/fffqFSpEgCgcePG+PzzzxEaGorAwEAAQHx8PBYvXozhw4dj+fLlAIBBgwbBy8sL48ePh4+PDwwNDQvVJxEREdH76IM9BSyVSmFnZ/fWdjt27ECnTp0UiRoAtGnTBm5ubggPD1eU7d69G9nZ2Rg2bJiiTCKRYOjQobh//z5Onz5d6D6JiIiI3kcfbAKoifj4eDx+/BgNGzZUqWvcuDGio6MVz6Ojo2Fubo7q1aurtJPXF7ZPIiIiovfRB3sKWBMJCQkAXp0ufpO9vT2ePXuGrKwsSKVSJCQkoHz58pBIJCrtAODBgweF7vNNWVlZyMrKUjxPTU3VcsuIiIiItPdRjwBmZGQAgNpkzNTUVKlNRkaGxu007fNNc+fOhZWVleLh6OhYqO0hIiIi0oWPOgGUyWQAoDTqJpeZmanURiaTadxO0z7fFBISgpSUFMUjLi6uUNtDREREpAsf9Slg+Wla+Wnb1yUkJKBMmTKKkTx7e3tERkZCCKF0Gli+rIODQ6H7fJNUKs23joiIiOhd+ahHACtUqABbW1ucP39epe7cuXNwd3dXPHd3d0d6ejquXr2q1O7s2bOK+sL2SURERPQ++qgTQADo0aMH9u7dq3S69ciRI7hx4wZ8fHwUZV27doWxsTFWrFihKBNCYNWqVahQoQKaNGlS6D6JiIiI3kcf9Cng5cuXIzk5WXGH7u+//4779+8DAEaOHAkrKytMnjwZERERaNmyJUaPHo20tDQsXLgQtWvXxoABAxR9VaxYEUFBQVi4cCGys7PRqFEj7Nq1CydOnMDmzZsVk0AD0LhPIiIioveRRAghSjoIbTk7O+Pu3btq6+7cuQNnZ2cAwOXLlzF27FicPHkSJiYm6NixIxYvXozy5csrLZOXl4f58+dj9erVSEhIQNWqVRESEoIvv/xSpX9N+yxIamoqrKyskJKSAktLS803nIjo/+NPwRGpKu6fgvsYvr8/6ATwQ/cxHEBEVLKYABKpYgL4dh/9NYBEREREpIwJIBEREZGeYQJIREREpGeYABIRERHpGSaARERERHqGCSARERGRnmECSERERKRnmAASERER6RkmgERERER6hgkgERERkZ5hAkhERESkZ5gAEhEREekZJoBEREREeoYJIBEREZGeYQJIREREpGeYABIRERHpGSaARERERHqGCSARERGRnmECSERERKRnmAASERER6RkmgERERER6hgkgERERkZ5hAkhERESkZ5gAEhEREekZJoBEREREeoYJIBEREZGeYQJIREREpGeYABIRERHpGSaARERERHqGCSARERGRnmECSERERKRnmAASERER6RkmgERERER6hgkgERERkZ5hAkhERESkZ5gAEhEREekZJoBEREREeoYJIBEREZGeYQJIREREpGeYABIRERHpGSaARERERHqGCSARERGRnmECSERERKRnmAASERER6RkmgERERER6hgkgERERkZ5hAkhERESkZ5gAEhEREekZJoBEREREeoYJIBEREZGeYQJIREREpGeYABIRERHpGSaARERERHqGCSARERGRnmECSERERKRn9CIBvHnzJvr06YOKFSvCzMwM1apVw6xZs5Cenq7U7tSpU2jWrBnMzMxgZ2eHUaNGIS0tTaW/rKwsTJw4EQ4ODpDJZPDw8MChQ4fe1eYQERERFYlRSQdQ3OLi4tC4cWNYWVlhxIgRKFOmDE6fPo3p06fj77//xu7duwEAMTExaN26NapXr47vv/8e9+/fx6JFi3Dz5k388ccfSn36+/tj+/btCAoKQtWqVREaGooOHTogMjISzZo1K4nNJCIiItLYR58AhoWFITk5GSdPnkTNmjUBAIGBgcjLy8PGjRuRlJSE0qVLY/LkyShdujSioqJgaWkJAHB2dsbgwYNx8OBBtG3bFgBw7tw5bN26FQsXLsS4ceMAAP3790etWrUwYcIEnDp1qmQ2lIiIiEhDH/0p4NTUVABA+fLllcrt7e1hYGAAExMTpKam4tChQ+jXr58i+QNeJXYWFhYIDw9XlG3fvh2GhoYIDAxUlJmamiIgIACnT59GXFxcMW8RERERUdF89AlgixYtAAABAQGIiYlBXFwctm3bhpUrV2LUqFEwNzfHxYsXkZOTg4YNGyota2JiAnd3d0RHRyvKoqOj4ebmppQoAkDjxo0BvDqVTERERPQ+K5YEMDExETk5OcXRdaG1a9cO3377LQ4dOoR69eqhUqVK6NOnD0aOHIkffvgBAJCQkADg1ajgm+zt7fHgwQPF84SEhHzbAVBq+6asrCykpqYqPYiIiIjeNa0SwPPnz2PWrFm4cuWKUvnOnTthZ2eH8uXLo2zZsli6dKlOgiwqZ2dneHp6Ys2aNdixYwcGDhyIOXPmYPny5QCAjIwMAIBUKlVZ1tTUVFEvb5tfu9f7Umfu3LmwsrJSPBwdHYu0XURERETa0OomkGXLlmHbtm0YPny4ouzOnTvo06cPsrOzYW9vj0ePHmHs2LGoW7eu4jRsSdi6dSsCAwNx48YNVKxYEQDQvXt35OXlYeLEifD19YVMJgPwaoTuTZmZmYp6AJDJZPm2k9fnJyQkBGPHjlU8T01NZRJIRERE75xWI4BnzpxBvXr1ULZsWUXZunXrkJ2djUWLFiE+Ph5nz56FgYFBiY8CrlixAvXq1VMkf3JdunRBeno6oqOjFadv5aeCX5eQkAAHBwfFc3t7+3zbAVBq+yapVApLS0ulBxEREdG7plUC+OjRI1SqVEmp7NChQzA3N8eIESMAAA0aNEDz5s3xzz//FD3KInj06BFyc3NVyrOzswEAOTk5qFWrFoyMjHD+/HmlNi9fvkRMTAzc3d0VZe7u7rhx44bK9Xtnz55V1BMRERG9z7RKAHNzc5Vu8khLS8OFCxfQtGlTmJiYKModHBzw8OHDokdZBG5uboiOjsaNGzeUyrds2QIDAwPUqVMHVlZWaNOmDTZt2oTnz58r2oSFhSEtLQ0+Pj6Ksp49eyI3Nxdr1qxRlGVlZWH9+vXw8PDgKV0iIiJ672l1DWClSpXw999/K57v27cPOTk5aNOmjVK71NRUWFlZFS3CIho/fjz++OMPNG/eHCNGjEDZsmWxd+9e/PHHHxg0aJDilO3s2bPRpEkTeHl5ITAwEPfv38fixYvRtm1btGvXTtGfh4cHfHx8EBISgsePH6NKlSrYsGEDYmNjsXbt2pLaTCIiIiKNaTUC2LlzZ9y7dw/du3fHsmXLMG7cOBgYGKBr165K7aKjo+Hk5KSTQLXl6emJU6dOoUGDBlixYgWCgoJw+/ZtzJ49GytXrlS0q1+/Pg4fPgyZTIYxY8ZgzZo1CAgIwPbt21X63LhxI4KCghAWFoZRo0YhOzsbe/fuhaen57vcNCIiIiKtSIQQorALJSYmonHjxoiNjVWUjR07FosWLVI8P3v2LD777DOMGzcOCxYs0EmwHxv5CGlKSgpvCCEirdxruPLtjYj0TKXzQ4u1/4/h+1urU8A2Njb4999/sX37djx58gQNGjRAq1atlNo8fPgQo0ePRr9+/XQSKBERERHphlYjgKQbH8NfEERUsjgCSKSKI4Bv99H/FjARERERKdPqFPDrLl++jJs3b+L58+fIbzCxf//+RV0NEREREemI1gng4cOHMWzYMNy+fTvfNkIISCQSJoBERERE7xGtEsDz58+jY8eOkEgk6Nu3Ly5evIiLFy9i0qRJuH37Ng4fPoykpCQMGDBA5RdDiIiIiKhkaZUAzp07Fzk5Ofjf//6Hzz//HAMGDMDFixcxe/ZsAEBycjK+/vpr7N27V+Xn1YiIiIioZGl1E8ipU6dQr149fP7552rrra2tsXHjRhgYGGDKlClFCpCIiIiIdEurBPDZs2eoWrWq4rn8939fvHihKJNKpWjevDkOHTpUxBCJiIiISJe0SgBtbW2Rmpqq9BwA/vvvP6V2GRkZSElJKUJ4RERERKRrWiWAVapUwZ07dxTPGzduDCEEVq9erSi7desWjh49isqVKxc9SiIiIiLSGa0SwA4dOuD69eu4evUqAKBdu3ZwcnLCypUr4eHhgR49eqBRo0bIzMxEQECATgMmIiIioqLR6i7g/v37w8rKCnl5eQBeXQO4Z88e9OrVC3/99Rf++usvGBgYYNCgQRg9erROAyYiIiKiotEqAbSzs8PXX3+tVFa7dm1cvXoV165dQ1JSEqpUqaK4NpCIiIiI3h9F/im4N1WrVk3XXRIRERGRDml1DaChoaFG1/YNHjwYRkY6zzGJiIiIqAi0SgCFEBBCaNyWiIiIiN4fWiWAmkpJSYFUKi3OVRARERFRIWl8fvbevXtKz9PS0lTK5HJycnD9+nUcPHgQrq6uRYuQiIiIiHRK4wTQ2dkZEolE8XzHjh3YsWNHgcsIITB48GDtoyMiIiIindM4AfT09FQkgMeOHUO5cuXyvePXxMQEDg4O6NKlC7744gvdREpEREREOqFxAhgVFaX4v4GBAdq3b49169YVR0xEREREVIy0mqPlzp07sLCw0HUsRERERPQOaJUAOjk56ToOIiIiInpHijRLc1RUFI4fP46EhARkZWWpbSORSLB27dqirIaIiIiIdEirBDAlJQVdu3bFiRMn3jrRMxNAIiIioveLVgngxIkTcfz4cVSpUgVDhgyBm5sbSpUqpevYiIiIiKgYaJUA7t69G+XLl8eZM2dQpkwZXcdERERERMVIq5+CS0lJQdOmTZn8EREREX2AtEoAq1atihcvXug6FiIiIiJ6B7RKAEeOHImoqCjcunVL1/EQERERUTHTKgEcNGgQRo0aBS8vL6xfvx7379/XdVxEREREVEy0ugnE0NAQACCEwKBBgwpsK5FIkJOTo81qiIiIiKgYaJUAOjo6QiKR6DoWIiIiInoHtEoAY2NjdRwGEREREb0rWl0DSEREREQfLp0lgElJSUhKStJVd0RERERUTIqUAO7fvx/e3t6wsLCAjY0NbGxsYGFhgXbt2mH//v26ipGIiIiIdEjrBHDMmDHo3LkzDh06hPT0dFhaWsLKygrp6ek4ePAgOnfujLFjx+oyViIiIiLSAa0SwG3btmHp0qWwtbXFjz/+qDj9++zZMyQnJ2PZsmUoV64cli5divDwcF3HTERERERFoFUCuGLFCpiamuL48eMYMWIErKysFHWWlpYYPnw4jh07BqlUihUrVugsWCIiIiIqOq0SwH/++QetWrWCm5tbvm3c3NzQqlUrxMTEaBsbERERERUDrRLAly9fwtzc/K3tzM3N8fLlS21WQURERETFRKsE0NXVFceOHcOLFy/ybZOeno5jx47B1dVV6+CIiIiISPe0SgB79eqFx48fo1u3brh586ZK/e3bt9G9e3c8efIEvXv3LnKQRERERKQ7Wv0U3Lhx47B7924cOXIENWrUQP369eHs7AwAuHv3Lv7++2/k5uaiYcOGCA4O1mW8RERERFREWiWAMpkMUVFRCAkJwbp16/DXX3/hr7/+UqofOHAg5s6dC5lMprNgiYiIiKjotEoAAcDCwgLLli3D/Pnz8ffff+PBgwcAAAcHBzRo0ABmZmY6C5KIiIiIdEfrBFDOzMwMzZs310UsRERERPQOaHUTSFpaGv79918kJibm2yYxMRH//vtvgXcKExEREdG7p1UC+P3336NevXq4fft2vm1u376NevXqYenSpVoHR0RERES6p1UC+Pvvv6NKlSrw8PDIt42HhwdcXV2xa9cubWMjIiIiomKgVQL433//oVq1am9tV716ddy5c0ebVRARERFRMdEqAczIyNBoeheZTIa0tDRtVkFERERExUSrBNDR0VFp3r/8/PXXX3BwcNBmFURERERUTLRKAL29vREbG4sffvgh3zZLly7FnTt30K5dO62DIyIiIiLd0yoBnDBhAiwtLTFu3Dh06tQJe/bsweXLl3H58mXs2bMHnTp1wtixY2FpaYkJEyboOmatXLhwAV26dEGZMmVgZmaGWrVq4ccff1Rqc+rUKTRr1gxmZmaws7PDqFGj1J7CzsrKwsSJE+Hg4ACZTAYPDw8cOnToXW0KERERUZFoNRF0xYoVsWfPHvTo0QP79+/HH3/8oVQvhICNjQ0iIiLg5OSkk0CL4uDBg+jcuTPq1auHqVOnwsLCArdv38b9+/cVbWJiYtC6dWtUr14d33//Pe7fv49Fixbh5s2bKtvn7++P7du3IygoCFWrVkVoaCg6dOiAyMhINGvW7F1vHhEREVGhSIQQQtuFk5KS8PPPP+PIkSOIi4sD8Or6wDZt2mDQoEEoXbq0zgLVVmpqKtzc3NCkSRNs374dBgbqBz07dOiAmJgYXLt2DZaWlgCAX375BYMHD8aBAwfQtm1bAMC5c+fg4eGBhQsXYty4cQCAzMxM1KpVC+XKlcOpU6cKFZuVlRVSUlIU6yQiKox7DVeWdAhE751K54cWa/8fw/e3VqeA5UqXLo0JEybgwIEDuHLlCq5cuYIDBw5g/Pjx70XyBwC//vorHj16hNmzZ8PAwAAvXrxAXl6eUpvU1FQcOnQI/fr1U3oh+/fvDwsLC4SHhyvKtm/fDkNDQwQGBirKTE1NERAQgNOnTysSYSIiIqL3VZESwA/B4cOHYWlpifj4eHzyySewsLCApaUlhg4diszMTADAxYsXkZOTg4YNGyota2JiAnd3d0RHRyvKoqOj4ebmppLxN27cGMCrU8lERERE77OPPgG8efMmcnJy0LVrV3h7e2PHjh0YOHAgVq1ahQEDBgAAEhISAAD29vYqy9vb2+PBgweK5wkJCfm2A6DU9k1ZWVlITU1VehARERG9a1rdBPIhSUtLQ3p6OoYMGaK467d79+54+fIlVq9ejVmzZiEjIwMAIJVKVZY3NTVV1AOvJsHOr528Pj9z587FzJkzi7Q9REREREX10Y8Ayn+xxNfXV6m8b9++AIDTp08r2mRlZaksn5mZqfSrJzKZLN92r69PnZCQEKSkpCgevF6QiIiISsJHnwDKf4mkfPnySuXlypUD8OpOZvnpW/mp4NclJCQo/ZqJvb19vu1eX586UqkUlpaWSg8iIiKid+2jTwAbNGgAAIiPj1cql1+rZ2tri1q1asHIyAjnz59XavPy5UvExMTA3d1dUebu7o4bN26oXL939uxZRT0RERHR++yjTwB79eoFAFi7dq1S+S+//AIjIyO0aNECVlZWaNOmDTZt2oTnz58r2oSFhSEtLQ0+Pj6Ksp49eyI3Nxdr1qxRlGVlZWH9+vXw8PCAo6NjMW8RERERUdHo5CaQmzdvIjExEWXLloWbm5suutSZevXqYeDAgVi3bh1ycnLg5eWFqKgoREREICQkRHHKdvbs2WjSpAm8vLwQGBiI+/fvY/HixWjbtq3S7xl7eHjAx8cHISEhePz4MapUqYINGzYgNjZWJckkIiIieh9pPQKYlZWFyZMnw8bGBtWqVUOzZs0wb948Rf2mTZtQv37992JevFWrVmHGjBk4e/YsgoKCEB0djR9++AFz5sxRtKlfvz4OHz4MmUyGMWPGYM2aNQgICMD27dtV+tu4cSOCgoIQFhaGUaNGITs7G3v37oWnp+e73CwiIiIirWj1U3AZGRlo1aoVzp07h/Lly6NBgwbYt28f/P39sW7dOgCvrrFzdHTExIkTlRIt+j8fw0/JEFHJ4k/BEaniT8G9nVYjgAsWLMDZs2cxcOBA/Pfff/j9999V2jg4OKBGjRo4fPhwkYMkIiIiIt3RKgHctm0bKlWqhJUrVyomQFbnk08+4Vx3RERERO8ZrRLAO3fuoGHDhjAyKvgeEhMTEyQlJWkVGBEREREVD60SQJlMplFid+fOHZQuXVqbVRARERFRMdEqAXR3d8f58+fx5MmTfNvcuXMH0dHRaNSokdbBEREREZHuaZUADh48GM+fP4evry8SExNV6pOTkzFw4EBkZ2cjMDCwyEESERERke5oNRG0r68vfv/9d2zduhWVK1dGkyZNAAB//vknunbtimPHjiE1NRX9+/dHp06ddBowERERERWN1hNBb968GfPnz4epqSkOHjwI4NUvgvz++++QSCSYPXs21q9fr7NAiYiIiEg3tP4pOIlEgvHjx2Ps2LG4cOECYmNjkZeXh4oVK6JRo0YwMTHRZZxEREREpCNF/i1gQ0NDNGrUiDd7EBEREX0gtD4FTEREREQfJo1GAAcOHKj1CiQSCdauXav18kRERESkWxolgKGhoWrLJRIJAEAIkW85E0AiIiKi94tGCWBkZKRKWUREBFasWAEPDw/4+vrC2dkZAHD37l1s2bIFZ86cwfDhw9GzZ0+dBkxERERERaNRAujl5aX0/MCBA1i1ahVWrFiBIUOGqLQfOXIkVq9ejeHDh6Njx466iZSIiIiIdEIi3jx/qwFPT09kZmbi3LlzBbbz8PCAsbExTp48qXWAH7PU1FRYWVkhJSUFlpaWJR0OEX2A7jVcWdIhEL13Kp0fWqz9fwzf31rdBRwTE4OqVau+tV2VKlXw77//arMKIiIiIiomWs0DaGRkhEuXLr213aVLl2BkVOSpBqmIOEJApKq4RwiIiN5nWo0Aenp64tKlS5g1a5bKHcBy3377LS5evAhPT88iBUhEREREuqXV8NycOXNw9OhRzJw5E2FhYejZsyecnJwAvLoLeMeOHbh9+zYsLCwwe/ZsnQZMREREREWjVQJYo0YNHD16FH5+frh69Srmz5+vMidgtWrVEBoaipo1a+ouWiIiIiIqMq0v0GvYsCEuX76MqKgonDhxAg8ePAAA2Nvbo3nz5mjRooUiKSQiIiKi90eR79Bo0aIFWrRooYNQiIiIiOhd0OomECIiIiL6cDEBJCIiItIzTACJiIiI9AwTQCIiIiI9wwSQiIiISM8wASQiIiLSM0wAiYiIiPRMkecBjI+Px59//on4+HgAQIUKFdC0aVNUqFChyMERERERke5pnQA+efIEw4cPx86dO5GXl6dUJ5FI0KNHDyxfvhy2trZFDpKIiIiIdEerBDAlJQWenp64fv06ZDIZ2rZtC2dnZ0gkEsTGxuLAgQOIiIjAv//+izNnzsDKykrXcRMRERGRlrRKAOfNm4fr16/Dx8dH7ShfYmIiRowYgfDwcMyfPx9z5szRSbBEREREVHRa3QSyc+dOODo6YtOmTWpP8drY2CAsLAyOjo7YsWNHkYMkIiIiIt3RKgG8e/cumjZtCmNj43zbGBsbo2nTprh3757WwRERERGR7mmVAMpkMiQmJr61XWJiImQymTarICIiIqJiolUC2KBBAxw7dgznz5/Pt83ff/+NqKgoNGzYUOvgiIiIiEj3tEoAx4wZg+zsbLRu3RpTp07F1atXkZGRgYyMDFy7dg3Tp09HmzZtkJubizFjxug6ZiIiIiIqAq3uAu7QoQNmz56NqVOnYs6cOWrv8pVIJPjuu+/Qvn37IgdJRERERLqj9U/BhYSE4MyZM+jXrx+cnZ1hYmICExMTODs746uvvsLp06cREhKiy1iJiIiISAe0GgHcs2cPjI2N0b59e2zYsEHXMRERERFRMdJqBPCLL77Ajz/+qOtYiIiIiOgd0CoBtLW1RenSpXUdCxERERG9A1olgC1atMC5c+cghNB1PERERERUzLRKAL/99lskJiZizJgxyMzM1HVMRERERFSMtLoJZMuWLejQoQOWLVuGrVu3ok2bNqhUqRJMTU1V2kokEkydOrXIgRIRERGRbmiVAM6YMQMSiQRCCDx+/Bi//vprvm2ZABIRERG9X7RKANevX6/rOIiIiIjoHdEqAfTz89N1HERERET0jmj9SyBERERE9GHSagRQLicnB/v27cO5c+eQmJgIDw8PDBw4EADw4MEDJCYmokaNGjAyKtJqiIiIiEiHtM7MTp48iX79+iEuLg5CCEgkEmRnZysSwNOnT6NXr16IiIhA9+7ddRYwERERERWNVqeAr1y5gnbt2iEhIQEjR45EeHi4yqTQnTt3hpmZGXbs2KGTQImIiIhIN7QaAfz222+RmZmJ/fv3o23btmrbmJiYoH79+oiOji5SgERERESkW1qNAEZGRqJx48b5Jn9yFSpUwIMHD7QKrLjMnj0bEokEtWrVUqk7deoUmjVrBjMzM9jZ2WHUqFFIS0tTaZeVlYWJEyfCwcEBMpkMHh4eOHTo0LsIn4iIiKjItEoAk5OT4ejo+NZ2L168QHZ2tjarKBb379/HnDlzYG5urlIXExOD1q1bIz09Hd9//z0GDRqENWvWwMfHR6Wtv78/vv/+e3z55ZdYunQpDA0N0aFDB5w8efJdbAYRERFRkWh1CrhcuXK4devWW9tdvXpVo0TxXRk3bhw+/fRT5ObmIjExUalu8uTJKF26NKKiomBpaQkAcHZ2xuDBg3Hw4EHFaOe5c+ewdetWLFy4EOPGjQMA9O/fH7Vq1cKECRNw6tSpd7tRRERERIWk1Qhgq1atEBMTg8jIyHzb7Ny5E7du3cLnn3+udXC6dPz4cWzfvh1LlixRqUtNTcWhQ4fQr18/RfIHvErsLCwsEB4erijbvn07DA0NERgYqCgzNTVFQEAATp8+jbi4uGLdDiIiIqKi0ioBnDRpEkxMTNCtWzesXLkSDx8+VNQlJSVh3bp1CAgIgLm5OcaOHauzYLWVm5uLkSNHYtCgQahdu7ZK/cWLF5GTk4OGDRsqlZuYmMDd3V3pRpbo6Gi4ubkpJYoA0LhxYwCvTiUTERERvc+0SgCrVauGLVu2IC8vDyNGjECFChUgkUiwYcMG2NjYYPDgwcjKysLmzZvh4uKi65gLbdWqVbh79y6+/fZbtfUJCQkAAHt7e5U6e3t7pRtZEhIS8m0HoMCbXrKyspCamqr0ICIiInrXtP4puG7duuHSpUsYOXIkqlWrBlNTU5iYmKBy5cr4+uuv8e+//6JLly66jFUrT58+xbRp0zB16lTY2tqqbZORkQEAkEqlKnWmpqaKennb/Nq93pc6c+fOhZWVleLxPl0fSURERPqjSL/R5uTkpPaauvfJlClTUKZMGYwcOTLfNjKZDMCrEbo3ZWZmKurlbfNr93pf6oSEhCidEk9NTWUSSERERO/cR/0jvTdv3sSaNWuwZMkSpVOzmZmZyM7ORmxsLCwtLRWnb+Wngl+XkJAABwcHxXN7e3vEx8erbQdAqe2bpFKp2tFDIiIionepyAmgfEoVdaNicpUqVSrqarQSHx+PvLw8jBo1CqNGjVKpd3FxwejRozFz5kwYGRnh/Pnz6NWrl6L+5cuXiImJUSpzd3dHZGQkUlNTlW4EOXv2rKKeiIiI6H2mdQJ46NAhzJkzB6dPny5wsmeJRIKcnBxtV1MktWrVws6dO1XKp0yZgufPn2Pp0qVwdXWFlZUV2rRpg02bNmHq1KkoVaoUACAsLAxpaWlKk0H37NkTixYtwpo1axTzAGZlZWH9+vXw8PDgKV0iIiJ672mVAO7YsQO9e/dGXl4ebGxs4OTkBAsLC13HVmQ2Njbo1q2bSrn8usXX62bPno0mTZrAy8sLgYGBuH//PhYvXoy2bduiXbt2inYeHh7w8fFBSEgIHj9+jCpVqmDDhg2IjY3F2rVri3mLiIiIiIpOqwRwxowZAIBffvkF/v7+MDDQ+mbi90b9+vVx+PBhTJw4EWPGjEGpUqUQEBCAuXPnqrTduHEjpk6dirCwMCQlJaFOnTrYu3cvPD09SyByIiIiosKRCCFEYReSyWRo0qQJjhw5Uhwx6Y3U1FRYWVkhJSVFZWJpXbrXcGWx9U30oap0fmhJh6ATfH8TqSru9/e7+v4uTloN3dnZ2cHGxkbXsRARERHRO6BVAujj44Pjx48r5r4jIiIiog+HVgngjBkz4OjoiM6dO+P27du6jomIiIiIipFWN4GYmZnh8OHDaNKkCapVqwZnZ2dUqFBB7c0gEomE1woSERERvUe0SgDj4+PRpk0b3LhxA0II3L59O9+RQIlEUqQAiYiIiEi3tEoAx44di+vXr6NNmzYYOXIkKleu/F7OA0hEREREqrRKAA8fPoyqVavijz/+gKGhoa5jIiIiIqJipNVNIHl5eahfvz6TPyIiIqIPkFYJ4Keffsq7f4mIiIg+UFolgLNnz8bFixfx888/6zoeIiIiIipmWl0DeOnSJQwYMABDhgzB5s2b8fnnn+c7DQwA9O/fv0hBEhEREZHuaJUA+vv7QyKRQAiB48eP48SJE2rbCSEgkUiYABIRERG9R7RKAKdNm8b5/YiIiIg+UFolgDNmzNBxGERERET0rmh1EwgRERERfbiYABIRERHpGa1OAbdq1UrjthKJBEeOHNFmNURERERUDApMAFNTU2FpaalSHhUV9daO5XcJ82YRIiIiovdLgQng0qVLkZeXh+nTpyuV37lzR237vLw8xMXF4eDBg1i6dCmGDRuGYcOG6S5aIiIiIiqyAhPA0qVLY9SoUbh06RI2bNgAMzMzAICTk1O+y7i4uMDT0xOtWrWCt7c3Pv300wLbExEREdG7VeBNICNGjMDatWtx4MABNG3aFPfu3dO441atWqFhw4aYN29ekYMkIiIiIt15613AAwYMQHR0NGQyGUJDQwvVecWKFXH58mVtYyMiIiKiYqDRXcCurq44efIkbty4oXHHGRkZ+Ouvv2Bqaqp1cERERESkexpPA2NgYIBq1aoBQIGngtPS0nDjxg0sXrwYcXFx8PX1LXqURERERKQzWs0D6Ozs/NbpXYQQ+OSTT7Bw4UKtAiMiIiKi4qFVAujp6ZlvAmhiYgJ7e3t4eXnB19eXp4CJiIiI3jNaJYCaTARNRERERO8n/hYwERERkZ5hAkhERESkZzQ6BTxr1qwirWTatGlFWp6IiIiIdEejBHDGjBmQSCQQQmjc8es3iTABJCIiInp/aJQAzp07t1CdxsfHY+3atcjIyHjrdDFERERE9G5plABOnDhRo84ePXqEOXPm4JdffkFmZiYsLS0RFBRUlPiIiIiISMe0mgbmTU+ePMG8efOwatUqZGZmwsLCAsHBwQgODoa1tbUuVkFEREREOlKkBPDp06eYP38+Vq5ciRcvXsDCwgKTJk1CcHAwypQpo6sYiYiIiEiHtEoAk5KSsHDhQixfvhxpaWkwNzfHhAkTMH78eJQtW1bXMRIRERGRDhUqAUxJScGiRYuwbNkypKamwszMDMHBwZg4cSJsbGyKK0YiIiIi0iGNEsDU1FR8//33WLp0KVJSUiCTyTBmzBhMnDgR5cqVK+4YiYiIiEiHNEoAnZyckJqaClNTU4wePRqTJk1C+fLlizs2IiIiIioGGv0UXEpKCgAgKysLP/30ExwdHWFiYqLRQyqVFusGEBEREVHhaHwNoBACQgjk5eUVZzxEREREVMw0SgCZ9BERERF9PDQ6BUxEREREHw8mgERERER6hgkgERERkZ5hAkhERESkZ5gAEhEREekZJoBEREREeoYJIBEREZGeYQJIREREpGeYABIRERHpGSaARERERHqGCSARERGRnmECSERERKRnmAASERER6RkmgERERER65qNPAP/66y+MGDECNWvWhLm5OSpVqoRevXrhxo0bKm2vXr2Kdu3awcLCAmXKlMFXX32FJ0+eqLTLy8vDggUL4OLiAlNTU9SpUwdbtmx5F5tDREREVGRGJR1AcZs/fz7+/PNP+Pj4oE6dOnj48CGWL1+O+vXr48yZM6hVqxYA4P79+/D09ISVlRXmzJmDtLQ0LFq0CBcvXsS5c+dgYmKi6PObb77BvHnzMHjwYDRq1Ai7d+9G3759IZFI0KdPn5LaVCIiIiKNSIQQoqSDKE6nTp1Cw4YNlRK4mzdvonbt2ujZsyc2bdoEABg2bBhCQ0Nx7do1VKpUCQBw+PBhfP7551i9ejUCAwMBAPHx8XBxcUFgYCCWL18OABBCwMvLC3fu3EFsbCwMDQ01ii01NRVWVlZISUmBpaWlLjdbyb2GK4utb6IPVaXzQ0s6BJ3g+5tIVXG/v9/V93dx+uhPATdp0kQp+QOAqlWrombNmrh69aqibMeOHejUqZMi+QOANm3awM3NDeHh4Yqy3bt3Izs7G8OGDVOUSSQSDB06FPfv38fp06eLcWuIiIiIiu6jTwDVEULg0aNHsLGxAfBqVO/x48do2LChStvGjRsjOjpa8Tw6Ohrm5uaoXr26Sjt5PREREdH7TC8TwM2bNyM+Ph69e/cGACQkJAAA7O3tVdra29vj2bNnyMrKUrQtX748JBKJSjsAePDgQb7rzcrKQmpqqtKDiIiI6F3TuwTw2rVrGD58OD777DP4+fkBADIyMgAAUqlUpb2pqalSm4yMDI3aqTN37lxYWVkpHo6OjkXbGCIiIiIt6FUC+PDhQ3Ts2BFWVlbYvn274mYNmUwGAIpRvtdlZmYqtZHJZBq1UyckJAQpKSmKR1xcXNE2iIiIiEgLH/00MHIpKSlo3749kpOTceLECTg4OCjq5Kdv5aeCX5eQkIAyZcooRv3s7e0RGRkJIYTSaWD5sq/3+yapVKp29JCIiIjoXdKLEcDMzEx07twZN27cwN69e1GjRg2l+goVKsDW1hbnz59XWfbcuXNwd3dXPHd3d0d6errSHcQAcPbsWUU9ERER0fvso08Ac3Nz0bt3b5w+fRoRERH47LPP1Lbr0aMH9u7dq3Ra9siRI7hx4wZ8fHwUZV27doWxsTFWrFihKBNCYNWqVahQoQKaNGlSfBtDREREpAMf/Sng4OBg7NmzB507d8azZ88UEz/L9evXDwAwefJkREREoGXLlhg9ejTS0tKwcOFC1K5dGwMGDFC0r1ixIoKCgrBw4UJkZ2ejUaNG2LVrF06cOIHNmzdrPAk0ERERUUn56BPAmJgYAMDvv/+O33//XaVengA6Ojri2LFjGDt2LCZNmgQTExN07NgRixcvVrlub968eShdujRWr16N0NBQVK1aFZs2bULfvn2LfXuIiIiIiuqj/ym49xl/Co6o5PCn4Ig+XvwpuLf76K8BJCIiIiJlTACJiIiI9AwTQCIiIiI9wwSQiIiISM8wASQiIiLSM0wAiYiIiPQME0AiIiIiPcMEkIiIiEjPMAEkIiIi0jNMAImIiIj0DBNAIiIiIj3DBJCIiIhIzzABJCIiItIzTACJiIiI9AwTQCIiIiI9wwSQiIiISM8wASQiIiLSM0wAiYiIiPQME0AiIiIiPcMEkIiIiEjPMAEkIiIi0jNMAImIiIj0DBNAIiIiIj3DBJCIiIhIzzABJCIiItIzTACJiIiI9AwTQCIiIiI9wwSQiIiISM8wASQiIiLSM0wAiYiIiPQME0AiIiIiPcMEkIiIiEjPMAEkIiIi0jNMAImIiIj0DBNAIiIiIj3DBJCIiIhIzzABJCIiItIzTACJiIiI9AwTQCIiIiI9wwSQiIiISM8wASQiIiLSM0wAiYiIiPQME0AiIiIiPcMEkIiIiEjPMAEkIiIi0jNMAImIiIj0DBNAIiIiIj3DBJCIiIhIzzABJCIiItIzTACJiIiI9AwTQCIiIiI9wwSQiIiISM8wASQiIiLSM0wAiYiIiPQME0AiIiIiPcMEUEtZWVmYOHEiHBwcIJPJ4OHhgUOHDpV0WERERERvxQRQS/7+/vj+++/x5ZdfYunSpTA0NESHDh1w8uTJkg6NiIiIqEBGJR3Ah+jcuXPYunUrFi5ciHHjxgEA+vfvj1q1amHChAk4depUCUdIRERElD+OAGph+/btMDQ0RGBgoKLM1NQUAQEBOH36NOLi4kowOiIiIqKCMQHUQnR0NNzc3GBpaalU3rhxYwBATExMCURFREREpBmeAtZCQkIC7O3tVcrlZQ8ePFC7XFZWFrKyshTPU1JSAACpqanFEOX/eZ6bUaz9E32Iivt9967w/U2kqrjf3/L+hRDFup7ixARQCxkZGZBKpSrlpqaminp15s6di5kzZ6qUOzo66jZAIno7q+CSjoCIiss7en8/f/4cVlZW72RdusYEUAsymUxpJE8uMzNTUa9OSEgIxo4dq3iel5eHZ8+eoWzZspBIJMUTLL03UlNT4ejoiLi4OJXLB4jow8b3t34RQuD58+dwcHAo6VC0xgRQC/b29oiPj1cpT0hIAIB8DwipVKoycmhtba3z+Oj9ZmlpyS8Ioo8U39/640Md+ZPjTSBacHd3x40bN1SuMTh79qyinoiIiOh9xQRQCz179kRubi7WrFmjKMvKysL69evh4eHBa/qIiIjovcZTwFrw8PCAj48PQkJC8PjxY1SpUgUbNmxAbGws1q5dW9Lh0XtKKpVi+vTpam8gIqIPG9/f9KGRiA/5HuYSlJmZialTp2LTpk1ISkpCnTp18O2338Lb27ukQyMiIiIqEBNAIiIiIj3DawCJiIiI9AwTQCIiIiI9wwSQqBhFRUVBIpEgNDS0pEMhohIwY8YMSCQSxMbGlnQoREqYABK9RVJSEmQyGSQSCcLCwlTqY2JiMGPGDH7AE71H5H98LVq0qNjXtWvXLsyYMaPY10OkS0wAid5i8+bNyMrKgouLC9atW6dSHxMTg5kzZ6pNAD09PZGRkYGvvvrqHURKRCVh165dan/nHQCmTJmCjIwMODk5veOoiArGBJDoLdauXYuWLVsiKCgIx44dw3///afxsgYGBjA1NYWhoWExRkhE7ysjIyOYmpry997pvcMEkKgAFy5cQExMDPz8/NC3b18YGRkpjQLOmDEDAwYMAAC0bNkSEokEEokE/v7+AFSvAbx69SokEgnGjh2rdn2+vr4wMTHBkydPAADXrl3DsGHDULNmTZQqVQpmZmZo0KABfvnll+LbaKKPUGxsLCQSCWbMmIG9e/eiUaNGMDU1hb29PcaPH4+cnByl9ufOnYO/vz/c3NxgZmaGUqVKoWnTpti5c6dSuxYtWmDDhg0AoHj/v/6ef/MawJUrV0IikWDPnj0qMebl5aFixYpKPyd68OBB9O7dG5UrV4ZMJoO1tTXatm2LY8eO6W7nkF7iL4EQFWDt2rWwsLBAjx49YG5ujk6dOmHDhg2YNWsWDAwM0L17dyQkJGDNmjWYPHkyqlevDgBwdXVV21/16tXRqFEj/Prrr1i4cKHSyGBqaip2796N9u3bw9bWFsCrBPL48ePo1KkTXFxc8OLFC0RERGDw4MF48uQJQkJCin8nEH1E9u/fjxUrVmDIkCEYOHAgdu/ejUWLFqF06dKYPHmyot3OnTtx7do19OrVC05OTnj69Ck2bNiA7t27Y/Pmzejbty8A4JtvvkFeXh5OnDihdI1wkyZN1K6/T58+GDNmDDZu3IguXboo1R05cgTx8fEIDg5WlIWGhuLZs2fo378/KlasiPj4ePzyyy9o3bo1IiMj0bx5c13uHtIngojUysjIENbW1sLPz09RtmvXLgFA7N+/X1G2fv16AUBERkaq9BEZGSkAiPXr1yvKli9fLgCIffv2KbX95ZdfBACxY8cORVlaWppKn7m5ucLLy0tYWlqKly9far+BRB8x+Xtv4cKFQggh7ty5IwAIMzMzcefOHUW7vLw8UbNmTWFnZ6e0vLr33osXL4Sbm5uoXr26Urmfn5/I7+t0+vTpAoDSOnv27CmkUql49uyZUtt+/foJIyMj8ejRowLjePjwoShbtqxo3769+o0n0gBPARPl47fffkNycjL8/PwUZR06dICtra3am0E0JT/Nu3HjRqXyjRs3okyZMujUqZOizNzcXPH/zMxMPH36FM+ePUPbtm2RmpqKa9euaR0HkT7q1q0bnJ2dFc8lEglatmyJhw8fIi0tTVH++nsvPT0dT58+RXp6Olq1aoWrV68iNTVV6xj8/PyQlZWFbdu2KcrS0tKwc+dOtGvXDuXKlVMbR1paGp4+fQpDQ0N4eHjg7NmzWsdAxFPARPlYu3YtbG1tUbFiRdy6dUtR3rZtW0RERCAxMRE2NjaF7lee5O3evRupqamwtLREbGwsTpw4gaFDh8LExETRNi0tDTNmzEB4eDji4uJU+kpKStJu44j0VOXKlVXKypYtCwB4+vQpLCwsAACPHz/GlClTsHv3bjx+/FhlmeTkZFhaWmoVgzzJ27hxI4YMGQIA2LFjB168eIH+/fsrtb19+za++eYbHDhwAMnJyUp1vLGEioIJIJEad+7cQWRkJIQQcHNzU9tm06ZNCAoK0qr//v3747fffkN4eDgGDRqEsLAwCCGURhsBoG/fvti7dy8CAwPh6emJsmXLwtDQEPv378cPP/yAvLw8rdZPpK8KuiNfCKH4t23btrh69SpGjx6Nhg0bwsrKCoaGhli/fj1+/fXXIr33jIyM0LdvXyxZsgS3bt1ClSpVsHHjRpQuXVrpusC0tDR4enrixYsXCAoKQu3atVGqVCkYGBhg7ty5OHr0qNYxEDEBJFJj/fr1EELg559/hrW1tUr9lClTsG7dOgQFBWn1V3iHDh1gY2ODjRs3KhLAatWqoXHjxoo2ycnJ2Lt3L7766iusWrVKafnDhw8Xep1EpJl///0X//zzD6ZNm6Yyv5+6O/C1+Qzw8/PDkiVLsHHjRgwePBhRUVEIDAyEVCpVtDly5AgePHiAdevWKWYbkJsyZUqh10n0OiaARG/Iy8tDaGgoateujUGDBqltc/nyZcyYMQN//fWX4pTRs2fPNF6HsbEx+vbti2XLluHXX3/FzZs3MXfuXKU28pEK+aiEXEJCAqeBISpG+b33Ll26pDINDAClz4AyZcpotA53d3fUqVMHmzZtgqmpKfLy8lTOAOQXx8GDB3n9HxUZE0CiNxw8eBBxcXEICAjIt02PHj0wY8YMrF27FpMnT4aBgQFmz56NpKQkmJubw8XFBR4eHgWux8/PDz/++COGDh0KAwMD9OvXT6m+VKlSaNu2LTZt2gSZTIZGjRrh7t27WL16NVxcXPD06VOdbC8RKatevTpq1qyJBQsWID09HZ988glu3LiB1atXo3bt2vj777+V2n/66adYvnw5hg0bho4dO8LY2BgeHh5wcXEpcD1+fn4IDg7G/Pnz4ebmhk8//VSpvlmzZrCzs0NwcDBiY2NRsWJFxMTEICwsDLVr18bFixd1vu2kP3gXMNEb1q5dCwDo3r17vm1q1aoFNzc3bN26VXFXcEZGBoYOHQpfX1+sXLnyreupX78+atWqhdTUVLRq1QoVK1ZUabNp0yYMHDgQv//+O0aMGIFdu3Zh9uzZGD58uPYbSEQFMjQ0xL59+9C5c2ds2LABo0ePxrFjx7Bhwwalu/TlfH19ERwcjJMnT8Lf3x++vr4aTdT85ZdfwsjICKmpqSo3fwCAtbU1Dhw4AA8PDyxbtgzBwcG4cuUK9u/fj/r16+tkW0l/ScSbY8tERERE9FHjCCARERGRnmECSERERKRnmAASERER6RkmgERERER6hgkgERERkZ5hAkhERESkZ5gAEhEREekZJoBEREREeoYJIBEREZGeYQJIREREpGeYABIRERHpGSaARERERHqGCSARERGRnmECSERERKRnmAASERER6RkmgERERER6hgkgERERkZ5hAkhEBYqMjESPHj1QoUIFmJiYoHTp0vjkk0/g4+OD5cuXIyUlpaRDfG/5+/tDIpEgKiqqpEMhIlLCBJCI8jVr1iy0atUKv/32G6ysrNCpUye0bdsWMpkMv/32G0aOHImrV68qLePs7AyJRKLTOCQSCZydnXXaJxWfqKgoSCQS+Pv7l3QoRJQPo5IOgIjeT3///TdmzJgBY2NjhIeHo1u3bkr1Dx8+xKZNm2BtbV0i8RERkfaYABKRWr/99huEEOjVq5dK8gcAdnZ2GDdu3LsPjIiIioyngIlIrSdPngAAbG1tNWovP+139+5dAK9O28ofr5++vXXrFmbMmIHPPvsMdnZ2MDExQcWKFdG/f3/cuHFDqc/Q0FDF6eS7d+8q9dmiRQtFu4JOEcv7mDFjhlJ5Wloa5s6di7p168LKygoWFhZwdXWFj48PDhw4oNE2y61btw7u7u6QyWSws7ODv78/Hj58mG/7EydOYMSIEahTpw5Kly4NmUyGatWqYdKkSUhOTi7UugFACIEtW7bg888/R9myZWFqagpnZ2f06tULR44cUWl/+vRpdO3aFba2tpBKpXB2dsawYcPw4MEDlbav77979+6hb9++sLW1hUwmQ8OGDfH7778rtff390fLli0BABs2bFB6zV5/Dfbt24eBAweievXqsLS0hLm5OerWrYs5c+YgKyur0PuAiAqHI4BEpJajoyMAYMeOHQgJCUG5cuUKbG9nZwc/Pz9s374dL168gJ+fn6LOxsZG8f9ffvkFCxYsQK1atdCoUSNIpVJcuXIFYWFh2L17N06cOIE6deoAAKpUqQI/Pz9s2LAB5ubm6Nmzp6KfatWqab1tubm5aNOmDc6ePQsbGxu0aNECpqamuH//Pvbv3w9zc3N4e3tr1NekSZMwf/58GBsbo2XLlrCyssIff/yByMhI1K1bV+0y48ePxz///IM6deqgdevWyMzMxIULFzB//nzs3bsXZ86cgYWFhcbb4uvri4iICJiYmKBp06YoX7484uLisG/fPrx8+RKtW7dWtN+0aRP8/f2Rm5uLpk2bwtHRERcuXMDKlSvx22+/ISoqSu2+jY2NRaNGjVCqVCm0bt0a9+7dw+nTp9GtWzf88ccfaNu2LQCgWbNmePjwIQ4cOABXV1c0a9ZM0Ye7u7vi/wEBAcjIyECtWrVQp04dpKSk4Ny5c/jmm29w5MgRHDx4EIaGhhrtAyLSgiAiUuP27dtCJpMJAKJUqVLCz89P/Pzzz+LChQsiJycn3+WcnJxEQR8tp0+fFv/9959K+bp16wQA0bJlS5U6AMLJySnfPguqX79+vQAgpk+frig7evSoACAaNWokMjIylNqnpKSI8+fP57uuN7dFIpEIKysrceHCBUX58+fPRatWrQQAAUBERkYqLbd//36RnJysVJaZmSkCAwMFADFz5kyN1i+EEN9++60AIGrUqKGyX5OTk0VUVJTi+b1794RMJhOGhoZi9+7divLc3FwRFBQkAIiGDRsq9SHffwBEcHCwyM3NVdT98MMPAoBo3ry50jKRkZECgPDz88s37l27don09HSlstTUVNGpUycBQGzYsEHjfUBEhccEkIjydfjwYeHo6KhIAOQPa2trMXToUPHgwQOVZd6WABakadOmQiKRqCRHuk4At23bJgCIoKAgreKU69+/vwAgpk2bplJ3+fJlIZFI1CaA+UlPTxdGRkaifv36GrXPysoS1tbWAoA4c+bMW9tPmzZNABC+vr4qdZmZmcLBwUEAECdPnlSUy/efi4uLyMrKUlomOztblC5dWhgbGyvVaZIA5ufmzZsCgOjevXuhlyUizfEUMBHlq3Xr1rh16xb27duHgwcP4ty5c/j333+RnJyMlStXYseOHTh+/Dg++eSTQvWblpaG33//HTExMXj27Bmys7MBAAkJCRBC4Pbt26hfv35xbBKAV6ciDQwMsH79etSoUQPdu3dH2bJlC93PiRMnAAB9+vRRqatRowbq1q2LmJgYtcvGx8fj999/x7Vr15Camoq8vDwAgImJCW7evKnR+s+fP4/k5GTUrVsXHh4eGsf75ZdfqtRJpVL4+Phg6dKlOHHiBJo2bapU36JFC5iYmCiVGRkZwcXFBRcuXMDTp09hb2+vUdxyN2/exP79+3Hr1i28ePECeXl5EEIo6oio+DABJKICmZiY4IsvvsAXX3wBAEhOTsbWrVsxefJkPH78GCNGjMChQ4c07u/o0aPo06eP4iYTdZ4/f17kuAvi5uaGBQsWICQkBIGBgRgyZAhq1aqF1q1bw9/fX3EN4tvIb5pwcnJSW+/s7Kw2Afz+++8xadIkReKrrbi4OACAq6urRu3l8eZ3w4y8PD4+XqWuYsWKapcpVaoUABTqxg0hBMaNG4cffvhBkfC9qbiPASJ9x7uAiahQrK2tMWTIEISGhgJ49Ush6enpGi2blpaGXr16ITExEdOmTcOVK1eURn58fX0BIN+kQBvykbU3BQcH4/bt2/jxxx/RsWNH3Lt3Dz/88APc3d2xdOlSna3/TWfOnEFwcDDMzMwQGhqK2NhYZGZmQry6JKfQo2i6VNAE3gYGuvu62LZtG77//ntUrFgR27dvR3x8PF6+fAkhhCKR1OUxQESqmAASkVZatWoF4NVdqJpOXXLixAk8ffoUPXr0wMyZM1G9enWYmZkpEo///vtPq1iMjY2Rlpamtk4+SqaOo6MjRo4ciT179uDJkycICwuDoaEhJkyYgKSkpLeuV56syae+eZO68p07dwIAZs+eDT8/Pzg5OUEqlQIAMjIyCpw+Rl38AHD79m2N2js4OBQYb2xsLACgQoUKGsegDfk+WLlyJXr06AEHBwcYGxsD0P4YIKLCYQJIRGq9bQTm1q1bAF6dIn59mhf5dWI5OTkqy8iTKnWnE2/duoULFy6oXZexsbHa/uTs7e3x9OlTPH36VKXu8OHDBWzF/zEyMkK/fv3QqFEjvHz5UqNr0Jo3bw4ACA8PV6m7du2a2tO/Be2DiIiIQo18NWjQANbW1vjnn39w7tw5jePdsmWLSt3Lly8RERGh1E5bBR0DQMH7QN2+JCLdYwJIRGpNnToV48ePVzu6FB8fj6+//hoA0KVLF6WbA+SjTNevX1dZzs3NDcCrXxl5/RrA5ORkBAQE5HtNnIODAx49epTvSKOXlxcA4LvvvlMqX7BgAU6ePKnSPjIyEocPH1Y5PXznzh1cvXoVEokk32veXjdkyBAAwJIlS/DPP/8oyl+8eIGRI0eqTebk+2Dt2rVK23vlyhVMnDjxret8nVQqxZgxYwC8mlfvzZG9lJQUHDt2TPE8ICAAMpkMW7duxb59+xTleXl5mDx5MuLj49GgQQOVG0AKq6BjAPi/fbBmzRqlfXTixAksXLiwSOsmIg2VxK3HRPT+Gz16tGLaFzc3N9GtWzfRp08f0axZM2FsbCwAiCpVqoj79+8rLbd48WIBQJQvX1706dNHBAQEiIkTJyrqP//8c8VUMt26dRPdunUT1tbWokqVKqJr165qp00ZOXKkYiqSL7/8UgQEBIgFCxYo6i9duqSYs9Dd3V306NFDuLm5CZlMJoYNG6YyDYx8/jpbW1vRrl078eWXX4q2bdsKqVQqAIiRI0dqvJ/GjRsnAAhjY2Ph7e0tevXqJcqXLy8qVaokOnfurLI9iYmJws7OTrE9vXr1Em3atBHGxsbCx8en0NPoZGdni27dugkAwsTERLRu3Vr4+vqKZs2aCTMzM9G1a1el9hs3bhQGBgZCIpGIZs2aCV9fX/HJJ58oXrOrV68qtVc3jc7rvLy8BABx584dpfI6deoo5lr09/cXAQEBirkHr1+/LszNzRXzF/bp00c0b95cSCQSxf4saNofIio6JoBEpNaTJ09EWFiY6Nevn6hdu7YoW7asMDIyEmXKlBFNmzYVCxYsEGlpaSrLZWdniylTpghXV1dFovj6l3l6err45ptvRNWqVYVUKhWOjo5iyJAhIjExUfj5+alNANPS0sSIESOEo6OjMDIyEgCEl5eXUpvTp0+LFi1aCDMzM2FpaSnat28vYmJi1CYwN2/eFFOmTBFNmzYV9vb2wsTERFSoUEG0bt1a7NixQ+Tl5RVqX/3888+iTp06QiqVinLlyol+/fqJ+Pj4fLcnLi5O9O3bV1SoUEGYmpqK6tWri3nz5omcnByt5lHMzc0VoaGhwtPTU1hZWQmpVCqcnZ1Fr1691M5B+Oeff4rOnTuLsmXLCmNjY1GpUiUxdOhQlWReCO0TwJs3b4pu3bqJsmXLCgMDA5U+rl69Kjp37izKlSsnzMzMRL169cSaNWuEEG+f95GIik4iBG+1IiIiItInvAaQiIiISM8wASQiIiLSM0wAiYiIiPQME0AiIiIiPcMEkIiIiEjPMAEkIiIi0jNMAImIiIj0DBNAIiIiIj3DBJCIiIhIzzABJCIiItIzTACJiIiI9AwTQCIiIiI9wwSQiIiISM/8PxLeesS3YCqRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=df['flChurn'].unique(), y=df['flChurn'].value_counts(dropna=False), data=df, color = '#fe169a') \n",
    "plt.ylabel(\"Número de contas\", labelpad=10, fontsize = 15)\n",
    "plt.xlabel(\"Status da conta\", labelpad=10, fontsize = 15)\n",
    "plt.yticks(fontsize = 12)\n",
    "ax.set_xticklabels([\"Ativa\", \"Inativa\"], fontsize=13)\n",
    "ax.set_title('Número de contas ativas e inativas entre fevereiro e agosto de 2024')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "95b8ce1a-c54b-4174-b98a-c4bf32101134",
     "showTitle": true,
     "title": "Avaliar a atividade das contas ao longo dos meses"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convertendo a coluna dtRef para o tipo correto\n",
    "df['dtRef'] = pd.to_datetime(df['dtRef'])\n",
    "\n",
    "# Agrupando os dados por dtRef e flChurn e contando o número de idCliente\n",
    "df_grouped = df.groupby([df['dtRef'].dt.strftime('%Y-%m-%d'), 'flChurn'])['idCliente'].count().unstack(fill_value=0).reset_index()\n",
    "\n",
    "# Renomeando as colunas para melhor entendimento\n",
    "df_grouped.columns = ['dtRef', 'Ativa', 'Inativa']\n",
    "\n",
    "# Configurando o eixo X para ter as posições dos meses\n",
    "x = np.arange(len(df_grouped['dtRef']))\n",
    "\n",
    "# Largura das barras\n",
    "largura = 0.35\n",
    "\n",
    "# Criando o gráfico de barras duplas\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Barras para contas ativas\n",
    "barras_ativas = ax.bar(x - largura/2, df_grouped['Ativa'], largura, label='Contas ativas', color='#8a3cef')\n",
    "\n",
    "# Barras para contas desistentes\n",
    "barras_desistentes = ax.bar(x + largura/2, df_grouped['Inativa'], largura, label='Contas inativas', color='#fe169a')\n",
    "\n",
    "# Adicionando rótulos e título\n",
    "ax.set_xlabel('Data da safra')\n",
    "ax.set_ylabel('Número de contas')\n",
    "ax.set_title('Contas ativas e inativas por safra')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(df_grouped['dtRef'], rotation=45)\n",
    "ax.legend()\n",
    "\n",
    "# Mostrando o gráfico\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "d8d5644e-01bb-4ffb-9499-c3b39b87eae4",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "A diferença entre o número de contas ativas e inativas em cada janela de observação diminui a cada safra (mês)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "527689b8-ce8f-4686-b2fc-d75a85d1f353",
     "showTitle": true,
     "title": "Analisar a média de transações por dia"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/spark-5a9e4130-8e76-478c-b09a-01/.ipykernel/2185/command-1807406999630052-2905109176:23: UserWarning: FixedFormatter should only be used together with FixedLocator\n  ax.set_xticklabels(dias_semana, fontsize=13)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculando a média das transações por dia agrupadas por flChurn\n",
    "import numpy as np\n",
    "\n",
    "df_mean_transacoes = df.groupby('flChurn')[['nrQtdeTransacaoDay2', 'nrQtdeTransacaoDay3', 'nrQtdeTransacaoDay4', 'nrQtdeTransacaoDay5', 'nrQtdeTransacaoDay6']].mean().transpose()\n",
    "\n",
    "# Configurando o eixo X com os dias\n",
    "x = np.arange(len(df_mean_transacoes.index))\n",
    "\n",
    "# Largura das barras\n",
    "largura = 0.35\n",
    "\n",
    "# Criando o gráfico de barras\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Barras para a média de transações por dia para contas ativas\n",
    "barras_transacoes_ativas = ax.bar(x - largura/2, df_mean_transacoes[0].values, largura, label='Contas ativas', color='#8a3cef')\n",
