analise_transacoes_comerciais.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Etapa 01) Análise exploratória dos dados (DataUnderstanding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando as bibliotecas iniciais para analise\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Time_on_platform</th>\n",
       "      <th>Devices_connected</th>\n",
       "      <th>Subscription_type</th>\n",
       "      <th>Num_streaming_services</th>\n",
       "      <th>Num_active_profiles</th>\n",
       "      <th>Avg_rating</th>\n",
       "      <th>Churned</th>\n",
       "      <th>User_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>4700.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>a271aa56-bcfc-4f0a-91f7-b773de6b86a4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>6107.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>fe14b048-770b-4619-8ec6-485b0969ae31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>6374.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0283dffd-6684-4a05-9c6f-c02098a6849d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c316f2f5-3403-4a0a-82c2-c98e4b3379d2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1948.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14879bcb-1977-4ad8-a7f9-6aa9bf7b872f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Gender  Time_on_platform  Devices_connected Subscription_type  \\\n",
       "0  49.0    Male            4700.0                3.0             Basic   \n",
       "1  69.0  Female            6107.0                2.0          Standard   \n",
       "2  44.0    Male            6374.0                3.0           Premium   \n",
       "3  26.0    Male               NaN                5.0             Basic   \n",
       "4  18.0     NaN            1948.0                3.0             Basic   \n",
       "\n",
       "   Num_streaming_services  Num_active_profiles  Avg_rating  Churned  \\\n",
       "0                     NaN                    3         NaN      0.0   \n",
       "1                     NaN                    1        2.14      0.0   \n",
       "2                     1.0                    1        1.91      1.0   \n",
       "3                     NaN                    2         NaN      NaN   \n",
       "4                     NaN                    3        3.88      0.0   \n",
       "\n",
       "                                User_id  \n",
       "0  a271aa56-bcfc-4f0a-91f7-b773de6b86a4  \n",
       "1  fe14b048-770b-4619-8ec6-485b0969ae31  \n",
       "2  0283dffd-6684-4a05-9c6f-c02098a6849d  \n",
       "3  c316f2f5-3403-4a0a-82c2-c98e4b3379d2  \n",
       "4  14879bcb-1977-4ad8-a7f9-6aa9bf7b872f  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importando DataFrame como df\n",
    "df = pd.read_csv(\"streaming_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Time_on_platform</th>\n",
       "      <th>Devices_connected</th>\n",
       "      <th>Num_streaming_services</th>\n",
       "      <th>Num_active_profiles</th>\n",
       "      <th>Avg_rating</th>\n",
       "      <th>Churned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>66607.000000</td>\n",
       "      <td>56464.000000</td>\n",
       "      <td>52696.000000</td>\n",
       "      <td>9572.000000</td>\n",
       "      <td>77884.000000</td>\n",
       "      <td>60614.000000</td>\n",
       "      <td>61148.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.511072</td>\n",
       "      <td>4385.848505</td>\n",
       "      <td>2.995370</td>\n",
       "      <td>2.496239</td>\n",
       "      <td>3.002273</td>\n",
       "      <td>3.002252</td>\n",
       "      <td>0.243066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.043158</td>\n",
       "      <td>2526.580859</td>\n",
       "      <td>1.414072</td>\n",
       "      <td>1.120700</td>\n",
       "      <td>1.413235</td>\n",
       "      <td>1.154963</td>\n",
       "      <td>0.428938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>2196.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.990000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>4384.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.010000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>6573.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>8759.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age  Time_on_platform  Devices_connected  \\\n",
       "count  66607.000000      56464.000000       52696.000000   \n",
       "mean      43.511072       4385.848505           2.995370   \n",
       "std       15.043158       2526.580859           1.414072   \n",
