pred-3_extremos-estend.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Terceira abordagem de predição: extremos estendidos\n",
    "\n",
    "Treino com os extremos e teste em todos:\n",
    "- com equilíbrio de amostras\n",
    "- com validação cruzada \n",
    "- com otimização de parâmetros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bibliotecas e Configurações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importação dos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>ANF</th>\n",
       "      <th>USERVALUEQ1_LABEL</th>\n",
       "      <th>USERVALUEQ2_VALUE</th>\n",
       "      <th>vol_Rede_Social</th>\n",
       "      <th>vol_Video</th>\n",
       "      <th>vol_Comunicacao</th>\n",
       "      <th>vol_Loja_de_Apps</th>\n",
       "      <th>vol_Musica</th>\n",
       "      <th>vol_Google</th>\n",
       "      <th>vol_Navegacao</th>\n",
       "      <th>...</th>\n",
       "      <th>term_fabr_motorola</th>\n",
       "      <th>term_fabr_não informado</th>\n",
       "      <th>term_fabr_outros</th>\n",
       "      <th>term_fabr_samsung</th>\n",
       "      <th>term_fabr_xiaomi</th>\n",
       "      <th>device_tech_2G</th>\n",
       "      <th>device_tech_3G</th>\n",
       "      <th>device_tech_4G</th>\n",
       "      <th>device_tech_5G</th>\n",
       "      <th>Qtd_CRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>8</td>\n",
       "      <td>Não Informado</td>\n",
       "      <td>8.111028</td>\n",
       "      <td>6.282269</td>\n",
       "      <td>5.894406</td>\n",
       "      <td>5.347112</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>3.295874</td>\n",
       "      <td>3.295874</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>Plano/Bonus</td>\n",
       "      <td>7.180832</td>\n",
       "      <td>5.153297</td>\n",
       "      <td>5.283209</td>\n",
       "      <td>1.946053</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "      <td>Velocidade</td>\n",
       "      <td>8.094379</td>\n",
       "      <td>5.846442</td>\n",
       "      <td>6.682110</td>\n",
       "      <td>2.890427</td>\n",
       "      <td>4.844195</td>\n",
       "      <td>1.609638</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>81</td>\n",
       "      <td>10</td>\n",
       "      <td>Outros</td>\n",
       "      <td>4.897847</td>\n",
       "      <td>5.771444</td>\n",
       "      <td>1.386544</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>10</td>\n",
       "      <td>Sinal/Cobertura</td>\n",
       "      <td>3.784212</td>\n",
       "      <td>3.434019</td>\n",
       "      <td>5.918897</td>\n",
       "      <td>1.791926</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>1.386544</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45201</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>Sinal/Cobertura</td>\n",
       "      <td>5.262695</td>\n",
       "      <td>5.398167</td>\n",
       "      <td>3.828663</td>\n",
       "      <td>5.075180</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45202</th>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sinal/Cobertura</td>\n",
       "      <td>7.978654</td>\n",
       "      <td>6.054442</td>\n",
       "      <td>7.756196</td>\n",
       "      <td>3.912043</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>3.401231</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45203</th>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>Duracao pacote dados</td>\n",
       "      <td>3.434019</td>\n",
       "      <td>5.332724</td>\n",
       "      <td>4.248510</td>\n",
       "      <td>1.386544</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45204</th>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>Sinal/Cobertura</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>5.159061</td>\n",
       "      <td>1.386544</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45205</th>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>Duracao pacote dados</td>\n",
       "      <td>2.639129</td>\n",
       "      <td>3.637612</td>\n",
       "      <td>2.890427</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45206 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ANF  USERVALUEQ1_LABEL     USERVALUEQ2_VALUE  vol_Rede_Social  \\\n",
       "0       51                  8         Não Informado         8.111028   \n",
       "1       11                  9           Plano/Bonus         7.180832   \n",
       "2       37                 10            Velocidade         8.094379   \n",
       "3       81                 10                Outros         4.897847   \n",
       "4       85                 10       Sinal/Cobertura         3.784212   \n",
       "...    ...                ...                   ...              ...   \n",
       "45201   48                  0       Sinal/Cobertura         5.262695   \n",
       "45202   35                  7       Sinal/Cobertura         7.978654   \n",
       "45203   43                 10  Duracao pacote dados         3.434019   \n",
       "45204   42                 10       Sinal/Cobertura        -6.907755   \n",
       "45205   22                  9  Duracao pacote dados         2.639129   \n",
       "\n",
       "       vol_Video  vol_Comunicacao  vol_Loja_de_Apps  vol_Musica  vol_Google  \\\n",
       "0       6.282269         5.894406          5.347112   -6.907755    3.295874   \n",
       "1       5.153297         5.283209          1.946053   -6.907755   -6.907755   \n",