    "\n",
    "# Barras para a média de transações por dia para contas inativas\n",
    "barras_transacoes_inativas = ax.bar(x + largura/2, df_mean_transacoes[1].values, largura, label='Contas inativas', color='#fe169a')\n",
    "\n",
    "# Substituindo os rótulos dos dias no eixo X\n",
    "dias_semana = ['Dia 2', 'Dia 3', 'Dia 4', 'Dia 5', 'Dia 6']\n",
    "ax.set_xticklabels(dias_semana, fontsize=13)\n",
    "\n",
    "# Adicionando rótulos e título\n",
    "ax.set_xlabel('Dia da transação')\n",
    "ax.set_ylabel('Média de transações')\n",
    "ax.set_title('Média de transações por dia por status da conta')\n",
    "ax.set_xticks(x)\n",
    "ax.legend()\n",
    "\n",
    "# Mostrando o gráfico\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "ebc1160f-c62d-4e8a-ad12-dd0b6b08559a",
     "showTitle": true,
     "title": "Analisar a média de pontos por dia de contas ativas e inativas"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Calculating the count of nrPontosDia grouped by flChurn\n",
    "df_count = df.groupby(['nrPontosDia', 'flChurn']).size().reset_index(name='count')\n",
    "\n",
    "# Creating the scatter plot for each churn category with updated labels and colors\n",
    "for churn_status, group in df_count.groupby('flChurn'):\n",
    "    if churn_status == 1:\n",
    "        label = 'Inativa'\n",
    "        color = '#fe169a'\n",
    "    else:\n",
    "        label = 'Ativa'\n",
    "        color = '#8a3cef'\n",
    "    plt.scatter(group['nrPontosDia'], group['count'], label=label, color=color)\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('Número médio de pontos por dia')\n",
    "plt.ylabel('Número de contas')\n",
    "plt.title('Número de contas por média de pontos por dia')\n",
    "plt.legend()\n",
    "\n",
    "# Displaying the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "0ff9c491-42be-4398-a038-52349c554110",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Calculating the count of nrRecenciaDias grouped by flChurn\n",
    "df_count = df.groupby(['nrRecenciaDias', 'flChurn']).size().reset_index(name='count')\n",
    "\n",
    "# Creating the scatter plot for each churn category with updated labels and colors\n",
    "for churn_status, group in df_count.groupby('flChurn'):\n",
    "    if churn_status == 1:\n",
    "        label = 'Inativa'\n",
    "        color = '#fe169a'\n",
    "    else:\n",
    "        label = 'Ativa'\n",
    "        color = '#8a3cef'\n",
    "    plt.scatter(group['nrRecenciaDias'], group['count'], label=label, color=color)\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('Número de dias desde a última transação')\n",
    "plt.ylabel('Número de contas')\n",
    "plt.title('Número de contas por recência de transação')\n",
    "plt.legend()\n",
    "\n",
    "# Displaying the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "89fe4e73-09a4-4e95-ae55-beeb95795adf",
     "showTitle": true,
     "title": "Média de pontos positivos e negativos para contas ativas e inativas"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flChurn                    0           1\nnrTicketMedioPos   24.343433   26.411835\nnrTicketMedioNeg  -86.608578   -3.764822\nnrSomaPontosPos   488.429924  104.489130\nnrSomaPontosNeg  -180.018939   -7.559289\n"
     ]
    }
   ],
   "source": [
    "grouped = df.groupby('flChurn')[['nrTicketMedioPos', 'nrTicketMedioNeg', 'nrSomaPontosPos', 'nrSomaPontosNeg']].mean()\n",
    "result = grouped.transpose()\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "88adb887-487e-4ed8-a689-6269199c5141",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "O número de pontos positivos por transação (nrTicketMedioPos) é similar entre contas ativas e inativas (0 e 1, respectivamente). Entretanto, observa-se que o ticket médio negativo é muito menor para contas inativas.\n",
    "Nas contas ativas, a média do número total de pontos negativos é cerca de 2,71 vezes maior que a média do número total de pontos positivos.\n",
    "Já nas contas inativas, essa diferença é de cerca de 13,75.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "b4703dfd-085f-4eb0-bed8-768f6df9c712",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "# Analytical Base Table (ABT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "02e91107-251b-4362-9f6b-e7c0f67496d2",
     "showTitle": true,
     "title": "Remover variáveis relacionadas a outras variáveis"
    }
   },
   "outputs": [],
   "source": [
    "df = df.drop(columns=['nrSomaPontos', 'nrTicketMedio','nrQtdeTransacoes'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "e2c1ee41-8894-4b2b-8e6a-b882fe2c3ec0",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "Podemos remover variáveis com baixa variabilidade, uma vez que elas não contribuem significativamente para a análise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "08685f37-744c-4975-8732-24af6b5ee387",
     "showTitle": true,
     "title": "Verificar a variabilidade das variáveis"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/spark-5a9e4130-8e76-478c-b09a-01/.ipykernel/2185/command-428473906446192-4093858002:1: FutureWarning: The default value of numeric_only in DataFrame.var is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n  df.var() # Calculando a variância\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "flChurn                     0.225453\n",
       "nrSomaPontosPos        246445.525381\n",
       "nrSomaPontosNeg        153870.117212\n",
       "nrTicketMedioPos          556.127931\n",
       "nrTicketMedioNeg        28479.646656\n",
       "nrPontosDia              3799.625247\n",
       "nrQtdeDias                 13.990726\n",
       "nrRecenciaDias             76.840374\n",
       "nrQtdeTransacaoDay2       209.486378\n",
       "nrQtdeTransacaoDay3       234.904966\n",
       "nrQtdeTransacaoDay4       151.912088\n",
       "nrQtdeTransacaoDay5       138.474838\n",
       "nrQtdeTransacaoDay6       310.590643\n",
       "nrQtdeDay2                  0.820361\n",
       "nrQtdeDay3                  0.849738\n",
       "nrQtdeDay4                  0.777995\n",
       "nrQtdeDay5                  0.781963\n",
       "nrQtdeDay6                  0.770537\n",
       "nrAvgRecorrencia           12.710083\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.var() # Calculando a variância"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "e42624a1-e3d9-4087-9276-30f20eef57a9",
     "showTitle": true,
     "title": "Remover variáveis com variância muito baixa"
    }
   },
   "outputs": [],
   "source": [
    "# Remover colunas com variância muito baixa\n",
    "df = df.drop(columns=['nrQtdeDay2', 'nrQtdeDay3', 'nrQtdeDay4', 'nrQtdeDay5', 'nrQtdeDay6'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "c7dedbcf-66b3-46e0-b42b-fa07e5ccefe5",
     "showTitle": true,
     "title": "Remover variável com valores nulos"
    }
   },
   "outputs": [],
   "source": [
