pred-1_conj-completo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primeira abordagem de predição: conjunto completo\n",
    "- primeiro é testada a correlação linear\n",
    "  - de todas as features entre si\n",
    "  - de todas as features com a flag detrator\n",
    "- depois segue para a predição:\n",
    "  - sem equilibrar amostras\n",
    "  - sem validação cruzada\n",
    "  - sem otimização de parâmetros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bibliotecas e Configurações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <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",
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       "      <td>1</td>\n",
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       "      <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",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "    <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",
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       "      <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",
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       "      <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",
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       "      <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": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['USERVALUEQ1_LABEL'].value_counts() # divisão de clientes por nota atribuída"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exclusão de colunas que vazam dados na predição"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['USERVALUEQ1_LABEL', 'USERVALUEQ2_VALUE', 'NPS_Class_Promotor', 'NPS_Class_Passivo'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizar df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>51</td>\n",
       "      <td>8.111028</td>\n",
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       "      <td>3.295874</td>\n",
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       "      <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>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>-6.907755</td>\n",
       "      <td>1.946053</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>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>1.386544</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>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>-6.907755</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>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>-6.907755</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ANF  vol_Rede_Social  vol_Video  vol_Comunicacao  vol_Loja_de_Apps  \\\n",
       "0   51         8.111028   6.282269         5.894406          5.347112   \n",
       "1   11         7.180832   5.153297         5.283209          1.946053   \n",
       "2   37         8.094379   5.846442         6.682110          2.890427   \n",
       "3   81         4.897847   5.771444         1.386544         -6.907755   \n",
       "4   85         3.784212   3.434019         5.918897          1.791926   \n",
       "\n",
       "   vol_Musica  vol_Google  vol_Navegacao  vol_InternetBank  vol_Transporte  \\\n",
       "0   -6.907755    3.295874       3.295874         -6.907755       -6.907755   \n",
       "1   -6.907755   -6.907755      -6.907755         -6.907755        1.946053   \n",
       "2    4.844195    1.609638      -6.907755          1.386544       -6.907755   \n",
       "3   -6.907755    0.001000      -6.907755         -6.907755       -6.907755   \n",
       "4   -6.907755    1.386544      -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  ...                   0                        0                 0   \n",
       "2  ...                   1                        0                 0   \n",
       "3  ...                   0                        0                 1   \n",
       "4  ...                   0                        1                 0   \n",
       "\n",
       "   term_fabr_samsung  term_fabr_xiaomi  device_tech_2G  device_tech_3G  \\\n",
       "0                  0                 0               0               0   \n",
       "1                  1                 0               0               0   \n",
       "2                  0                 0               0               0   \n",
       "3                  0                 0               0               1   \n",
       "4                  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               1               0        0  \n",
       "3               0               0        0  \n",
       "4               1               0        0  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45206 entries, 0 to 45205\n",
      "Data columns (total 58 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   ANF                        45206 non-null  int64  \n",
      " 1   vol_Rede_Social            45206 non-null  float64\n",
      " 2   vol_Video                  45206 non-null  float64\n",
      " 3   vol_Comunicacao            45206 non-null  float64\n",
      " 4   vol_Loja_de_Apps           45206 non-null  float64\n",