       "2       5.846442         6.682110          2.890427    4.844195    1.609638   \n",
       "3       5.771444         1.386544         -6.907755   -6.907755    0.001000   \n",
       "4       3.434019         5.918897          1.791926   -6.907755    1.386544   \n",
       "...          ...              ...               ...         ...         ...   \n",
       "45201   5.398167         3.828663          5.075180   -6.907755    0.001000   \n",
       "45202   6.054442         7.756196          3.912043   -6.907755    3.401231   \n",
       "45203   5.332724         4.248510          1.386544   -6.907755   -6.907755   \n",
       "45204   5.159061         1.386544         -6.907755   -6.907755   -6.907755   \n",
       "45205   3.637612         2.890427         -6.907755   -6.907755   -6.907755   \n",
       "\n",
       "       vol_Navegacao  ...  term_fabr_motorola  term_fabr_não informado  \\\n",
       "0           3.295874  ...                   0                        0   \n",
       "1          -6.907755  ...                   0                        0   \n",
       "2          -6.907755  ...                   1                        0   \n",
       "3          -6.907755  ...                   0                        0   \n",
       "4          -6.907755  ...                   0                        1   \n",
       "...              ...  ...                 ...                      ...   \n",
       "45201      -6.907755  ...                   0                        0   \n",
       "45202      -6.907755  ...                   0                        0   \n",
       "45203      -6.907755  ...                   1                        0   \n",
       "45204      -6.907755  ...                   0                        0   \n",
       "45205      -6.907755  ...                   1                        0   \n",
       "\n",
       "       term_fabr_outros  term_fabr_samsung  term_fabr_xiaomi  device_tech_2G  \\\n",
       "0                     0                  0                 0               0   \n",
       "1                     0                  1                 0               0   \n",
       "2                     0                  0                 0               0   \n",
       "3                     1                  0                 0               0   \n",
       "4                     0                  0                 0               0   \n",
       "...                 ...                ...               ...             ...   \n",
       "45201                 0                  0                 1               0   \n",
       "45202                 0                  1                 0               0   \n",
       "45203                 0                  0                 0               0   \n",
       "45204                 0                  1                 0               0   \n",
       "45205                 0                  0                 0               0   \n",
       "\n",
       "       device_tech_3G  device_tech_4G  device_tech_5G  Qtd_CRM  \n",
       "0                   0               1               0        0  \n",
       "1                   0               1               0        0  \n",
       "2                   0               1               0        0  \n",
       "3                   1               0               0        0  \n",
       "4                   0               1               0        0  \n",
       "...               ...             ...             ...      ...  \n",
       "45201               0               1               0        0  \n",
       "45202               0               1               0        0  \n",
       "45203               0               0               1        0  \n",
       "45204               1               0               0        0  \n",
       "45205               0               1               0        0  \n",
       "\n",
       "[45206 rows x 62 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('NPS_Complete_UFF_2022-07-18_2022-12-31_extra-tratado.csv', sep=';')\n",
    "\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notas atribuídas (distribuição)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    18937\n",
       "8      5477\n",
       "9      4578\n",
       "0      4099\n",
       "7      2879\n",
       "5      2756\n",
       "6      1684\n",
       "1      1438\n",
       "3      1202\n",
       "2      1147\n",
       "4      1009\n",
       "Name: USERVALUEQ1_LABEL, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['USERVALUEQ1_LABEL'].value_counts() # divisão de clientes por nota atribuída"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint, uniform\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    13278\n",
       "9      3179\n",
       "0      2896\n",
       "1       992\n",
       "Name: USERVALUEQ1_LABEL, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Amostrar 70% para treino e 30% para teste\n",
    "df_train = df.sample(frac=0.7, random_state=1)\n",
    "df_test = df.drop(df_train.index)\n",
    "\n",
    "# Vamos treinar apenas com as notas 0, 1, 9 e 10\n",
    "df_train = df_train[df_train['USERVALUEQ1_LABEL'].isin([0, 1, 9, 10])]\n",
    "df_train['USERVALUEQ1_LABEL'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     992\n",
       "1     992\n",
       "9     992\n",
       "10    992\n",
       "Name: USERVALUEQ1_LABEL, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Balancear o dataset de treino para ter a mesma quantidade de amostras de cada nota\n",