    "df = df.drop(columns=['nrAvgRecorrencia'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "e5cfccb9-1926-4679-9eb4-e074b4017e26",
     "showTitle": true,
     "title": "Analisar correlação entre variáveis"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_num = df.drop(columns=['dtRef','idCliente','flChurn']) # Separando somente as variáveis quantitativas do banco de dados\n",
    "corr = df_num.corr() # Criando a matriz de correlação    \n",
    "mask = np.triu(np.ones_like(corr, dtype=bool)) # Criando uma máscara que indica os valores correlacionados\n",
    "f, ax = plt.subplots(figsize=(20, 11))          # Criando uma figura para plotar os valores\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True) # Definindo a paleta de cores\n",
    "sns.heatmap (corr, mask=mask, cmap='vlag', vmax=1, center=0, # Plotando a matriz de correlação\n",
    "             square=True, linewidth=.5, cbar_kws={'shrink':.8}, annot=True, fmt= \".2f\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "3e4ea172-5dd6-4c70-abce-a4fad44fe64c",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "A quantidade de transações em um dia da semana está positivamente correlacionada com a quantidade de transações em qualquer outro dia da semana.\n",
    "Isto indica quanto maior o número de transações em um dia, maior o número de transações em qualquer outro dia da semana."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "009c874f-6585-40b9-8932-da159ed6814b",
     "showTitle": true,
     "title": "Tabela resultante após seleção de variáveis"
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "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>dtRef</th>\n",
       "      <th>idCliente</th>\n",
       "      <th>flChurn</th>\n",
       "      <th>nrSomaPontosPos</th>\n",
       "      <th>nrSomaPontosNeg</th>\n",
       "      <th>nrTicketMedioPos</th>\n",
       "      <th>nrTicketMedioNeg</th>\n",
       "      <th>nrPontosDia</th>\n",
       "      <th>nrQtdeDias</th>\n",
       "      <th>nrRecenciaDias</th>\n",
       "      <th>nrQtdeTransacaoDay2</th>\n",
       "      <th>nrQtdeTransacaoDay3</th>\n",
       "      <th>nrQtdeTransacaoDay4</th>\n",
       "      <th>nrQtdeTransacaoDay5</th>\n",
       "      <th>nrQtdeTransacaoDay6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>000ff655-fa9f-4baa-a108-47f581ec52a1</td>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>37.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>001749bd-37b5-4b1e-8111-f9fbba90f530</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>0019bb9e-26d4-4ebf-8727-fc911ea28a92</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-02-01</td>\n",
       "      <td>0097ab76-4637-4ece-8ebc-ab6abd61d662</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>00a6d4c8-38e5-4e92-981e-4e4ac5084546</td>\n",
       "      <td>0</td>\n",
       "      <td>3312</td>\n",
       "      <td>-2000</td>\n",
       "      <td>97.411765</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>93.714286</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dtRef  ... nrQtdeTransacaoDay6\n",
       "0 2024-06-01  ...                   1\n",
       "1 2024-08-01  ...                   0\n",
       "2 2024-07-01  ...                   0\n",
       "3 2024-02-01  ...                   0\n",
       "4 2024-08-01  ...                   8\n",
       "\n",
       "[5 rows x 15 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "75ad77fa-b2dd-4c3e-87d0-71bb81afbbb7",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "# Árvore de Decisão "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "6594a31c-44dc-476a-a0d7-c91be3cb1fd1",
     "showTitle": true,
     "title": "Definir a variável alvo para treinar o modelo"
    }
   },
   "outputs": [],
   "source": [
    "target = 'flChurn' \n",
    "features = df.columns.tolist()[3:]\n",
    "X = df[features]\n",
    "y = df[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "99d6c2a8-2333-4117-9253-ea9d77219911",
     "showTitle": true,
     "title": "Separar os dados em treino - validação - teste (80 - 10 - 10)"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taxa resposta treino: 0.6550324675324676\nTaxa resposta test: 0.6655844155844156\n"
     ]
    }
   ],
   "source": [
    "# Separando Treino e teste (80/20)\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=43)\n",
    "print(\"Taxa resposta treino:\", y_train.mean())\n",
    "print(\"Taxa resposta test:\", y_test.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "d40420ce-5f75-4501-8c9f-488436dc9678",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "Treinando e validando o modelo com profundidade 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "f42d0067-71f3-4f67-bbf8-bcbceeb99700",
     "showTitle": true,
     "title": "Max depth = 10"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/databricks/python/lib/python3.10/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but IterativeImputer was fitted without feature names\n  warnings.warn(\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia no conjunto de treino: 0.92\nAcurácia no conjunto de validação: 0.75\n"
     ]
    }
   ],
   "source": [
    "# Criando um objeto imputer com IterativeImputer\n",
    "imputer = IterativeImputer(max_iter=10, random_state=4)\n",
    "\n",
    "# Ajustar o imputer aos dados de treinamento e transformá-los para preencher valores NaN\n",
    "X_train_imputed = imputer.fit_transform(X_train)\n",
    "X_val_imputed = imputer.transform(X_test)  # Transformar dados de validação usando o mesmo imputer\n",
    "\n",
    "# Listas para armazenar as acurácias\n",
    "train_scores = []\n",
    "val_scores = []\n",
    "\n",
    "# Laço para treinar o modelo e registrar os resultados\n",
    "values = range(1, 11)\n",
    "\n",
    "for i in values:\n",
    "    model = DecisionTreeClassifier(max_depth=10, random_state=4)  # Ajustando a profundidade da árvore para i\n",
    "    model.fit(X_train_imputed, y_train)  # Treinar o modelo com dados imputados\n",
    "\n",
    "    yhat_train = model.predict(X_train_imputed)  # Fazendo previsões com dados de treino\n",
    "    acc_train = accuracy_score(y_train, yhat_train)  # Avaliando a acurácia do modelo\n",
    "    train_scores.append(acc_train)  # Armazenando a acurácia do modelo na lista \n",
    "\n",
    "    yhat_val = model.predict(X_val_imputed)  # Fazendo previsões com dados de validação\n",
    "    acc_val = accuracy_score(y_test, yhat_val)  # Avaliando a acurácia do modelo com dados de validação\n",
    "    val_scores.append(acc_val)  # Armazenando a acurácia do modelo na lista\n",
    "\n",
    "print(f\"Acurácia no conjunto de treino: {acc_train:.2f}\")\n",
    "print(f\"Acurácia no conjunto de validação: {acc_val:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "e96fa492-7ba2-4ee0-8d11-d1f36442db9b",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(values, train_scores, '-o', label=\"Treino\", color='#8a3cef')\n",
