sorveteria_clusterizacao_clientes.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Referências: \n",
    "- HUANG, Zhexue. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data mining and knowledge discovery, v. 2, n. 3, p. 283-304, 1998.\n",
    "- https://sproutsocial.com/pt/insights/new-social-media-demographics-pt_br/- \n",
    "- https://www.alura.com.br/curso-online-cluster-analysis\n",
    "- GERON, A. Mãos à Obra Aprendizado de Máquina com Scikit-learn & TensorFlow: Conceitos. Ferramentas e Técnicas Para a Construção de Sistemas Inteligentes, p. 576, 2019\n",
    "- https://towardsdatascience.com/customer-segmentation-using-k-means-clustering-d33964f238c3\n",
    "- https://dwdigital.com.br/canais-de-marketing-para-alcancar-a-geracao-z/\n",
    "- https://www.next4.com.br/marketing-digital-para-idosos-crescimento-exponencial-nos-ultimos-anos-e-muita-oportunidade-para-sua-empresa/\n",
    "- https://consumidormoderno.com.br/2021/06/18/estrategias-marketing-geracao-x/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evitar mensagens de warning\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando bibliotecas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt  \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando as bases de dados\n",
    "portfolio = pd.read_csv(r'C:\\Users\\USER\\Desktop\\vaga estágio\\portfolio_ofertas.csv')\n",
    "clientes =  pd.read_csv(r'C:\\Users\\USER\\Desktop\\vaga estágio\\dados_clientes.csv')\n",
    "eventos =   pd.read_csv(r'C:\\Users\\USER\\Desktop\\vaga estágio\\eventos_ofertas.csv', encoding='latin-1')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.0 Observação inicial e limpeza das bases de dados"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Data frame de portfólio"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>recompensa</th>\n",
       "      <th>canal</th>\n",
       "      <th>valor_minimo</th>\n",
       "      <th>duracao</th>\n",
       "      <th>id</th>\n",
       "      <th>oferta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>['email', 'mobile', 'social']</td>\n",
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       "      <td>informativo</td>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>['web', 'email']</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>desconto</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
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       "      <td>7</td>\n",
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       "      <td>10</td>\n",
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       "      <td>desconto</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>['email', 'mobile', 'social']</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>informativo</td>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>['web', 'email', 'mobile', 'social']</td>\n",
       "      <td>5</td>\n",
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       "      <td>compre 1, leve 2</td>\n",
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       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>['web', 'email', 'mobile']</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
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       "      <td>desconto</td>\n",
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      "text/plain": [
       "   Unnamed: 0  recompensa                                 canal  valor_minimo  \\\n",
       "0           1          10         ['email', 'mobile', 'social']            10   \n",
       "1           2          10  ['web', 'email', 'mobile', 'social']            10   \n",
       "2           3           0            ['web', 'email', 'mobile']             0   \n",
       "3           4           5            ['web', 'email', 'mobile']             5   \n",
       "4           5           5                      ['web', 'email']            20   \n",
       "5           6           3  ['web', 'email', 'mobile', 'social']             7   \n",
       "6           7           2  ['web', 'email', 'mobile', 'social']            10   \n",
       "7           8           0         ['email', 'mobile', 'social']             0   \n",
       "8           9           5  ['web', 'email', 'mobile', 'social']             5   \n",
       "9          10           2            ['web', 'email', 'mobile']            10   \n",
       "\n",
       "   duracao                                id            oferta  \n",
       "0        7  ae264e3637204a6fb9bb56bc8210ddfd  compre 1, leve 2  \n",
       "1        5  4d5c57ea9a6940dd891ad53e9dbe8da0  compre 1, leve 2  \n",
       "2        4  3f207df678b143eea3cee63160fa8bed       informativo  \n",
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       "4       10  0b1e1539f2cc45b7b9fa7c272da2e1d7          desconto  \n",
       "5        7  2298d6c36e964ae4a3e7e9706d1fb8c2          desconto  \n",
       "6       10  fafdcd668e3743c1bb461111dcafc2a4          desconto  \n",
       "7        3  5a8bc65990b245e5a138643cd4eb9837       informativo  \n",
       "8        5  f19421c1d4aa40978ebb69ca19b0e20d  compre 1, leve 2  \n",
       "9        7  2906b810c7d4411798c6938adc9daaa5          desconto  "
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coluna de index estava duplicada\n",
    "portfolio = portfolio.drop(columns='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "recompensa       int64\n",
       "canal           object\n",
       "valor_minimo     int64\n",
       "duracao          int64\n",
       "id              object\n",
       "oferta          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checagem de tipos\n",
    "portfolio.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por se tratar de um DF de 10 linhas, é possível notar que não possui dados incoerentes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Data frame de clientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [
    {
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       "      <td>20170126</td>\n",