    "df_train = df_train.groupby('USERVALUEQ1_LABEL').apply(lambda x: x.sample(n=992, random_state=1)).reset_index(drop=True)\n",
    "# Quantas amostras temos agora?\n",
    "df_train['USERVALUEQ1_LABEL'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separar os dados de treino em X e y\n",
    "X_train = df_train.drop(columns=['USERVALUEQ1_LABEL', 'USERVALUEQ2_VALUE', 'NPS_Class_Detrator', 'NPS_Class_Promotor', 'NPS_Class_Passivo'])\n",
    "y_train = df_train['NPS_Class_Detrator']\n",
    "\n",
    "# Separar os dados de teste em X e y\n",
    "X_test = df_test.drop(columns=['USERVALUEQ1_LABEL', 'USERVALUEQ2_VALUE', 'NPS_Class_Detrator', 'NPS_Class_Promotor', 'NPS_Class_Passivo'])\n",
    "y_test = df_test['NPS_Class_Detrator']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>ANF</th>\n",
       "      <th>vol_Rede_Social</th>\n",
       "      <th>vol_Video</th>\n",
       "      <th>vol_Comunicacao</th>\n",
       "      <th>vol_Loja_de_Apps</th>\n",
       "      <th>vol_Musica</th>\n",
       "      <th>vol_Google</th>\n",
       "      <th>vol_Navegacao</th>\n",
       "      <th>vol_InternetBank</th>\n",
       "      <th>vol_Transporte</th>\n",
       "      <th>...</th>\n",
       "      <th>term_fabr_motorola</th>\n",
       "      <th>term_fabr_não informado</th>\n",
       "      <th>term_fabr_outros</th>\n",
       "      <th>term_fabr_samsung</th>\n",
       "      <th>term_fabr_xiaomi</th>\n",
       "      <th>device_tech_2G</th>\n",
       "      <th>device_tech_3G</th>\n",
       "      <th>device_tech_4G</th>\n",
       "      <th>device_tech_5G</th>\n",
       "      <th>Qtd_CRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>6.603945</td>\n",
       "      <td>2.772651</td>\n",
       "      <td>5.273005</td>\n",
       "      <td>1.098946</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>1.609638</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>3.332240</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73</td>\n",
       "      <td>7.223296</td>\n",
       "      <td>4.605180</td>\n",
       "      <td>5.187391</td>\n",
       "      <td>1.098946</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>0.693647</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>6.736968</td>\n",
       "      <td>4.934481</td>\n",
       "      <td>5.323015</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>4.043069</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>5.010642</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>3.637612</td>\n",
       "      <td>2.484990</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>3.044570</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>8.281724</td>\n",
       "      <td>8.322637</td>\n",
       "      <td>8.223896</td>\n",
       "      <td>5.214941</td>\n",
       "      <td>2.484990</td>\n",
       "      <td>2.833272</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>2.995782</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3963</th>\n",
       "      <td>71</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3964</th>\n",
       "      <td>44</td>\n",
       "      <td>8.019613</td>\n",
       "      <td>1.609638</td>\n",
       "      <td>5.590991</td>\n",
       "      <td>1.791926</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>3.688904</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3965</th>\n",
       "      <td>11</td>\n",
       "      <td>7.590347</td>\n",
       "      <td>7.507142</td>\n",
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       "      <td>4.836290</td>\n",
       "      <td>-6.907755</td>\n",
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       "      <td>0.001000</td>\n",
       "      <td>2.639129</td>\n",
       "      <td>2.302685</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3966</th>\n",
       "      <td>44</td>\n",
       "      <td>2.833272</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>4.564359</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3967</th>\n",
       "      <td>31</td>\n",
       "      <td>6.075348</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3968 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ANF  vol_Rede_Social  vol_Video  vol_Comunicacao  vol_Loja_de_Apps  \\\n",
       "0      21         6.603945   2.772651         5.273005          1.098946   \n",
       "1      73         7.223296   4.605180         5.187391          1.098946   \n",
       "2      11         6.736968   4.934481         5.323015          0.001000   \n",
       "3      32         5.010642  -6.907755         3.637612          2.484990   \n",
       "4      61         8.281724   8.322637         8.223896          5.214941   \n",
       "...   ...              ...        ...              ...               ...   \n",
       "3963   71        -6.907755  -6.907755        -6.907755         -6.907755   \n",
       "3964   44         8.019613   1.609638         5.590991          1.791926   \n",
       "3965   11         7.590347   7.507142         5.627625          4.836290   \n",
       "3966   44         2.833272  -6.907755         4.564359         -6.907755   \n",
       "3967   31         6.075348   3.332240         3.761223         -6.907755   \n",
       "\n",
       "      vol_Musica  vol_Google  vol_Navegacao  vol_InternetBank  vol_Transporte  \\\n",
       "0      -6.907755    1.609638      -6.907755          3.332240       -6.907755   \n",