    "plt.plot(values, val_scores, '-o', label=\"Validação\", color='#fe169a')\n",
    "plt.xlabel('Profundidade da Árvore')\n",
    "plt.ylabel('Acurácia')\n",
    "plt.title('Acurácia vs. Profundidade da Árvore de Decisão')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "0420955f-455d-4cf5-b106-0888fc9812cb",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "6eef10a9-2b7d-4ba9-8b82-a6b9855f5752",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/databricks/python/lib/python3.10/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but IterativeImputer was fitted without feature names\n  warnings.warn(\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia no conjunto de treino: 0.96\nAcurácia no conjunto de validação: 0.72\n"
     ]
    }
   ],
   "source": [
    "# Criando um objeto imputer com IterativeImputer\n",
    "imputer_forest = IterativeImputer(max_iter=10, random_state=4)\n",
    "\n",
    "# Ajustar o imputer aos dados de treinamento e transformá-los para preencher valores NaN\n",
    "X_train_imputed = imputer_forest.fit_transform(X_train)\n",
    "X_val_imputed = imputer_forest.transform(X_test)  # Transformar dados de validação usando o mesmo imputer\n",
    "\n",
    "# Criando o modelo RandomForestClassifier com 100 estimadores\n",
    "n_estimators_values = [100]  \n",
    "train_scores = []\n",
    "val_scores = []\n",
    "\n",
    "# Criando o modelo RandomForestClassifier com 100 estimadores\n",
    "for n_estimators in n_estimators_values:\n",
    "    model = RandomForestClassifier(n_estimators=n_estimators, random_state=4)\n",
    "    model.fit(X_train_imputed, y_train)  # Treinar o modelo com dados imputados\n",
    "\n",
    "    yhat_train = model.predict(X_train_imputed)  # Fazendo previsões com dados de treino\n",
    "    yhat_val = model.predict(X_val_imputed)  # Fazendo previsões com dados de validação\n",
    "\n",
    "# Avaliando a acurácia do modelo\n",
    "acc_train = accuracy_score(y_train, yhat_train)\n",
    "acc_val = accuracy_score(y_test, yhat_val)\n",
    "# Adicionando as acurácias às listas\n",
    "train_scores.append(acc_train)\n",
    "val_scores.append(acc_val)\n",
    "\n",
    "# Exibir os resultados\n",
    "print(f\"Acurácia no conjunto de treino: {acc_train:.2f}\")\n",
    "print(f\"Acurácia no conjunto de validação: {acc_val:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "c81fa3c5-99f9-4763-8fdf-6dbf9d858cea",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotando o gráfico (Dado que o código está configurado para avaliar um modelo com um número fixo de estimadores (n_estimators=100), o melhor gráfico para mostrar a acurácia do modelo seria um gráfico de barras)\n",
    "labels = ['Treinamento', 'Validação']\n",
    "accuracies = [acc_train, acc_val]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(labels, accuracies, color=['#8a3cef', '#fe169a'])\n",
    "plt.xlabel('100 estimadores')\n",
    "plt.ylabel('Acurácia')\n",
    "plt.title('Acurácia do Modelo Random Forest com 100 Estimadores')\n",
    "plt.ylim(0, 1)  # Limitar o eixo y para 0 a 1 para facilitar a comparação\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "efeba88f-85c4-4d8e-ab3d-0ac7fd3847a5",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "##Outras métricas para a random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "74ba13ff-47a1-43d4-a830-2dc843c731f1",
     "showTitle": true,
     "title": "Matriz de confusão"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de Confusão - Treinamento:\n[[392  33]\n [ 11 796]]\nMatriz de Confusão - Validação:\n[[ 48  55]\n [ 32 173]]\n"
     ]
    }
   ],
   "source": [
    "# matriz de confusão para treino e validação\n",
    "conf_matrix_train = confusion_matrix(y_train, yhat_train)\n",
    "conf_matrix_val = confusion_matrix(y_test, yhat_val)\n",
    "\n",
    "print(\"Matriz de Confusão - Treinamento:\")\n",
    "print(conf_matrix_train)\n",
    "print(\"Matriz de Confusão - Validação:\")\n",
    "print(conf_matrix_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "3f40a17d-193f-4a9d-857c-803761acf5ae",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "A matriz de confusão é uma ferramenta que avalia o desempenho de um modelo de classificação, mostrando a quantidade de previsões corretas e incorretas para cada classe. Ela detalha os verdadeiros positivos, falsos positivos, verdadeiros negativos e falsos negativos, permitindo entender como o modelo está se comportando em relação a cada classe. É útil para identificar onde o modelo está acertando e onde está errando.\n",
    "\n",
    "_Treinamento_:\n",
    "  - Verdadeiros Positivos (TP): 796\n",
    "  - Falsos Positivos (FP): 33\n",
    "  - Falsos Negativos (FN): 11\n",
    "  - Verdadeiros Negativos (TN): 392\n",
    "\n",
    "O modelo tem uma alta taxa de verdadeiros positivos e baixos falsos positivos e negativos, indicando boa performance no treinamento.\n",
    "\n",
    "_Validação_:\n",
    "  - TP: 173\n",
    "  - FP: 55\n",
    "  - FN: 32\n",
    "  - TN: 48\n",
    "\n",
    "\n",
    "No conjunto de validação, há um aumento significativo nos falsos positivos e falsos negativos. Isso sugere que o modelo tem dificuldade em generalizar para novos dados, o que pode ser um problema de overfitting.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "3cdd05f3-e352-4051-a4a7-dfb7a36513ba",
     "showTitle": true,
     "title": "Precisão"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisão no conjunto de treino: 0.96\nPrecisão no conjunto de validação: 0.76\n"
     ]
    }
   ],
   "source": [
    "# precisão para treino e validação\n",
    "from sklearn.metrics import precision_score\n",
    "\n",
    "precision_train = precision_score(y_train, yhat_train)\n",
    "precision_val = precision_score(y_test, yhat_val)\n",
    "\n",
    "print(f\"Precisão no conjunto de treino: {precision_train:.2f}\")\n",
    "print(f\"Precisão no conjunto de validação: {precision_val:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "19006cb7-512f-459c-9e6f-72ee51001d4e",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    " Indica o quão confiável é o modelo quando ele prevê que um evento ocorrerá. Uma alta precisão significa que a maioria das previsões positivas do modelo é correta.\n",
    " \n",
    "_Precisão no Conjunto de Treino: 0.96_: \n",
    " alta precisão no treinamento indica que quando o modelo prevê churn, ele é geralmente correto.\n",
    "\n",
    "_Precisão no Conjunto de Validação: 0.76_: a precisão no conjunto de validação é menor, mas ainda razoável. Isso mostra que o modelo é mais propenso a erros ao prever churn em novos dados.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
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     "inputWidgets": {},
     "nuid": "1b805c10-5a4d-485c-82a3-9403969367a9",
     "showTitle": true,