       "min       18.000000          0.000000           1.000000   \n",
       "25%       30.000000       2196.000000           2.000000   \n",
       "50%       43.000000       4384.000000           3.000000   \n",
       "75%       57.000000       6573.000000           4.000000   \n",
       "max       69.000000       8759.000000           5.000000   \n",
       "\n",
       "       Num_streaming_services  Num_active_profiles    Avg_rating       Churned  \n",
       "count             9572.000000         77884.000000  60614.000000  61148.000000  \n",
       "mean                 2.496239             3.002273      3.002252      0.243066  \n",
       "std                  1.120700             1.413235      1.154963      0.428938  \n",
       "min                  1.000000             1.000000      1.000000      0.000000  \n",
       "25%                  1.000000             2.000000      1.990000      0.000000  \n",
       "50%                  2.000000             3.000000      3.010000      0.000000  \n",
       "75%                  4.000000             4.000000      4.000000      0.000000  \n",
       "max                  4.000000             5.000000      5.000000      1.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descrição Estatística\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 77884 entries, 0 to 77883\n",
      "Data columns (total 10 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Age                     66607 non-null  float64\n",
      " 1   Gender                  63368 non-null  object \n",
      " 2   Time_on_platform        56464 non-null  float64\n",
      " 3   Devices_connected       52696 non-null  float64\n",
      " 4   Subscription_type       52663 non-null  object \n",
      " 5   Num_streaming_services  9572 non-null   float64\n",
      " 6   Num_active_profiles     77884 non-null  int64  \n",
      " 7   Avg_rating              60614 non-null  float64\n",
      " 8   Churned                 61148 non-null  float64\n",
      " 9   User_id                 77884 non-null  object \n",
      "dtypes: float64(6), int64(1), object(3)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# Tipos de dados\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                       11277\n",
       "Gender                    14516\n",
       "Time_on_platform          21420\n",
       "Devices_connected         25188\n",
       "Subscription_type         25221\n",
       "Num_streaming_services    68312\n",
       "Num_active_profiles           0\n",
       "Avg_rating                17270\n",
       "Churned                   16736\n",
       "User_id                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a existência de dados nulos\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quantidade de usúarios por gênero\n",
    "gender = df.groupby(\"Gender\")[\"User_id\"].agg(\"count\").reset_index()\n",
    "sns.barplot(gender, x=\"Gender\", y=\"User_id\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Média de idade por gênero\n",
    "age = df.groupby(\"Gender\")[\"Age\"].agg(\"mean\").reset_index()\n",
    "sns.barplot(age, x=\"Gender\", y=\"Age\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Média de idade por gênero e por tipo de subscrição\n",
    "subs = df.groupby([\"Subscription_type\", \"Gender\"])[\"Age\"].agg(\"mean\").reset_index()\n",
    "sns.barplot(df, x=\"Subscription_type\", y=\"Age\", hue=\"Gender\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Subscription_type', ylabel='User_id'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quantidade de Churned por Subscrição\n",
    "churn = df.groupby([\"Subscription_type\", \"Churned\"])[\"User_id\"].agg(\"count\").reset_index()\n",
    "sns.barplot(churn, x=\"Subscription_type\", y=\"User_id\", hue=\"Churned\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Etapa 02) Tratamento dos Dados (Data Preparation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituindo valores nulos da coluna [Time_on_platform, Num_streaming_services, Churned, Avg_rating, Devices_connected] por 0\n",
    "for c in [\"Time_on_platform\", \"Num_streaming_services\", \"Churned\", \"Avg_rating\", \"Devices_connected\"]:\n",
    "    df[c] = df[c].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropando linhas nulas das colunas [Gender, Subscription_type, Age]\n",
    "df.dropna(subset=[\"Gender\", \"Subscription_type\", \"Age\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Time_on_platform</th>\n",
       "      <th>Devices_connected</th>\n",
       "      <th>Subscription_type</th>\n",
       "      <th>Num_streaming_services</th>\n",
       "      <th>Num_active_profiles</th>\n",
