Telco_Customer_Churn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![logos](https://images-submarino.b2w.io/produtos/01/00/item/126812/5/126812590_1GG.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Incentivado pela leitura do Licro 'Data Science para Negócios', resolvi usar o dataset da Empresa Telco - Telecom.\n",
    "# O case é citado diversas vezes no livro, pretendo aprender/ensinar/expor conceitos sobre a atuação de 'árvores de decisão' \n",
    "# para o churn de clientes do segmentos. Pretendo ser o mais simples e prático possível. \n",
    "# Não pretendo e nem quero ser especialista/guru no assunto, acredito que possamos disseminar e compartilhar conhecimento."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Te convido a explorar e conhecer o Mundo de dataframes e <b>árvores de decisão</b>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Leitura incrível sobre o <b>[tema](https://medium.com/machine-learning-beyond-deep-learning/%C3%A1rvores-de-decis%C3%A3o-3f52f6420b69)</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Começamos sempre com a importação das bibliotecas\n",
    "import platform\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import numpy as np\n",
    "#import graphviz\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python version 3.7.1\n",
      "pandas version 0.23.4\n",
      "sklearn version 0.20.1\n",
      "numpy version 1.15.4\n",
      "matplotlib version 3.0.2\n"
     ]
    }
   ],
   "source": [
    "# Que tal levantar as versões dos programas usados?\n",
    "print('python version', platform.python_version())\n",
    "print('pandas version', pd.__version__)\n",
    "print('sklearn version', sklearn.__version__)\n",
    "print('numpy version', np.__version__)\n",
    "#print('graphviz version', graphviz.__version__)\n",
    "print('matplotlib version', matplotlib.__version__)"
   ]
  },
  {
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
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       "      <td>0</td>\n",
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       "      <td>No</td>\n",
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       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ...  DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...                No   \n",
       "1                No             DSL            Yes  ...               Yes   \n",
       "2                No             DSL            Yes  ...                No   \n",
       "3  No phone service             DSL            Yes  ...               Yes   \n",
       "4                No     Fiber optic             No  ...                No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importação do csv, fácil e simples de executar\n",
    "df = pd.read_csv('WA_Fn-UseC_-Telco-Customer-Churn.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Limpeza dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remover todos registros com valores nulos\n",
    "df = df.dropna(how=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remover registros duplicados\n",
    "df = df[~df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7032, 21)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conversão da informação de TotalCharges para FLOAT\n",
    "total_charges_filter = df.TotalCharges == \" \"\n",
    "df = df[~total_charges_filter]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.TotalCharges = pd.to_numeric(df.TotalCharges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "      <th>Dependents</th>\n",
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       "      <th>OnlineSecurity</th>\n",
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       "      <th>Churn</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
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       "      <td>0.368844</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.085974</td>\n",
       "      <td>2266.771362</td>\n",
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       "      <th>min</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>401.450000</td>\n",
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       "      <th>50%</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>1397.475000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.862500</td>\n",
       "      <td>3794.737500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>8684.800000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        customerID gender  SeniorCitizen Partner Dependents       tenure  \\\n",
       "count         7032   7032    7032.000000    7032       7032  7032.000000   \n",
       "unique        7032      2            NaN       2          2          NaN   \n",
       "top     5602-BVFMK   Male            NaN      No         No          NaN   \n",
       "freq             1   3549            NaN    3639       4933          NaN   \n",
       "mean           NaN    NaN       0.162400     NaN        NaN    32.421786   \n",
       "std            NaN    NaN       0.368844     NaN        NaN    24.545260   \n",
       "min            NaN    NaN       0.000000     NaN        NaN     1.000000   \n",
       "25%            NaN    NaN       0.000000     NaN        NaN     9.000000   \n",
       "50%            NaN    NaN       0.000000     NaN        NaN    29.000000   \n",
       "75%            NaN    NaN       0.000000     NaN        NaN    55.000000   \n",