      " 5   vol_Musica                 45206 non-null  float64\n",
      " 6   vol_Google                 45206 non-null  float64\n",
      " 7   vol_Navegacao              45206 non-null  float64\n",
      " 8   vol_InternetBank           45206 non-null  float64\n",
      " 9   vol_Transporte             45206 non-null  float64\n",
      " 10  vol_Jogos                  45206 non-null  float64\n",
      " 11  vol_MarketPlace            45206 non-null  float64\n",
      " 12  vol_TIM                    45206 non-null  float64\n",
      " 13  vol_Alimentacao            45206 non-null  float64\n",
      " 14  vol_Ecommerce              45206 non-null  float64\n",
      " 15  vol_Viagem                 45206 non-null  float64\n",
      " 16  FLAG_EXISTENCIA_R          45206 non-null  int64  \n",
      " 17  FLAG_PARAMETRO_R           45206 non-null  int64  \n",
      " 18  FLAG_ELEGIBILIDADE_R       45206 non-null  int64  \n",
      " 19  imsi_LTE                   45206 non-null  int64  \n",
      " 20  qtde_celula                45206 non-null  int64  \n",
      " 21  tot_chamada                45206 non-null  int64  \n",
      " 22  tot_duracao_chamada        45206 non-null  float64\n",
      " 23  tot_sms_env_receb          45206 non-null  int64  \n",
      " 24  originada_perc             45206 non-null  float64\n",
      " 25  eficiencia_Voz             45206 non-null  float64\n",
      " 26  tot_volume                 45206 non-null  float64\n",
      " 27  vol_gratuito_perc          45206 non-null  float64\n",
      " 28  vol_4g_perc                45206 non-null  float64\n",
      " 29  vol_ul_perc                45206 non-null  float64\n",
      " 30  eficiencia_pdp             45206 non-null  float64\n",
      " 31  qtde_municipio             45206 non-null  float64\n",
      " 32  VoLTE_perc                 45206 non-null  float64\n",
      " 33  DISP_STATUS_p              45206 non-null  float64\n",
      " 34  ACD_STATUS_p               45206 non-null  float64\n",
      " 35  OCUP_STATUS_p              45206 non-null  float64\n",
      " 36  dias_franquia_zerada       45206 non-null  float64\n",
      " 37  dias_traf_red              45206 non-null  float64\n",
      " 38  dias_consumo_bonus_ctrl    45206 non-null  float64\n",
      " 39  CUSTOMER_SEGMENT_Controle  45206 non-null  int64  \n",
      " 40  CUSTOMER_SEGMENT_Pré-pago  45206 non-null  int64  \n",
      " 41  CUSTOMER_SEGMENT_Pós-pago  45206 non-null  int64  \n",
      " 42  NPS_Class_Detrator         45206 non-null  int64  \n",
      " 43  SURVEY_Base                45206 non-null  int64  \n",
      " 44  SURVEY_Uso Dados           45206 non-null  int64  \n",
      " 45  term_fabr_apple            45206 non-null  int64  \n",
      " 46  term_fabr_asus             45206 non-null  int64  \n",
      " 47  term_fabr_lg               45206 non-null  int64  \n",
      " 48  term_fabr_motorola         45206 non-null  int64  \n",
      " 49  term_fabr_não informado    45206 non-null  int64  \n",
      " 50  term_fabr_outros           45206 non-null  int64  \n",
      " 51  term_fabr_samsung          45206 non-null  int64  \n",
      " 52  term_fabr_xiaomi           45206 non-null  int64  \n",
      " 53  device_tech_2G             45206 non-null  int64  \n",
      " 54  device_tech_3G             45206 non-null  int64  \n",
      " 55  device_tech_4G             45206 non-null  int64  \n",
      " 56  device_tech_5G             45206 non-null  int64  \n",
      " 57  Qtd_CRM                    45206 non-null  int64  \n",
      "dtypes: float64(31), int64(27)\n",
      "memory usage: 20.0 MB\n"
     ]
    }
   ],
   "source": [
    "display(df.head())\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlação Linear (Heatmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Todas as features entre si"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatrix = df.corr()\n",
    "sns.heatmap(corrMatrix[(corrMatrix >= 0.4) | (corrMatrix <= -0.4)], annot=True, cmap='viridis') # mostra apenas correlações >= 0.4 e <= -0.4\n",
    "sns.set(rc={'figure.figsize':(40, 40)})\n",
    "sns.set(font_scale=1.4)\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NPS_Class_Detrator com todas as outras features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x2880 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap apenas da coluna NPS_Class_Detrator com as outras\n",
    "corrMatrix = df.corr()\n",
    "sns.heatmap(corrMatrix[['NPS_Class_Detrator']], annot=True, cmap='viridis')\n",
    "sns.set(rc={'figure.figsize':(40, 40)})\n",
    "sns.set(font_scale=1.4)\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_df = df.drop(\"NPS_Class_Detrator\", axis=1)\n",
    "y_df = df.NPS_Class_Detrator # Alvo\n",
    "\n",
    "# Split into train and test \n",