       "      <th>Avg_rating</th>\n",
       "      <th>Churned</th>\n",
       "      <th>User_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>4700.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>a271aa56-bcfc-4f0a-91f7-b773de6b86a4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>6107.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>fe14b048-770b-4619-8ec6-485b0969ae31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>6374.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0283dffd-6684-4a05-9c6f-c02098a6849d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>c316f2f5-3403-4a0a-82c2-c98e4b3379d2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>54.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>3024.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>a1df3a13-9255-4d00-8a9d-20565fefaab9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Gender  Time_on_platform  Devices_connected Subscription_type  \\\n",
       "0  49.0    Male            4700.0                3.0             Basic   \n",
       "1  69.0  Female            6107.0                2.0          Standard   \n",
       "2  44.0    Male            6374.0                3.0           Premium   \n",
       "3  26.0    Male               0.0                5.0             Basic   \n",
       "5  54.0    Male            3024.0                2.0             Basic   \n",
       "\n",
       "   Num_streaming_services  Num_active_profiles  Avg_rating  Churned  \\\n",
       "0                     0.0                    3        0.00      0.0   \n",
       "1                     0.0                    1        2.14      0.0   \n",
       "2                     1.0                    1        1.91      1.0   \n",
       "3                     0.0                    2        0.00      0.0   \n",
       "5                     0.0                    1        0.00      0.0   \n",
       "\n",
       "                                User_id  \n",
       "0  a271aa56-bcfc-4f0a-91f7-b773de6b86a4  \n",
       "1  fe14b048-770b-4619-8ec6-485b0969ae31  \n",
       "2  0283dffd-6684-4a05-9c6f-c02098a6849d  \n",
       "3  c316f2f5-3403-4a0a-82c2-c98e4b3379d2  \n",
       "5  a1df3a13-9255-4d00-8a9d-20565fefaab9  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotando DataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Time_on_platform</th>\n",
       "      <th>Devices_connected</th>\n",
       "      <th>Subscription_type</th>\n",
       "      <th>Num_streaming_services</th>\n",
       "      <th>Num_active_profiles</th>\n",
       "      <th>Avg_rating</th>\n",
       "      <th>Churned</th>\n",
       "      <th>User_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>4700.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>a271aa56-bcfc-4f0a-91f7-b773de6b86a4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>6107.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.14</td>\n",
       "      <td>No</td>\n",
       "      <td>fe14b048-770b-4619-8ec6-485b0969ae31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>6374.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.91</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0283dffd-6684-4a05-9c6f-c02098a6849d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>c316f2f5-3403-4a0a-82c2-c98e4b3379d2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>54.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>3024.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>a1df3a13-9255-4d00-8a9d-20565fefaab9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Gender  Time_on_platform  Devices_connected Subscription_type  \\\n",
       "0  49.0    Male            4700.0                3.0             Basic   \n",
       "1  69.0  Female            6107.0                2.0          Standard   \n",
       "2  44.0    Male            6374.0                3.0           Premium   \n",
       "3  26.0    Male               0.0                5.0             Basic   \n",
       "5  54.0    Male            3024.0                2.0             Basic   \n",
       "\n",
       "   Num_streaming_services  Num_active_profiles  Avg_rating Churned  \\\n",
       "0                     0.0                    3        0.00      No   \n",
       "1                     0.0                    1        2.14      No   \n",
       "2                     1.0                    1        1.91     Yes   \n",
       "3                     0.0                    2        0.00      No   \n",
       "5                     0.0                    1        0.00      No   \n",
       "\n",
       "                                User_id  \n",