       "max            NaN    NaN       1.000000     NaN        NaN    72.000000   \n",
       "\n",
       "       PhoneService MultipleLines InternetService OnlineSecurity  ...    \\\n",
       "count          7032          7032            7032           7032  ...     \n",
       "unique            2             3               3              3  ...     \n",
       "top             Yes            No     Fiber optic             No  ...     \n",
       "freq           6352          3385            3096           3497  ...     \n",
       "mean            NaN           NaN             NaN            NaN  ...     \n",
       "std             NaN           NaN             NaN            NaN  ...     \n",
       "min             NaN           NaN             NaN            NaN  ...     \n",
       "25%             NaN           NaN             NaN            NaN  ...     \n",
       "50%             NaN           NaN             NaN            NaN  ...     \n",
       "75%             NaN           NaN             NaN            NaN  ...     \n",
       "max             NaN           NaN             NaN            NaN  ...     \n",
       "\n",
       "       DeviceProtection TechSupport StreamingTV StreamingMovies  \\\n",
       "count              7032        7032        7032            7032   \n",
       "unique                3           3           3               3   \n",
       "top                  No          No          No              No   \n",
       "freq               3094        3472        2809            2781   \n",
       "mean                NaN         NaN         NaN             NaN   \n",
       "std                 NaN         NaN         NaN             NaN   \n",
       "min                 NaN         NaN         NaN             NaN   \n",
       "25%                 NaN         NaN         NaN             NaN   \n",
       "50%                 NaN         NaN         NaN             NaN   \n",
       "75%                 NaN         NaN         NaN             NaN   \n",
       "max                 NaN         NaN         NaN             NaN   \n",
       "\n",
       "              Contract PaperlessBilling     PaymentMethod MonthlyCharges  \\\n",
       "count             7032             7032              7032    7032.000000   \n",
       "unique               3                2                 4            NaN   \n",
       "top     Month-to-month              Yes  Electronic check            NaN   \n",
       "freq              3875             4168              2365            NaN   \n",
       "mean               NaN              NaN               NaN      64.798208   \n",
       "std                NaN              NaN               NaN      30.085974   \n",
       "min                NaN              NaN               NaN      18.250000   \n",
       "25%                NaN              NaN               NaN      35.587500   \n",
       "50%                NaN              NaN               NaN      70.350000   \n",
       "75%                NaN              NaN               NaN      89.862500   \n",
       "max                NaN              NaN               NaN     118.750000   \n",
       "\n",
       "        TotalCharges  Churn  \n",
       "count    7032.000000   7032  \n",
       "unique           NaN      2  \n",
       "top              NaN     No  \n",
       "freq             NaN   5163  \n",
       "mean     2283.300441    NaN  \n",
       "std      2266.771362    NaN  \n",
       "min        18.800000    NaN  \n",
       "25%       401.450000    NaN  \n",
       "50%      1397.475000    NaN  \n",
       "75%      3794.737500    NaN  \n",
       "max      8684.800000    NaN  \n",
       "\n",
       "[11 rows x 21 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = [\n",
    "    \"gender\",\n",
    "    \"SeniorCitizen\",\n",
    "    \"Partner\",\n",
    "    \"Dependents\",\n",
    "    \"PhoneService\",\n",
    "    \"MultipleLines\",\n",
    "    \"InternetService\",\n",
    "    \"OnlineSecurity\",\n",
    "    \"OnlineBackup\",\n",
    "    \"DeviceProtection\",\n",
    "    \"TechSupport\",\n",
    "    \"StreamingTV\",\n",
    "    \"StreamingMovies\",\n",
    "    \"Contract\",\n",
    "    \"PaperlessBilling\",\n",
    "    \"PaymentMethod\",\n",
    "]\n",
    "numerical_features = [\"tenure\", \"MonthlyCharges\", \"TotalCharges\"]\n",
    "target = \"Churn\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7032.000000</td>\n",
       "      <td>7032.000000</td>\n",
       "      <td>7032.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32.421786</td>\n",
       "      <td>64.798208</td>\n",
       "      <td>2283.300441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.545260</td>\n",
       "      <td>30.085974</td>\n",
       "      <td>2266.771362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>18.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.587500</td>\n",
       "      <td>401.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>1397.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.862500</td>\n",
       "      <td>3794.737500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>8684.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tenure  MonthlyCharges  TotalCharges\n",
       "count  7032.000000     7032.000000   7032.000000\n",
       "mean     32.421786       64.798208   2283.300441\n",
       "std      24.545260       30.085974   2266.771362\n",
       "min       1.000000       18.250000     18.800000\n",