    "X_train, X_test, y_train, y_test = train_test_split(X_df, y_df, test_size=0.3, random_state=1) # 70% treino, 30% teste\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pré-processamento com Standard Scaler -> não precisa p/ modelos de árvore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'from sklearn.preprocessing import MinMaxScaler\\n\\nscaler = MinMaxScaler()\\n\\n# Devemos aplicar o fit_transform somente nos dados de treino\\nX_train_scaled = scaler.fit_transform(X_train)\\n# Devemos aplicar o transform somente nos dados de teste\\nX_test_scaled = scaler.transform(X_test)\\n\\n# Transformando os dados de volta em DataFrame\\nX_train_scaled = pd.DataFrame(X_train_scaled, columns=X_train.columns)\\nX_test_scaled = pd.DataFrame(X_test_scaled, columns=X_test.columns)'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Devemos aplicar o fit_transform somente nos dados de treino\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "# Devemos aplicar o transform somente nos dados de teste\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Transformando os dados de volta em DataFrame\n",
    "X_train_scaled = pd.DataFrame(X_train_scaled, columns=X_train.columns)\n",
    "X_test_scaled = pd.DataFrame(X_test_scaled, columns=X_test.columns)\n",
    "\n",
    "# Verificando se foram normalizados\n",
    "# X_train_scaled.describe()'''\n",
    "\n",
    "# TESTE - MinMaxScaler\n",
    "'''from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Devemos aplicar o fit_transform somente nos dados de treino\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "# Devemos aplicar o transform somente nos dados de teste\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Transformando os dados de volta em DataFrame\n",
    "X_train_scaled = pd.DataFrame(X_train_scaled, columns=X_train.columns)\n",
    "X_test_scaled = pd.DataFrame(X_test_scaled, columns=X_test.columns)'''\n",
    "\n",
    "# Verificando se foram normalizados\n",
    "# X_train_scaled.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=100)\n",
    "rf.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "F1-SCORE:\n",
    "\n",
    "n_estimators=10 == 0.57 \n",
    "\n",
    "n_estimators=100 == 0.61\n",
    "\n",
    "n_estimators=200 == 0.63\n",
    "\n",
    "n_estimators=1000 == 0.63\n",
    "\n",
    "n_estimators=1500 == 0.63\n",
    "\n",
    "n_estimators=2000 == 0.63\n",
    "\n",
    "... aumento demasiado do custo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.99      0.82      9539\n",
      "           1       0.52      0.03      0.06      4023\n",
      "\n",
      "    accuracy                           0.70     13562\n",
      "   macro avg       0.61      0.51      0.44     13562\n",
      "weighted avg       0.65      0.70      0.60     13562\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train a machine learning model\n",
    "rf.fit(X_train, y_train) # X_train_scaled ou X_train\n",
    "\n",
    "# Make prediction on the testing data\n",
    "y_pred = rf.predict(X_test) # X_test_scaled ou X_test\n",
    "\n",
    "# classification report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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(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_trapaceira.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           importance\n",
      "qtde_celula                  0.047272\n",
      "tot_sms_env_receb            0.045404\n",
      "tot_volume                   0.044885\n",
      "tot_duracao_chamada          0.044026\n",
      "tot_chamada                  0.043752\n",
      "vol_Rede_Social              0.042533\n",
      "vol_Comunicacao              0.040778\n",
      "originada_perc               0.040346\n",
      "vol_Video                    0.039637\n",
      "eficiencia_Voz               0.038012\n",
      "vol_Loja_de_Apps             0.033376\n",
      "ANF                          0.033097\n",
      "vol_ul_perc                  0.032994\n",
      "eficiencia_pdp               0.032249\n",
      "OCUP_STATUS_p                0.031951\n",
      "vol_Google                   0.030701\n",
      "DISP_STATUS_p                0.030212\n",
      "VoLTE_perc                   0.029850\n",
      "qtde_municipio               0.028655\n",
      "vol_4g_perc                  0.022756\n",
      "vol_InternetBank             0.021925\n",
      "vol_Navegacao                0.020831\n",
      "vol_gratuito_perc            0.019487\n",
      "vol_MarketPlace              0.017479\n",
      "vol_Musica                   0.017183\n",
      "vol_Transporte               0.015030\n",
      "vol_TIM                      0.013760\n",
      "ACD_STATUS_p                 0.012827\n",
      "FLAG_EXISTENCIA_R            0.011625\n",
      "dias_franquia_zerada         0.011187\n",
      "FLAG_ELEGIBILIDADE_R         0.008642\n",
      "vol_Ecommerce                0.007881\n",
      "vol_Alimentacao              0.007184\n",
      "term_fabr_samsung            0.006318\n",
      "vol_Jogos                    0.006081\n",
      "term_fabr_motorola           0.006013\n",
      "CUSTOMER_SEGMENT_Controle    0.005728\n",
      "Qtd_CRM                      0.004916\n",
      "CUSTOMER_SEGMENT_Pré-pago    0.004752\n",
      "CUSTOMER_SEGMENT_Pós-pago    0.004652\n",
      "vol_Viagem                   0.004430\n",
      "device_tech_4G               0.003640\n",
      "device_tech_5G               0.003514\n",