       "0  a271aa56-bcfc-4f0a-91f7-b773de6b86a4  \n",
       "1  fe14b048-770b-4619-8ec6-485b0969ae31  \n",
       "2  0283dffd-6684-4a05-9c6f-c02098a6849d  \n",
       "3  c316f2f5-3403-4a0a-82c2-c98e4b3379d2  \n",
       "5  a1df3a13-9255-4d00-8a9d-20565fefaab9  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Substituindo valores 0 e 1 por Yes e No na coluna Churned\n",
    "df[\"Churned\"] = df[\"Churned\"].apply(lambda x: \"No\" if x == 0 else \"Yes\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Time_on_platform</th>\n",
       "      <th>Devices_connected</th>\n",
       "      <th>Subscription_type</th>\n",
       "      <th>Num_streaming_services</th>\n",
       "      <th>Num_active_profiles</th>\n",
       "      <th>Avg_rating</th>\n",
       "      <th>Churned</th>\n",
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       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>4700</td>\n",
       "      <td>3</td>\n",
       "      <td>Basic</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>a271aa56-bcfc-4f0a-91f7-b773de6b86a4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69</td>\n",
       "      <td>Female</td>\n",
       "      <td>6107</td>\n",
       "      <td>2</td>\n",
       "      <td>Standard</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>fe14b048-770b-4619-8ec6-485b0969ae31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44</td>\n",
       "      <td>Male</td>\n",
       "      <td>6374</td>\n",
       "      <td>3</td>\n",
       "      <td>Premium</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0283dffd-6684-4a05-9c6f-c02098a6849d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Basic</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>c316f2f5-3403-4a0a-82c2-c98e4b3379d2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>54</td>\n",
       "      <td>Male</td>\n",
       "      <td>3024</td>\n",
       "      <td>2</td>\n",
       "      <td>Basic</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>a1df3a13-9255-4d00-8a9d-20565fefaab9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Time_on_platform  Devices_connected Subscription_type  \\\n",
       "0   49    Male              4700                  3             Basic   \n",
       "1   69  Female              6107                  2          Standard   \n",
       "2   44    Male              6374                  3           Premium   \n",
       "3   26    Male                 0                  5             Basic   \n",
       "5   54    Male              3024                  2             Basic   \n",
       "\n",
       "   Num_streaming_services  Num_active_profiles  Avg_rating Churned  \\\n",
       "0                       0                    3           0      No   \n",
       "1                       0                    1           2      No   \n",
       "2                       1                    1           1     Yes   \n",
       "3                       0                    2           0      No   \n",
       "5                       0                    1           0      No   \n",
       "\n",
       "                                User_id  \n",
       "0  a271aa56-bcfc-4f0a-91f7-b773de6b86a4  \n",
       "1  fe14b048-770b-4619-8ec6-485b0969ae31  \n",
       "2  0283dffd-6684-4a05-9c6f-c02098a6849d  \n",
       "3  c316f2f5-3403-4a0a-82c2-c98e4b3379d2  \n",
       "5  a1df3a13-9255-4d00-8a9d-20565fefaab9  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformando valores tipo float em tipo int\n",
    "df = df.map(lambda x: int(x) if isinstance(x, float) else x)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Etapa 03) Modelagem dos Dados - Regressão Logística"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separando X e y\n",
    "X = df.drop(columns=[\"Churned\", \"User_id\"]).copy()\n",
    "y = df[[\"Churned\"]].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando LabelEncoder\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Time_on_platform</th>\n",
       "      <th>Devices_connected</th>\n",
       "      <th>Subscription_type</th>\n",
       "      <th>Num_streaming_services</th>\n",
       "      <th>Num_active_profiles</th>\n",
       "      <th>Avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>4700</td>\n",
       "      <td>3</td>\n",
       "      <td>Basic</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69</td>\n",
       "      <td>Female</td>\n",
       "      <td>6107</td>\n",
       "      <td>2</td>\n",
       "      <td>Standard</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44</td>\n",
       "      <td>Male</td>\n",