     "title": "Recall"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall no conjunto de treino: 0.99\nRecall no conjunto de validação: 0.84\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "\n",
    "#recall para treino e validação\n",
    "recall_train = recall_score(y_train, yhat_train)\n",
    "recall_val = recall_score(y_test, yhat_val)\n",
    "\n",
    "print(f\"Recall no conjunto de treino: {recall_train:.2f}\")\n",
    "print(f\"Recall no conjunto de validação: {recall_val:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "title": ""
    }
   },
   "source": [
    "Mede a capacidade do modelo de detectar todos os casos positivos existentes.\n",
    "\n",
    "_Recall no Conjunto de Treino: 0.99_: muito alto, indicando que o modelo identifica quase todos os casos de churn no treinamento.\n",
    "\n",
    "_Recall no Conjunto de Validação: 0.84_:\n",
    "o recall é menor no conjunto de validação, mas ainda bom. Isso indica que o modelo está perdendo alguns casos de churn ao generalizar para novos dados, mas ainda é relativamente eficaz em detectar churn.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
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     },
     "inputWidgets": {},
     "nuid": "baf1b015-788b-42a6-a1e6-04ceaba78bd5",
     "showTitle": true,
     "title": "f1-score"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score no conjunto de treino: 0.97\nF1-Score no conjunto de validação: 0.80\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "# F1-score para treino e validação\n",
    "f1_train = f1_score(y_train, yhat_train)\n",
    "f1_val = f1_score(y_test, yhat_val)\n",
    "\n",
    "print(f\"F1-Score no conjunto de treino: {f1_train:.2f}\")\n",
    "print(f\"F1-Score no conjunto de validação: {f1_val:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
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    }
   },
   "source": [
    "Avalia o equilíbrio entre a capacidade do modelo de identificar corretamente os eventos positivos e minimizar falsos positivos e negativos. \n",
    "\n",
    "_F1-Score no Conjunto de Treino: 0.97_: indica um bom equilíbrio entre precisão e recall.\n",
    "\n",
    "_F1-Score no Conjunto de Validação: 0.80_: é menor no conjunto de validação, refletindo o impacto do desbalanceamento entre precisão e recall na capacidade de prever churn.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
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     },
     "inputWidgets": {},
     "nuid": "e6ef083d-63cc-402e-8501-0bdfa94e08f9",
     "showTitle": true,
     "title": "curva ROC e AUC"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC no conjunto de treino: 0.99\nAUC no conjunto de validação: 0.77\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "# Obter probabilidades de churn\n",
    "y_proba_train = model.predict_proba(X_train_imputed)[:, 1]\n",
    "y_proba_val = model.predict_proba(X_val_imputed)[:, 1]\n",
    "\n",
    "# Calcular a curva ROC e AUC\n",
    "fpr_train, tpr_train, _ = roc_curve(y_train, y_proba_train)\n",
    "fpr_val, tpr_val, _ = roc_curve(y_test, y_proba_val)\n",
    "\n",
    "roc_auc_train = auc(fpr_train, tpr_train)\n",
    "roc_auc_val = auc(fpr_val, tpr_val)\n",
    "\n",
    "print(f\"AUC no conjunto de treino: {roc_auc_train:.2f}\")\n",
    "print(f\"AUC no conjunto de validação: {roc_auc_val:.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
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     "nuid": "774b3265-122f-4d8e-bee4-a710075fd1b0",
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    }
   },
   "source": [
    "A curva ROC (Receiver Operating Characteristic) ilustra a capacidade do modelo de distinguir entre as classes positivas e negativas ao variar o limiar de decisão. A AUC (Área sob a Curva) quantifica essa capacidade, representando a área total sob a curva ROC. Um valor de AUC próximo de 1 indica um excelente desempenho na separação das classes (churn e não-churn).\n",
    "\n",
    "_AUC no Conjunto de Treino: 0.99_\n",
    "\n",
    "_AUC no Conjunto de Validação: 0.77_\n",
    " \n",
    " No treinamento, a AUC alta indica que o modelo tem excelente capacidade de discriminar entre churn e não-churn. No entanto, a AUC no conjunto de validação é menor, sugerindo que a discriminação entre classes não é tão eficaz em novos dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
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     },
     "inputWidgets": {},
     "nuid": "531c3932-e342-4304-922f-04cca6e12c49",
     "showTitle": true,
     "title": "precision-recall"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-PR no conjunto de treino: 0.99\nAUC-PR no conjunto de validação: 0.85\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve, average_precision_score\n",
    "\n",
    "# Calcular curva Precision-Recall e AUC-PR\n",
    "precision_train, recall_train, _ = precision_recall_curve(y_train, y_proba_train)\n",
    "precision_val, recall_val, _ = precision_recall_curve(y_test, y_proba_val)\n",
    "\n",
    "auc_pr_train = average_precision_score(y_train, y_proba_train)\n",
    "auc_pr_val = average_precision_score(y_test, y_proba_val)\n",
    "\n",
    "print(f\"AUC-PR no conjunto de treino: {auc_pr_train:.2f}\")\n",
    "print(f\"AUC-PR no conjunto de validação: {auc_pr_val:.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "13293862-ec04-44db-a765-5e2e5307c676",
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    }
   },
   "source": [
    "A curva Precision-Recall (PR) mostra a relação entre precisão e recall em diferentes limiares de decisão do modelo. A AUC-PR (Área sob a Curva Precision-Recall) quantifica a eficácia do modelo na identificação de casos positivos, especialmente em situações com desbalanceamento de classes.\n",
    "\n",
    "_AUC-PR no Conjunto de Treino: 0.99_\n",
    "\n",
    "_AUC-PR no Conjunto de Validação: 0.85_\n",
    "\n",
    "Alta AUC-PR tanto no treinamento quanto na validação indica que o modelo é bastante eficaz na identificação da classe minoritária (churn), mesmo quando há desbalanceamento de classes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "##Conclusão\n",
    "O modelo Random Forest foi testado para prever quais assinantes vão churnar. Ele teve um ótimo desempenho nos dados usados para treinar o modelo, com 96% de acurácia. No entanto, quando aplicamos o modelo a novos dados, a acurácia caiu para 72%, o que sugere que o modelo pode estar com overfit e não se sair tão bem em situações novas. A precisão e o recall também foram muito bons no treinamento, mas diminuíram na validação. \n",
    "De forma geral, vemos que o modelo identifica bem os assinantes que churnarão, mas precisa de ajustes para melhorar sua performance em dados que ainda não viu."
   ]
  },
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  }
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