       "25%       9.000000       35.587500    401.450000\n",
       "50%      29.000000       70.350000   1397.475000\n",
       "75%      55.000000       89.862500   3794.737500\n",
       "max      72.000000      118.750000   8684.800000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resumo estatístico do DataFrame, com quartis, mediana, etc.\n",
    "df[numerical_features].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos começar a gerar gráficos! <b>[histograma](https://www.fm2s.com.br/histograma/)</b><p>\n",
    "Caso tenha curiosidade sobre, acesso link ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001F9A8AFEE10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001F9A8A93240>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001F9A8CE2080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001F9A9416160>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[numerical_features].hist(bins=30, figsize=(10, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001F9A8D15E10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001F9A8CC21D0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001F9A8B453C8>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Observamos / Concluímos QUE quanto maior o TotalCharges menor o Churn\n",
    "# Segue ai, que tem + coisas\n",
    "fig, ax = plt.subplots(1, 3, figsize=(14, 4))\n",
    "df[df.Churn == \"No\"][numerical_features].hist(bins=30, color=\"blue\", alpha=0.5, ax=ax)\n",
    "df[df.Churn == \"Yes\"][numerical_features].hist(bins=30, color=\"red\", alpha=0.5, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ROWS, COLS = 4, 4\n",
    "fig, ax = plt.subplots(ROWS, COLS, figsize=(18, 18))\n",
    "row, col = 0, 0\n",
    "for i, categorical_feature in enumerate(categorical_features):\n",
    "    if col == COLS - 1:\n",
    "        row += 1\n",
    "    col = i % COLS\n",
    "    df[categorical_feature].value_counts().plot('bar', ax=ax[row, col]).set_title(categorical_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'churned')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agora veremos a informação de contrato 9mensal / 1 ano ou 2 anos)\n",
    "feature = 'Contract'\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 4))\n",
    "df[df.Churn == \"No\"][feature].value_counts().plot('bar', ax=ax[0]).set_title('not churned')\n",
    "df[df.Churn == \"Yes\"][feature].value_counts().plot('bar', ax=ax[1]).set_title('churned')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'churned')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[target].value_counts().plot('bar').set_title('churned')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label encoder gender - values: ['Female', 'Male']\n",
      "Label encoder SeniorCitizen - values: [0, 1]\n",
      "Label encoder Partner - values: ['No', 'Yes']\n",
      "Label encoder Dependents - values: ['No', 'Yes']\n",
      "Label encoder PhoneService - values: ['No', 'Yes']\n",
      "Label encoder MultipleLines - values: ['No', 'No phone service', 'Yes']\n",
      "Label encoder InternetService - values: ['DSL', 'Fiber optic', 'No']\n",
      "Label encoder OnlineSecurity - values: ['No', 'No internet service', 'Yes']\n",
      "Label encoder OnlineBackup - values: ['No', 'No internet service', 'Yes']\n",
      "Label encoder DeviceProtection - values: ['No', 'No internet service', 'Yes']\n",
      "Label encoder TechSupport - values: ['No', 'No internet service', 'Yes']\n",
      "Label encoder StreamingTV - values: ['No', 'No internet service', 'Yes']\n",
      "Label encoder StreamingMovies - values: ['No', 'No internet service', 'Yes']\n",
      "Label encoder Contract - values: ['Month-to-month', 'One year', 'Two year']\n",
      "Label encoder PaperlessBilling - values: ['No', 'Yes']\n",
      "Label encoder PaymentMethod - values: ['Bank transfer (automatic)', 'Credit card (automatic)', 'Electronic check', 'Mailed check']\n",
      "Label encoder Churn - values: ['No', 'Yes']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "categorical_feature_names = []\n",
    "label_encoders = {}\n",
    "for categorical in categorical_features + [target]:\n",
    "    label_encoders[categorical] = LabelEncoder()\n",
    "    df[categorical] = label_encoders[categorical].fit_transform(df[categorical])\n",
    "    names = label_encoders[categorical].classes_.tolist()\n",
    "    print('Label encoder %s - values: %s' % (categorical, names))\n",
    "    if categorical == target:\n",
    "        continue\n",
    "    categorical_feature_names.extend([categorical + '_' + str(name) for name in names])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>2</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>3</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen  Partner  Dependents  tenure  \\\n",
       "0  7590-VHVEG       0              0        1           0       1   \n",
       "1  5575-GNVDE       1              0        0           0      34   \n",
       "2  3668-QPYBK       1              0        0           0       2   \n",
       "3  7795-CFOCW       1              0        0           0      45   \n",
       "4  9237-HQITU       0              0        0           0       2   \n",
       "\n",
       "   PhoneService  MultipleLines  InternetService  OnlineSecurity  ...    \\\n",
       "0             0              1                0               0  ...     \n",