       "1      -6.907755    0.693647      -6.907755          0.001000       -6.907755   \n",
       "2      -6.907755    4.043069      -6.907755         -6.907755        0.001000   \n",
       "3      -6.907755    3.044570      -6.907755         -6.907755       -6.907755   \n",
       "4       2.484990    2.833272       0.001000          2.995782       -6.907755   \n",
       "...          ...         ...            ...               ...             ...   \n",
       "3963   -6.907755   -6.907755      -6.907755         -6.907755       -6.907755   \n",
       "3964   -6.907755    0.001000      -6.907755          3.688904       -6.907755   \n",
       "3965   -6.907755    2.708117       0.001000          2.639129        2.302685   \n",
       "3966   -6.907755   -6.907755      -6.907755         -6.907755       -6.907755   \n",
       "3967    1.098946    0.693647      -6.907755         -6.907755       -6.907755   \n",
       "\n",
       "      ...  term_fabr_motorola  term_fabr_não informado  term_fabr_outros  \\\n",
       "0     ...                   0                        0                 0   \n",
       "1     ...                   1                        0                 0   \n",
       "2     ...                   1                        0                 0   \n",
       "3     ...                   1                        0                 0   \n",
       "4     ...                   1                        0                 0   \n",
       "...   ...                 ...                      ...               ...   \n",
       "3963  ...                   1                        0                 0   \n",
       "3964  ...                   0                        0                 0   \n",
       "3965  ...                   1                        0                 0   \n",
       "3966  ...                   0                        0                 0   \n",
       "3967  ...                   0                        1                 0   \n",
       "\n",
       "      term_fabr_samsung  term_fabr_xiaomi  device_tech_2G  device_tech_3G  \\\n",
       "0                     1                 0               0               0   \n",
       "1                     0                 0               0               0   \n",
       "2                     0                 0               0               0   \n",
       "3                     0                 0               0               0   \n",
       "4                     0                 0               0               0   \n",
       "...                 ...               ...             ...             ...   \n",
       "3963                  0                 0               0               0   \n",
       "3964                  1                 0               0               0   \n",
       "3965                  0                 0               0               0   \n",
       "3966                  0                 0               0               0   \n",
       "3967                  0                 0               0               0   \n",
       "\n",
       "      device_tech_4G  device_tech_5G  Qtd_CRM  \n",
       "0                  1               0        0  \n",
       "1                  1               0        0  \n",
       "2                  0               1        0  \n",
       "3                  1               0        0  \n",
       "4                  1               0        0  \n",
       "...              ...             ...      ...  \n",
       "3963               1               0        0  \n",
       "3964               1               0        0  \n",
       "3965               0               1        0  \n",
       "3966               1               0        0  \n",
       "3967               0               1        0  \n",
       "\n",
       "[3968 rows x 57 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       1\n",
       "       ..\n",
       "3963    0\n",
       "3964    0\n",
       "3965    0\n",
       "3966    0\n",
       "3967    0\n",
       "Name: NPS_Class_Detrator, Length: 3968, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'objective': 'binary:hinge',\n",
       " 'use_label_encoder': None,\n",
       " 'base_score': None,\n",
       " 'booster': None,\n",
       " 'callbacks': None,\n",
       " 'colsample_bylevel': None,\n",
       " 'colsample_bynode': None,\n",
       " 'colsample_bytree': None,\n",
       " 'early_stopping_rounds': None,\n",
       " 'enable_categorical': False,\n",
       " 'eval_metric': None,\n",
       " 'feature_types': None,\n",
       " 'gamma': None,\n",
       " 'gpu_id': None,\n",
       " 'grow_policy': None,\n",
       " 'importance_type': None,\n",
       " 'interaction_constraints': None,\n",
       " 'learning_rate': None,\n",
       " 'max_bin': None,\n",
       " 'max_cat_threshold': None,\n",
       " 'max_cat_to_onehot': None,\n",
       " 'max_delta_step': None,\n",
       " 'max_depth': None,\n",
       " 'max_leaves': None,\n",
       " 'min_child_weight': None,\n",
       " 'missing': nan,\n",
       " 'monotone_constraints': None,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'num_parallel_tree': None,\n",
       " 'predictor': None,\n",
       " 'random_state': None,\n",
       " 'reg_alpha': None,\n",
       " 'reg_lambda': None,\n",
       " 'sampling_method': None,\n",
       " 'scale_pos_weight': None,\n",
       " 'subsample': None,\n",
       " 'tree_method': None,\n",