       "      <td>6374</td>\n",
       "      <td>3</td>\n",
       "      <td>Premium</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Basic</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>54</td>\n",
       "      <td>Male</td>\n",
       "      <td>3024</td>\n",
       "      <td>2</td>\n",
       "      <td>Basic</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Time_on_platform  Devices_connected Subscription_type  \\\n",
       "0   49    Male              4700                  3             Basic   \n",
       "1   69  Female              6107                  2          Standard   \n",
       "2   44    Male              6374                  3           Premium   \n",
       "3   26    Male                 0                  5             Basic   \n",
       "5   54    Male              3024                  2             Basic   \n",
       "\n",
       "   Num_streaming_services  Num_active_profiles  Avg_rating  \n",
       "0                       0                    3           0  \n",
       "1                       0                    1           2  \n",
       "2                       1                    1           1  \n",
       "3                       0                    2           0  \n",
       "5                       0                    1           0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando X\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utilizando do processo de LabelEncoder para transformar a coluna categorica Gender em númerica\n",
    "encoder = LabelEncoder()\n",
    "X[\"Gender\"] = encoder.fit_transform(X[\"Gender\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Gender</th>\n",
       "      <th>Time_on_platform</th>\n",
       "      <th>Devices_connected</th>\n",
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       "      <th>0</th>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>6374</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>3024</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Time_on_platform  Devices_connected  Num_streaming_services  \\\n",
       "0   49       1              4700                  3                       0   \n",
       "1   69       0              6107                  2                       0   \n",
       "2   44       1              6374                  3                       1   \n",
       "3   26       1                 0                  5                       0   \n",
       "5   54       1              3024                  2                       0   \n",
       "\n",
       "   Num_active_profiles  Avg_rating  Subscription_type_Basic  \\\n",
       "0                    3           0                        1   \n",
       "1                    1           2                        0   \n",
       "2                    1           1                        0   \n",
       "3                    2           0                        1   \n",
       "5                    1           0                        1   \n",
       "\n",
       "   Subscription_type_Premium  Subscription_type_Standard  \n",
       "0                          0                           0  \n",
       "1                          0                           1  \n",
       "2                          1                           0  \n",
       "3                          0                           0  \n",
       "5                          0                           0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformando categoricas em númericas\n",
    "X = pd.get_dummies(X, dtype=int)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Gender</th>\n",
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       "      <th>Num_streaming_services</th>\n",
       "      <th>Num_active_profiles</th>\n",
       "      <th>Avg_rating</th>\n",
       "      <th>Subscription_type_Basic</th>\n",
       "      <th>Subscription_type_Premium</th>\n",
       "      <th>Subscription_type_Standard</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.366193</td>\n",
       "      <td>0.994075</td>\n",
       "      <td>0.525837</td>\n",
       "      <td>0.527504</td>\n",
       "      <td>-0.340192</td>\n",
       "      <td>0.004632</td>\n",
       "      <td>-1.359435</td>\n",
       "      <td>1.417502</td>\n",
       "      <td>-0.709388</td>\n",
       "      <td>-0.706468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.691401</td>\n",
       "      <td>-1.005961</td>\n",
       "      <td>1.009474</td>\n",
       "      <td>-0.021766</td>\n",
       "      <td>-0.340192</td>\n",
       "      <td>-1.410847</td>\n",
       "      <td>0.034138</td>\n",