       "1             1              0                0               2  ...     \n",
       "2             1              0                0               2  ...     \n",
       "3             0              1                0               2  ...     \n",
       "4             1              0                1               0  ...     \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                 0            0            0                0         0   \n",
       "1                 2            0            0                0         1   \n",
       "2                 0            0            0                0         0   \n",
       "3                 2            2            0                0         1   \n",
       "4                 0            0            0                0         0   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
       "0                 1              2           29.85         29.85      0  \n",
       "1                 0              3           56.95       1889.50      0  \n",
       "2                 1              3           53.85        108.15      1  \n",
       "3                 0              0           42.30       1840.75      0  \n",
       "4                 1              2           70.70        151.65      1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion, Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn import tree\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "class ItemSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, key):\n",
    "        self.key = key\n",
    "\n",
    "    def fit(self, x, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, df):\n",
    "        return df[self.key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline(\n",
    "    [\n",
    "        (\n",
    "            \"union\",\n",
    "            FeatureUnion(\n",
    "                transformer_list=[\n",
    "                    (\n",
    "                        \"categorical_features\",\n",
    "                        Pipeline(\n",
    "                            [\n",
    "                                (\"selector\", ItemSelector(key=categorical_features)),\n",
    "                                (\"onehot\", OneHotEncoder()),\n",
    "                            ]\n",
    "                        ),\n",
    "                    )\n",
    "                ]\n",
    "                + [\n",
    "                    (\n",
    "                        \"numerical_features\",\n",
    "                        Pipeline(\n",
    "                            [\n",
    "                                (\"selector\", ItemSelector(key=numerical_features)),\n",
    "                                (\"scalar\", StandardScaler()),\n",
    "                            ]\n",
    "                        ),\n",
    "                    )\n",
    "                ]\n",
    "            ),\n",
    "        ),\n",
    "        (\"classifier\", tree.DecisionTreeClassifier(max_depth=3, random_state=42)),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lucas Barros\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:368: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\Lucas Barros\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\Lucas Barros\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:465: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, y, **fit_params).transform(X)\n",
      "C:\\Users\\Lucas Barros\\Anaconda3\\lib\\site-packages\\sklearn\\pipeline.py:451: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_train, df_test = train_test_split(df, test_size=0.25, random_state=42)\n",
    "\n",
    "pipeline.fit(df_train, df_train[target])\n",
    "pred = pipeline.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.94      0.87      1300\n",
      "           1       0.67      0.37      0.48       458\n",
      "\n",
      "   micro avg       0.79      0.79      0.79      1758\n",
      "   macro avg       0.74      0.65      0.67      1758\n",
      "weighted avg       0.77      0.79      0.77      1758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(df_test[target], pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'graphviz' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-44-cd209b0081f2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m                          \u001b[0mfilled\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrounded\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m                          special_characters=True)  \n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mgraph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgraphviz\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSource\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdot_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mgraph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'graphviz' is not defined"
     ]
    }
   ],
   "source": [
    "dot_data = tree.export_graphviz(pipeline.named_steps['classifier'], out_file=None, \n",
    "                         feature_names = categorical_feature_names + numerical_features,\n",
    "                         class_names=[str(el) for el in pipeline.named_steps.classifier.classes_],  \n",
    "                         filled=True, rounded=True,  \n",
    "                         special_characters=True)  \n",
    "graph = graphviz.Source(dot_data) \n",
    "graph"
   ]
  }
 ],
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