       " 'validate_parameters': None,\n",
       " 'verbosity': None}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = XGBClassifier(objective='binary:hinge')\n",
    "xgb.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:378: FitFailedWarning: \n",
      "20 fits failed out of a total of 50.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "5 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 620, in inner_f\n",
      "    return func(**kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\sklearn.py\", line 1490, in fit\n",
      "    self._Booster = train(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 620, in inner_f\n",
      "    return func(**kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\training.py\", line 185, in train\n",
      "    bst.update(dtrain, i, obj)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 1918, in update\n",
      "    _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 279, in _check_call\n",
      "    raise XGBoostError(py_str(_LIB.XGBGetLastError()))\n",
      "xgboost.core.XGBoostError: value 1.14857 for Parameter subsample exceed bound [0,1]\n",
      "subsample: Row subsample ratio of training instance.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "5 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 620, in inner_f\n",
      "    return func(**kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\sklearn.py\", line 1490, in fit\n",
      "    self._Booster = train(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 620, in inner_f\n",
      "    return func(**kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\training.py\", line 185, in train\n",
      "    bst.update(dtrain, i, obj)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 1918, in update\n",
      "    _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 279, in _check_call\n",
      "    raise XGBoostError(py_str(_LIB.XGBGetLastError()))\n",
      "xgboost.core.XGBoostError: value 1.01159 for Parameter subsample exceed bound [0,1]\n",
      "subsample: Row subsample ratio of training instance.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "5 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 620, in inner_f\n",
      "    return func(**kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\sklearn.py\", line 1490, in fit\n",
      "    self._Booster = train(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 620, in inner_f\n",
      "    return func(**kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\training.py\", line 185, in train\n",
      "    bst.update(dtrain, i, obj)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 1918, in update\n",
      "    _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 279, in _check_call\n",
      "    raise XGBoostError(py_str(_LIB.XGBGetLastError()))\n",
      "xgboost.core.XGBoostError: value 1.34937 for Parameter subsample exceed bound [0,1]\n",
      "subsample: Row subsample ratio of training instance.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "5 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 620, in inner_f\n",
      "    return func(**kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\sklearn.py\", line 1490, in fit\n",
      "    self._Booster = train(\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 620, in inner_f\n",
      "    return func(**kwargs)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\training.py\", line 185, in train\n",
      "    bst.update(dtrain, i, obj)\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 1918, in update\n",
      "    _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,\n",
      "  File \"c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py\", line 279, in _check_call\n",
      "    raise XGBoostError(py_str(_LIB.XGBGetLastError()))\n",
      "xgboost.core.XGBoostError: value 1.12787 for Parameter subsample exceed bound [0,1]\n",
      "subsample: Row subsample ratio of training instance.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\luiza\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_search.py:952: UserWarning: One or more of the test scores are non-finite: [0.52909915 0.53027534 0.54061574 0.56332604 0.61274484        nan\n",
      " 0.55713633        nan        nan        nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, gpu_id=None,\n",
       "                                           grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None...\n",
       "                                        &#x27;learning_rate&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260245A4820&gt;,\n",
       "                                        &#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260245A6380&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260245A5660&gt;,\n",
       "                                        &#x27;subsample&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260245A48E0&gt;},\n",
       "                   random_state=0, scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, gpu_id=None,\n",
       "                                           grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None...\n",
       "                                        &#x27;learning_rate&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260245A4820&gt;,\n",
       "                                        &#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260245A6380&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260245A5660&gt;,\n",