       "      <td>-0.705466</td>\n",
       "      <td>-0.709388</td>\n",
       "      <td>1.415493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.034891</td>\n",
       "      <td>0.994075</td>\n",
       "      <td>1.101252</td>\n",
       "      <td>0.527504</td>\n",
       "      <td>0.759207</td>\n",
       "      <td>-1.410847</td>\n",
       "      <td>-0.662648</td>\n",
       "      <td>-0.705466</td>\n",
       "      <td>1.409665</td>\n",
       "      <td>-0.706468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.157796</td>\n",
       "      <td>0.994075</td>\n",
       "      <td>-1.089724</td>\n",
       "      <td>1.626045</td>\n",
       "      <td>-0.340192</td>\n",
       "      <td>-0.703107</td>\n",
       "      <td>-1.359435</td>\n",
       "      <td>1.417502</td>\n",
       "      <td>-0.709388</td>\n",
       "      <td>-0.706468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.697495</td>\n",
       "      <td>0.994075</td>\n",
       "      <td>-0.050265</td>\n",
       "      <td>-0.021766</td>\n",
       "      <td>-0.340192</td>\n",
       "      <td>-1.410847</td>\n",
       "      <td>-1.359435</td>\n",
       "      <td>1.417502</td>\n",
       "      <td>-0.709388</td>\n",
       "      <td>-0.706468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age    Gender  Time_on_platform  Devices_connected  \\\n",
       "0  0.366193  0.994075          0.525837           0.527504   \n",
       "1  1.691401 -1.005961          1.009474          -0.021766   \n",
       "2  0.034891  0.994075          1.101252           0.527504   \n",
       "3 -1.157796  0.994075         -1.089724           1.626045   \n",
       "4  0.697495  0.994075         -0.050265          -0.021766   \n",
       "\n",
       "   Num_streaming_services  Num_active_profiles  Avg_rating  \\\n",
       "0               -0.340192             0.004632   -1.359435   \n",
       "1               -0.340192            -1.410847    0.034138   \n",
       "2                0.759207            -1.410847   -0.662648   \n",
       "3               -0.340192            -0.703107   -1.359435   \n",
       "4               -0.340192            -1.410847   -1.359435   \n",
       "\n",
       "   Subscription_type_Basic  Subscription_type_Premium  \\\n",
       "0                 1.417502                  -0.709388   \n",
       "1                -0.705466                  -0.709388   \n",
       "2                -0.705466                   1.409665   \n",
       "3                 1.417502                  -0.709388   \n",
       "4                 1.417502                  -0.709388   \n",
       "\n",
       "   Subscription_type_Standard  \n",
       "0                   -0.706468  \n",
       "1                    1.415493  \n",
       "2                   -0.706468  \n",
       "3                   -0.706468  \n",
       "4                   -0.706468  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalizando os dados\n",
    "scaler = StandardScaler()\n",
    "X = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando função train_test_split\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separando dados em treino e test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando a função SMOTE da bibliote imbalaced-learn\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resolvendo problemas de desbalanciamento nos dados\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train, y_train = smote.fit_resample(X_train, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Treinando modelo de LogisticRegressiom\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando ConfusionMatrixDisplay\n",
    "from sklearn.metrics import ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando matrix de confusão\n",
    "ConfusionMatrixDisplay.from_estimator(lr, X_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando métricas\n",
    "from sklearn.metrics import accuracy_score, balanced_accuracy_score, f1_score, roc_auc_score, recall_score, precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acúracia (Treino): 0.5121143972503925\n",
      "Acúracia (Treino): 0.5074626865671642\n",
      "==================================================\n",
      "Acúracia Balanceada (Treino): 0.5121143972503925\n",
      "Acúracia Balanceada (Teste): 0.4996392061597294\n",
      "==================================================\n",
      "Precisão (Treino): 0.5119541096177197\n",
      "Precisão (Teste): 0.19297752808988763\n",
      "==================================================\n",
      "Recall (Treino): 0.5188186871472822\n",
      "Recall (Teste): 0.48688873139617295\n",