      "SURVEY_Uso Dados             0.003497\n",
      "term_fabr_lg                 0.003483\n",
      "SURVEY_Base                  0.003424\n",
      "dias_consumo_bonus_ctrl      0.003179\n",
      "term_fabr_xiaomi             0.002820\n",
      "term_fabr_não informado      0.002811\n",
      "term_fabr_apple              0.002711\n",
      "FLAG_PARAMETRO_R             0.002636\n",
      "dias_traf_red                0.002620\n",
      "term_fabr_outros             0.001685\n",
      "term_fabr_asus               0.001538\n",
      "device_tech_3G               0.001027\n",
      "imsi_LTE                     0.000557\n",
      "device_tech_2G               0.000439\n"
     ]
    }
   ],
   "source": [
    "# feature importance\n",
    "feature_importances = pd.DataFrame(rf.feature_importances_,\n",
    "                                    index = X_train.columns,\n",
    "                                    columns=['importance']).sort_values('importance', ascending=False)\n",
    "print(feature_importances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': 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",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.71      0.71      9539\n",
      "           1       0.32      0.33      0.32      4023\n",
      "\n",
      "    accuracy                           0.59     13562\n",
      "   macro avg       0.52      0.52      0.52     13562\n",
      "weighted avg       0.60      0.59      0.60     13562\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dt.fit(X_train, y_train) # X_train_scaled ou X_train\n",
    "\n",
    "y_pred = dt.predict(X_test) # X_test_scaled ou X_test\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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)\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('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score: {:.2f}'.format(dt.score(X_test, y_test))\n",
    "plt.title(all_sample_title, size = 15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           importance\n",
      "qtde_celula                  0.058469\n",
      "tot_sms_env_receb            0.053201\n",
      "tot_volume                   0.051341\n",
      "tot_chamada                  0.042814\n",
      "tot_duracao_chamada          0.042193\n",
      "vol_Video                    0.041470\n",
      "originada_perc               0.040763\n",
      "vol_Comunicacao              0.040718\n",
      "vol_Rede_Social              0.039935\n",
      "OCUP_STATUS_p                0.038539\n",
      "eficiencia_Voz               0.036559\n",
      "ANF                          0.034986\n",
      "vol_ul_perc                  0.034716\n",
      "eficiencia_pdp               0.034571\n",
      "DISP_STATUS_p                0.031515\n",
      "vol_Loja_de_Apps             0.030090\n",
      "qtde_municipio               0.029328\n",
      "VoLTE_perc                   0.028264\n",
      "vol_Google                   0.025646\n",
      "vol_Navegacao                0.021355\n",
      "vol_4g_perc                  0.019032\n",
      "vol_InternetBank             0.017084\n",
      "vol_gratuito_perc            0.016855\n",
      "ACD_STATUS_p                 0.015838\n",
      "vol_MarketPlace              0.015731\n",
      "vol_Transporte               0.014528\n",
      "vol_Musica                   0.013320\n",
      "vol_TIM                      0.012533\n",
      "dias_franquia_zerada         0.012515\n",
      "FLAG_EXISTENCIA_R            0.010374\n",
      "vol_Ecommerce                0.008348\n",
      "vol_Alimentacao              0.007753\n",
      "FLAG_ELEGIBILIDADE_R         0.007737\n",
      "vol_Jogos                    0.006333\n",
      "term_fabr_motorola           0.005294\n",
      "vol_Viagem                   0.005041\n",
      "CUSTOMER_SEGMENT_Controle    0.004930\n",
      "term_fabr_samsung            0.004785\n",
      "Qtd_CRM                      0.004621\n",
      "term_fabr_lg                 0.003545\n",
      "CUSTOMER_SEGMENT_Pós-pago    0.003478\n",
      "term_fabr_xiaomi             0.003454\n",
      "FLAG_PARAMETRO_R             0.003453\n",
      "dias_consumo_bonus_ctrl      0.003185\n",
      "dias_traf_red                0.003092\n",
      "term_fabr_não informado      0.003089\n",
      "SURVEY_Uso Dados             0.002717\n",
      "CUSTOMER_SEGMENT_Pré-pago    0.002566\n",
      "device_tech_4G               0.002386\n",
      "term_fabr_asus               0.001906\n",
      "SURVEY_Base                  0.001836\n",
      "term_fabr_apple              0.001779\n",
      "device_tech_5G               0.001365\n",
      "device_tech_3G               0.001085\n",
      "term_fabr_outros             0.001069\n",
      "device_tech_2G               0.000459\n",
      "imsi_LTE                     0.000409\n"
     ]
    }
   ],
   "source": [
    "# feature importance\n",
    "feature_importances = pd.DataFrame(dt.feature_importances_,\n",
    "                                    index = X_train.columns,\n",