       "                                        &#x27;subsample&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260245A48E0&gt;},\n",
       "                   random_state=0, scoring=&#x27;f1&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              objective=&#x27;binary:hinge&#x27;, predictor=None, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              objective=&#x27;binary:hinge&#x27;, predictor=None, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, gpu_id=None,\n",
       "                                           grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None...\n",
       "                                        'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260245A4820>,\n",
       "                                        'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260245A6380>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260245A5660>,\n",
       "                                        'subsample': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260245A48E0>},\n",
       "                   random_state=0, scoring='f1')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyper_dict = {\n",
    "    \"colsample_bytree\": uniform(0.7, 0.3), # percentual de colunas a serem amostradas para cada árvore (colunas)\n",
    "    \"learning_rate\": uniform(0.03, 0.3), # taxa de aprendizado \n",
    "    \"max_depth\": randint(4, 8), # profundidade máxima de cada árvore\n",
    "    \"n_estimators\": randint(100, 250), # número de árvores\n",
    "    \"subsample\": uniform(0.5, 0.9) # percentual de amostras a serem usadas para cada árvore (linhas)\n",
    "}\n",
    "\n",
    "search_cv = RandomizedSearchCV(xgb, param_distributions=hyper_dict, n_iter=10, scoring='f1', random_state=0)\n",
    "search_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:\n",
      "{'colsample_bytree': 0.9871465476859138, 'learning_rate': 0.07210523412379355, 'max_depth': 4, 'n_estimators': 182, 'subsample': 0.9262472407463395}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best params:\")\n",
    "print(search_cv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.9871465476859138, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.07210523412379355,\n",
       "              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=182, n_jobs=None, num_parallel_tree=None,\n",
       "              objective=&#x27;binary:hinge&#x27;, predictor=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.9871465476859138, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.07210523412379355,\n",
       "              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=182, n_jobs=None, num_parallel_tree=None,\n",
       "              objective=&#x27;binary:hinge&#x27;, predictor=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.9871465476859138, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.07210523412379355,\n",
       "              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=182, n_jobs=None, num_parallel_tree=None,\n",
       "              objective='binary:hinge', predictor=None, ...)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_xgb = search_cv.best_estimator_\n",
    "best_model_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.41      0.53      9605\n",
      "           1       0.32      0.69      0.44      3957\n",
      "\n",
      "    accuracy                           0.49     13562\n",
      "   macro avg       0.54      0.55      0.49     13562\n",
      "weighted avg       0.63      0.49      0.51     13562\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = best_model_xgb.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matriz de confusão\n",
    "cm = confusion_matrix(y_test, y_pred, normalize='true')\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.heatmap(cm, annot=True, fmt=\".2f\", linewidths=0.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Real');\n",
    "plt.xlabel('Predito');\n",
    "all_sample_title = 'Acurácia: {:.2f}'.format(best_model_xgb.score(X_test, y_test))\n",
    "plt.title(all_sample_title, size = 15);\n",
    "plt.rcParams.update({'font.size': 15})\n",
    "plt.show()\n",
    "\n",
    "# salvar em pdf\n",
    "#plt.savefig('plots/xgb_cm_treino-0-1-9-10.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           importance\n",
      "Qtd_CRM                      0.042534\n",
      "tot_chamada                  0.035905\n",
      "VoLTE_perc                   0.033668\n",
      "dias_franquia_zerada         0.032650\n",
      "vol_Loja_de_Apps             0.024429\n",
      "qtde_celula                  0.024233\n",
      "vol_Musica                   0.023698\n",
      "vol_Navegacao                0.023325\n",
      "eficiencia_Voz               0.022966\n",
      "ACD_STATUS_p                 0.022690\n",
      "CUSTOMER_SEGMENT_Controle    0.021575\n",
      "vol_MarketPlace              0.021562\n",
      "eficiencia_pdp               0.021347\n",
      "tot_volume                   0.021319\n",
      "OCUP_STATUS_p                0.021304\n",
      "CUSTOMER_SEGMENT_Pós-pago    0.020898\n",
      "tot_sms_env_receb            0.020011\n",
      "vol_4g_perc                  0.019737\n",
      "originada_perc               0.019312\n",
      "DISP_STATUS_p                0.018971\n",
      "device_tech_4G               0.018746\n",
      "term_fabr_xiaomi             0.018687\n",
      "SURVEY_Base                  0.018471\n",