      "==================================================\n",
      "F1 Score (Treino): 0.51536354056902\n",
      "F1 Score (Teste): 0.2764031382015691\n",
      "==================================================\n",
      "ROCAUC: 0.5155888721829761\n",
      "ROCAUC: 0.5018337396549177\n"
     ]
    }
   ],
   "source": [
    "# Mostrando as métricas do modelo\n",
    "print(f\"Acúracia (Treino): {accuracy_score(y_train, lr.predict(X_train))}\")\n",
    "print(f\"Acúracia (Treino): {accuracy_score(y_test, lr.predict(X_test))}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Acúracia Balanceada (Treino): {balanced_accuracy_score(y_train, lr.predict(X_train))}\")\n",
    "print(f\"Acúracia Balanceada (Teste): {balanced_accuracy_score(y_test, lr.predict(X_test))}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Precisão (Treino): {precision_score(y_train, lr.predict(X_train), pos_label=\"Yes\")}\")\n",
    "print(f\"Precisão (Teste): {precision_score(y_test, lr.predict(X_test), pos_label=\"Yes\")}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Recall (Treino): {recall_score(y_train, lr.predict(X_train), pos_label=\"Yes\")}\")\n",
    "print(f\"Recall (Teste): {recall_score(y_test, lr.predict(X_test), pos_label=\"Yes\")}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"F1 Score (Treino): {f1_score(y_train, lr.predict(X_train), pos_label=\"Yes\")}\")\n",
    "print(f\"F1 Score (Teste): {f1_score(y_test, lr.predict(X_test), pos_label=\"Yes\")}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"ROCAUC: {roc_auc_score(y_train, lr.predict_proba(X_train)[:, 1])}\")\n",
    "print(f\"ROCAUC: {roc_auc_score(y_test, lr.predict_proba(X_test)[:, 1])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Etapa 04) Modelagem dos Dados - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando RandomForestClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Treinando modelo do RandomForestClassifier\n",
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando matrix de confusão\n",
    "ConfusionMatrixDisplay.from_estimator(rfc, X_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acúracia (Treino): 0.9965205584079433\n",
      "Acúracia (Treino): 0.8095303300013693\n",
      "==================================================\n",
      "Acúracia Balanceada (Treino): 0.9965205584079433\n",
      "Acúracia Balanceada (Teste): 0.628091676638265\n",
      "==================================================\n",
      "Precisão (Treino): 0.9974916032481612\n",
      "Precisão (Teste): 0.5108932461873639\n",
      "==================================================\n",
      "Recall (Treino): 0.9955446174735859\n",
      "Recall (Teste): 0.332388377037562\n",
      "==================================================\n",
      "F1 Score (Treino): 0.996517159361196\n",
      "F1 Score (Teste): 0.4027479604980678\n",
      "==================================================\n",
      "ROCAUC: 0.9998905930531524\n",
      "ROCAUC: 0.6931327803125765\n"
     ]
    }
   ],
   "source": [
    "# Mostrando as métricas do modelo\n",
    "print(f\"Acúracia (Treino): {accuracy_score(y_train, rfc.predict(X_train))}\")\n",
    "print(f\"Acúracia (Treino): {accuracy_score(y_test, rfc.predict(X_test))}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Acúracia Balanceada (Treino): {balanced_accuracy_score(y_train, rfc.predict(X_train))}\")\n",
    "print(f\"Acúracia Balanceada (Teste): {balanced_accuracy_score(y_test, rfc.predict(X_test))}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Precisão (Treino): {precision_score(y_train, rfc.predict(X_train), pos_label=\"Yes\")}\")\n",
    "print(f\"Precisão (Teste): {precision_score(y_test, rfc.predict(X_test), pos_label=\"Yes\")}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Recall (Treino): {recall_score(y_train, rfc.predict(X_train), pos_label=\"Yes\")}\")\n",
    "print(f\"Recall (Teste): {recall_score(y_test, rfc.predict(X_test), pos_label=\"Yes\")}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"F1 Score (Treino): {f1_score(y_train, rfc.predict(X_train), pos_label=\"Yes\")}\")\n",
    "print(f\"F1 Score (Teste): {f1_score(y_test, rfc.predict(X_test), pos_label=\"Yes\")}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"ROCAUC: {roc_auc_score(y_train, rfc.predict_proba(X_train)[:, 1])}\")\n",
    "print(f\"ROCAUC: {roc_auc_score(y_test, rfc.predict_proba(X_test)[:, 1])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Etapa 05) Modelagem dos Dados - Tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando GridSearchCV\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definindo lista de hyperparametros\n",