    "                                    columns=['importance']).sort_values('importance', ascending=False)\n",
    "print(feature_importances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost (Gradient Boosting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = XGBClassifier(objective='binary:hinge')\n",
    "xgb.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.95      0.81      9539\n",
      "           1       0.45      0.10      0.17      4023\n",
      "\n",
      "    accuracy                           0.70     13562\n",
      "   macro avg       0.58      0.52      0.49     13562\n",
      "weighted avg       0.64      0.70      0.62     13562\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgb.fit(X_train, y_train)\n",
    "\n",
    "y_pred = xgb.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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(xgb.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/xgb_cm_trapaceira.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           importance\n",
      "vol_4g_perc                  0.184541\n",
      "dias_franquia_zerada         0.083169\n",
      "VoLTE_perc                   0.081760\n",
      "vol_MarketPlace              0.079752\n",
      "qtde_celula                  0.075542\n",
      "Qtd_CRM                      0.070628\n",
      "device_tech_5G               0.021720\n",
      "CUSTOMER_SEGMENT_Controle    0.020021\n",
      "vol_Navegacao                0.011068\n",
      "vol_TIM                      0.010949\n",
      "CUSTOMER_SEGMENT_Pré-pago    0.010393\n",
      "qtde_municipio               0.010310\n",
      "term_fabr_asus               0.010244\n",
      "vol_Musica                   0.010206\n",
      "originada_perc               0.010000\n",
      "CUSTOMER_SEGMENT_Pós-pago    0.009602\n",
      "dias_traf_red                0.009503\n",
      "term_fabr_samsung            0.009351\n",
      "vol_Video                    0.009111\n",
      "eficiencia_Voz               0.008841\n",
      "eficiencia_pdp               0.008484\n",
      "OCUP_STATUS_p                0.008472\n",
      "device_tech_4G               0.008407\n",
      "ACD_STATUS_p                 0.008386\n",
      "vol_Alimentacao              0.008262\n",
      "vol_Comunicacao              0.008087\n",
      "tot_volume                   0.007931\n",
      "tot_sms_env_receb            0.007848\n",
      "DISP_STATUS_p                0.007681\n",
      "vol_Rede_Social              0.007631\n",
      "vol_Google                   0.007624\n",
      "device_tech_3G               0.007605\n",
      "vol_ul_perc                  0.007539\n",
      "vol_Transporte               0.007515\n",
      "tot_duracao_chamada          0.007508\n",
      "imsi_LTE                     0.007467\n",
      "term_fabr_lg                 0.007458\n",
      "tot_chamada                  0.007352\n",
      "vol_gratuito_perc            0.007348\n",
      "ANF                          0.007283\n",
      "term_fabr_motorola           0.007071\n",
      "vol_Viagem                   0.007070\n",
      "vol_Loja_de_Apps             0.007032\n",
      "FLAG_ELEGIBILIDADE_R         0.007010\n",
      "vol_Jogos                    0.006963\n",
      "FLAG_PARAMETRO_R             0.006927\n",
      "term_fabr_outros             0.006924\n",
      "dias_consumo_bonus_ctrl      0.006921\n",
      "vol_InternetBank             0.006918\n",
      "device_tech_2G               0.006909\n",
      "term_fabr_apple              0.006818\n",
      "SURVEY_Base                  0.006731\n",
      "vol_Ecommerce                0.006700\n",
      "FLAG_EXISTENCIA_R            0.006337\n",
      "term_fabr_não informado      0.006271\n",
      "term_fabr_xiaomi             0.004799\n",
      "SURVEY_Uso Dados             0.000000\n"
     ]
    }
   ],
   "source": [
    "# feature importance\n",
    "feature_importances = pd.DataFrame(xgb.feature_importances_,\n",
    "                                    index = X_train.columns,\n",
    "                                    columns=['importance']).sort_values('importance', ascending=False)\n",
    "print(feature_importances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SHAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'import shap\\nshap.initjs()'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''import shap\n",
    "shap.initjs()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'explainer = shap.Explainer(rf)\\nshap_values = explainer.shap_values(X_test_scaled)'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''explainer = shap.Explainer(rf)\n",
    "shap_values = explainer.shap_values(X_test_scaled)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'shap.summary_plot(shap_values, X_test_scaled)'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''shap.summary_plot(shap_values, X_test_scaled)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'shap.summary_plot(shap_values[0], X_test_scaled)'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''shap.summary_plot(shap_values[0], X_test_scaled)'''"
   ]
  }
 ],
 "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
}