      "tot_duracao_chamada          0.018457\n",
      "term_fabr_samsung            0.018066\n",
      "FLAG_EXISTENCIA_R            0.017895\n",
      "vol_TIM                      0.017776\n",
      "FLAG_PARAMETRO_R             0.017758\n",
      "qtde_municipio               0.017666\n",
      "vol_InternetBank             0.017633\n",
      "vol_ul_perc                  0.017539\n",
      "vol_Rede_Social              0.017514\n",
      "vol_Google                   0.017317\n",
      "dias_consumo_bonus_ctrl      0.017214\n",
      "vol_Jogos                    0.017090\n",
      "vol_gratuito_perc            0.017028\n",
      "CUSTOMER_SEGMENT_Pré-pago    0.016312\n",
      "ANF                          0.016145\n",
      "vol_Comunicacao              0.015971\n",
      "term_fabr_motorola           0.015931\n",
      "vol_Transporte               0.015894\n",
      "dias_traf_red                0.015769\n",
      "vol_Video                    0.015535\n",
      "vol_Alimentacao              0.015213\n",
      "vol_Ecommerce                0.014496\n",
      "vol_Viagem                   0.014103\n",
      "imsi_LTE                     0.012696\n",
      "FLAG_ELEGIBILIDADE_R         0.012667\n",
      "term_fabr_asus               0.012073\n",
      "term_fabr_apple              0.011879\n",
      "term_fabr_não informado      0.011052\n",
      "term_fabr_outros             0.007274\n",
      "SURVEY_Uso Dados             0.000000\n",
      "term_fabr_lg                 0.000000\n",
      "device_tech_2G               0.000000\n",
      "device_tech_3G               0.000000\n",
      "device_tech_5G               0.000000\n"
     ]
    }
   ],
   "source": [
    "# feature importance\n",
    "feature_importances = pd.DataFrame(best_model_xgb.feature_importances_,\n",
    "                                    index = X_train.columns,\n",
    "                                    columns=['importance']).sort_values('importance', ascending=False)\n",
    "print(feature_importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograma das features mais importantes\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.set_style('white')\n",
    "sns.set(font_scale=1.4)\n",
    "sns.barplot(x=feature_importances.index, y=feature_importances['importance'])\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Importance')\n",
    "plt.title('Feature Importance')\n",
    "plt.show()\n",
    "\n",
    "# Salvar em pdf\n",
    "# plt.savefig('plots/xgb_feature_importance.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(estimator=RandomForestClassifier(),\n",
       "                   param_distributions={&#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x000002601289BF70&gt;,\n",
       "                                        &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x0000026012762050&gt;,\n",
       "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260126B7220&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x0000026012761960&gt;},\n",
       "                   random_state=0, scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(estimator=RandomForestClassifier(),\n",
       "                   param_distributions={&#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x000002601289BF70&gt;,\n",
       "                                        &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x0000026012762050&gt;,\n",
       "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260126B7220&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x0000026012761960&gt;},\n",
       "                   random_state=0, scoring=&#x27;f1&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(estimator=RandomForestClassifier(),\n",
       "                   param_distributions={'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002601289BF70>,\n",
       "                                        'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000026012762050>,\n",
       "                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000260126B7220>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000026012761960>},\n",
       "                   random_state=0, scoring='f1')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyper_dict = {\n",
    "    \"max_depth\": randint(4, 8), # profundidade máxima de cada árvore\n",
    "    \"n_estimators\": randint(100, 250), # número de árvores\n",
    "    \"min_samples_leaf\": randint(1, 4), # número mínimo de amostras em cada folha\n",
    "    \"max_features\": randint(2, 10) # número máximo de features a serem consideradas em cada árvore\n",
    "}\n",
    "\n",
    "search_cv = RandomizedSearchCV(rf, param_distributions=hyper_dict, n_iter=10, scoring='f1', random_state=0)\n",
    "search_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:\n",
      "{'max_depth': 7, 'max_features': 8, 'min_samples_leaf': 1, 'n_estimators': 181}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best params:\")\n",
    "print(search_cv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=7, max_features=8, n_estimators=181)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=7, max_features=8, n_estimators=181)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=7, max_features=8, n_estimators=181)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_rf = search_cv.best_estimator_\n",
    "best_model_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.56      0.64      9605\n",