    "parameters = {\n",
    "    \"n_estimators\": [200, 300],\n",
    "    \"min_samples_split\": [2, 5],\n",
    "    \"min_samples_leaf\": [1, 2, 4]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inicializando o GridSearchCV\n",
    "search = GridSearchCV(rfc, param_grid=parameters, scoring=\"accuracy\", cv=5, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5],\n",
       "                         &#x27;n_estimators&#x27;: [200, 300]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5],\n",
       "                         &#x27;n_estimators&#x27;: [200, 300]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5],\n",
       "                         'n_estimators': [200, 300]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando o melhores hyperparametros para o modelo\n",
    "search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'monotonic_cst': None,\n",
       " 'n_estimators': 200,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melhores hyperparametros\n",
    "search.best_estimator_.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recriando modelo tunado\n",
    "model = RandomForestClassifier(\n",
    "    bootstrap=True,\n",
    "    ccp_alpha=0.0,\n",
    "    class_weight=None,\n",
    "    criterion='gini',\n",
    "    max_depth=20,\n",
    "    max_features='sqrt',\n",
    "    max_leaf_nodes=None,\n",
    "    max_samples=None,\n",
    "    min_impurity_decrease=0.0,\n",
    "    min_samples_leaf=1,\n",
    "    min_samples_split=2,\n",
    "    min_weight_fraction_leaf=0.0,\n",
    "    monotonic_cst=None,\n",
    "    n_estimators=200,\n",
    "    n_jobs=None,\n",
    "    oob_score=False,\n",
    "    random_state=None,\n",
    "    verbose=0,\n",
    "    warm_start=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=20, n_estimators=200)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=20, n_estimators=200)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=20, n_estimators=200)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Treinando modelo tunado\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acúracia (Treino): 0.9671150337336105\n",
      "Acúracia (Treino): 0.8073394495412844\n",
      "==================================================\n",
      "Acúracia Balanceada (Treino): 0.9671150337336105\n",
      "Acúracia Balanceada (Teste): 0.602209605163195\n",
      "==================================================\n",
      "Precisão (Treino): 0.992836996910955\n",
      "Precisão (Teste): 0.5026595744680851\n",
      "==================================================\n",
      "Recall (Treino): 0.9410192217931854\n",
      "Recall (Teste): 0.2678951098511694\n",
      "==================================================\n",
      "F1 Score (Treino): 0.966233879400488\n",
      "F1 Score (Teste): 0.34951456310679613\n",
      "==================================================\n",
      "ROCAUC: 0.9978054044957576\n",
      "ROCAUC: 0.6548634937497685\n"
     ]
    }
   ],
   "source": [
    "# Mostrando as métricas do modelo\n",
    "print(f\"Acúracia (Treino): {accuracy_score(y_train, model.predict(X_train))}\")\n",
    "print(f\"Acúracia (Treino): {accuracy_score(y_test, model.predict(X_test))}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Acúracia Balanceada (Treino): {balanced_accuracy_score(y_train, model.predict(X_train))}\")\n",
    "print(f\"Acúracia Balanceada (Teste): {balanced_accuracy_score(y_test, model.predict(X_test))}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Precisão (Treino): {precision_score(y_train, model.predict(X_train), pos_label=\"Yes\")}\")\n",
    "print(f\"Precisão (Teste): {precision_score(y_test, model.predict(X_test), pos_label=\"Yes\")}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Recall (Treino): {recall_score(y_train, model.predict(X_train), pos_label=\"Yes\")}\")\n",
    "print(f\"Recall (Teste): {recall_score(y_test, model.predict(X_test), pos_label=\"Yes\")}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"F1 Score (Treino): {f1_score(y_train, model.predict(X_train), pos_label=\"Yes\")}\")\n",
    "print(f\"F1 Score (Teste): {f1_score(y_test, model.predict(X_test), pos_label=\"Yes\")}\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"ROCAUC: {roc_auc_score(y_train, model.predict_proba(X_train)[:, 1])}\")\n",
    "print(f\"ROCAUC: {roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando matrix de confusão para modelo tunado\n",
    "ConfusionMatrixDisplay.from_estimator(model, X_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dnc",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}