      "           1       0.34      0.55      0.42      3957\n",
      "\n",
      "    accuracy                           0.56     13562\n",
      "   macro avg       0.55      0.56      0.53     13562\n",
      "weighted avg       0.63      0.56      0.58     13562\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"for key in cr.keys():\\n    if key != 'accuracy':\\n        cr[key] = {k: round(v, 2) for k, v in cr[key].items()}\\n\\n# salvar em excel\\ncr_df = pd.DataFrame(cr).transpose()\\ncr_df.to_csv('tables/rf_cr_extremos_estendidos.csv', index=False)\""
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = best_model_rf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "#cr = classification_report(y_test, y_pred, output_dict=True)\n",
    "#print(cr)\n",
    "\n",
    "# limitar a 2 casas decimais\n",
    "'''for key in cr.keys():\n",
    "    if key != 'accuracy':\n",
    "        cr[key] = {k: round(v, 2) for k, v in cr[key].items()}\n",
    "\n",
    "# salvar em excel\n",
    "cr_df = pd.DataFrame(cr).transpose()\n",
    "cr_df.to_csv('tables/rf_cr_extremos_estendidos.csv', index=False)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matriz de confusão\n",
    "cm = confusion_matrix(y_test, y_pred, normalize='true')\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.heatmap(cm, annot=True, fmt=\".2f\", linewidths=0.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Real');\n",
    "plt.xlabel('Predito');\n",
    "all_sample_title = 'Acurácia: {:.2f}'.format(best_model_rf.score(X_test, y_test))\n",
    "plt.title(all_sample_title, size = 15);\n",
    "plt.show()\n",
    "\n",
    "# salvar em pdf\n",
    "#plt.savefig('plots/rf_cm_treino-0-1-9-10.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           importance\n",
      "qtde_celula                  0.058588\n",
      "tot_chamada                  0.056059\n",
      "VoLTE_perc                   0.045482\n",
      "tot_duracao_chamada          0.041616\n",
      "tot_volume                   0.041105\n",
      "ANF                          0.039124\n",
      "vol_Rede_Social              0.038991\n",
      "tot_sms_env_receb            0.037572\n",
      "eficiencia_pdp               0.037460\n",
      "vol_Loja_de_Apps             0.036913\n",
      "eficiencia_Voz               0.035422\n",
      "vol_Video                    0.032774\n",
      "vol_Navegacao                0.032160\n",
      "originada_perc               0.031029\n",
      "vol_Comunicacao              0.029639\n",
      "qtde_municipio               0.024543\n",
      "OCUP_STATUS_p                0.023921\n",
      "vol_Google                   0.023786\n",
      "vol_Musica                   0.023664\n",
      "vol_MarketPlace              0.023404\n",
      "vol_ul_perc                  0.022122\n",
      "dias_franquia_zerada         0.021959\n",
      "DISP_STATUS_p                0.021330\n",
      "Qtd_CRM                      0.021235\n",
      "vol_4g_perc                  0.020675\n",
      "ACD_STATUS_p                 0.020374\n",
      "vol_InternetBank             0.016752\n",
      "vol_gratuito_perc            0.013957\n",
      "vol_Transporte               0.013693\n",
      "CUSTOMER_SEGMENT_Controle    0.013109\n",
      "vol_TIM                      0.012437\n",
      "CUSTOMER_SEGMENT_Pré-pago    0.008782\n",
      "vol_Ecommerce                0.008177\n",
      "FLAG_EXISTENCIA_R            0.007194\n",
      "vol_Alimentacao              0.007063\n",
      "vol_Jogos                    0.006711\n",
      "FLAG_ELEGIBILIDADE_R         0.006221\n",
      "dias_consumo_bonus_ctrl      0.005785\n",
      "vol_Viagem                   0.004201\n",
      "term_fabr_motorola           0.004069\n",
      "SURVEY_Uso Dados             0.003310\n",
      "CUSTOMER_SEGMENT_Pós-pago    0.003298\n",
      "term_fabr_samsung            0.003230\n",
      "SURVEY_Base                  0.002754\n",
      "device_tech_4G               0.002633\n",
      "device_tech_5G               0.002431\n",
      "term_fabr_apple              0.002251\n",
      "FLAG_PARAMETRO_R             0.002038\n",
      "term_fabr_xiaomi             0.001677\n",
      "term_fabr_não informado      0.001630\n",
      "term_fabr_lg                 0.001613\n",
      "dias_traf_red                0.001172\n",
      "term_fabr_asus               0.000990\n",
      "term_fabr_outros             0.000842\n",
      "device_tech_3G               0.000562\n",
      "imsi_LTE                     0.000298\n",
      "device_tech_2G               0.000174\n"
     ]
    }
   ],
   "source": [
    "# feature importance\n",
    "feature_importances = pd.DataFrame(best_model_rf.feature_importances_,\n",
    "                                    index = X_train.columns,\n",
    "                                    columns=['importance']).sort_values('importance', ascending=False)\n",
    "print(feature_importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograma das features mais importantes\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.set_style('white')\n",
    "sns.set(font_scale=1.4)\n",
    "sns.barplot(x=feature_importances.head(10).index, y=feature_importances['importance'].head(10))\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Importance')\n",
    "plt.title('Feature Importance')\n",
    "plt.show()\n",
    "\n",
    "# salvar em pdf sem erro de tamanho\n",
    "#plt.savefig('plots/rf_feature_importance.pdf')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}