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       "      <td>16999</td>\n",
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       "       Unnamed: 0 genero  idade                                id  \\\n",
       "0               1    NaN    118  68be06ca386d4c31939f3a4f0e3dd783   \n",
       "1               2      F     55  0610b486422d4921ae7d2bf64640c50b   \n",
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       "4               5    NaN    118  a03223e636434f42ac4c3df47e8bac43   \n",
       "...           ...    ...    ...                               ...   \n",
       "16995       16996      F     45  6d5f3a774f3d4714ab0c092238f3a1d7   \n",
       "16996       16997      M     61  2cb4f97358b841b9a9773a7aa05a9d77   \n",
       "16997       16998      M     49  01d26f638c274aa0b965d24cefe3183f   \n",
       "16998       16999      F     83  9dc1421481194dcd9400aec7c9ae6366   \n",
       "16999       17000      F     62  e4052622e5ba45a8b96b59aba68cf068   \n",
       "\n",
       "       membro_desde renda_anual  \n",
       "0          20170212         NaN  \n",
       "1          20170715      112000  \n",
       "2          20180712         NaN  \n",
       "3          20170509    1,00E+05  \n",
       "4          20170804         NaN  \n",
       "...             ...         ...  \n",
       "16995      20180604       54000  \n",
       "16996      20180713       72000  \n",
       "16997      20170126       73000  \n",
       "16998      20160307       50000  \n",
       "16999      20170722       82000  \n",
       "\n",
       "[17000 rows x 6 columns]"
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     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genero  idade  id     membro_desde  renda_anual\n",
       "False   False  False  False         False          14825\n",
       "True    False  False  False         True            2175\n",
       "dtype: int64"
      ]
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index duplicado\n",
    "clientes = clientes.drop(columns='Unnamed: 0')\n",
    "\n",
    "# Contagem de NaNs\n",
    "clientes.isna().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17000, 5)"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genero          object\n",
       "idade            int64\n",
       "id              object\n",
       "membro_desde     int64\n",
       "renda_anual     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É possível notar que a coluna de 'membro_desde' contém números inteiros. É preciso convertê-la para o tipo data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16997   2017-01-26\n",
       "16998   2016-03-07\n",
       "16999   2017-07-22\n",
       "Name: membro_desde, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converter coluna de inteiros em tipo data\n",
    "clientes['membro_desde'] = pd.to_datetime(clientes['membro_desde'], format='%Y%m%d')\n",
    "clientes['membro_desde'].tail(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os dados de faltantes de renda e gênero ocorrem sempre em pares em todos os casos. O que pode indicar algum tipo de coleta ou registro de dados problemática. É possível verificar se a idade frequente de 118 anos ocorre juntamente com os outros dois problemas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .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>genero</th>\n",
       "      <th>idade</th>\n",
       "      <th>id</th>\n",
       "      <th>membro_desde</th>\n",
       "      <th>renda_anual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16997</th>\n",
       "      <td>M</td>\n",
       "      <td>49</td>\n",
       "      <td>01d26f638c274aa0b965d24cefe3183f</td>\n",
       "      <td>2017-01-26</td>\n",
       "      <td>73000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16998</th>\n",
       "      <td>F</td>\n",
       "      <td>83</td>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16999</th>\n",
       "      <td>F</td>\n",
       "      <td>62</td>\n",
       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>82000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      genero  idade                                id membro_desde renda_anual\n",
       "16997      M     49  01d26f638c274aa0b965d24cefe3183f   2017-01-26       73000\n",
       "16998      F     83  9dc1421481194dcd9400aec7c9ae6366   2016-03-07       50000\n",
       "16999      F     62  e4052622e5ba45a8b96b59aba68cf068   2017-07-22       82000"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes.isna().value_counts()\n",
    "clientes.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        68be06ca386d4c31939f3a4f0e3dd783\n",
       "2        38fe809add3b4fcf9315a9694bb96ff5\n",
       "4        a03223e636434f42ac4c3df47e8bac43\n",
       "6        8ec6ce2a7e7949b1bf142def7d0e0586\n",
       "7        68617ca6246f4fbc85e91a2a49552598\n",
       "                       ...               \n",
       "16980    5c686d09ca4d475a8f750f2ba07e0440\n",
       "16982    d9ca82f550ac4ee58b6299cf1e5c824a\n",
       "16989    ca45ee1883624304bac1e4c8a114f045\n",
       "16991    a9a20fa8b5504360beb4e7c8712f8306\n",
       "16994    c02b10e8752c4d8e9b73f918558531f7\n",
       "Name: id, Length: 2175, dtype: object"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Salvando os IDs removidos em outro para abatê-los do DF\n",
    "ids_removidos = clientes[clientes['idade'] == 118]\n",
    "ids_removidos = pd.Series(ids_removidos['id'])\n",
    "ids_removidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genero  idade  id     membro_desde  renda_anual\n",
       "False   False  False  False         False          14825\n",
       "dtype: int64"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dados faltantes de gênero\n",
    "clientes = clientes[clientes['idade'] != 118]\n",
    "clientes.isna().value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como o esperado, todos os registros com a idade notoriamente estranha de 118 anos eram os que também tinham renda e gênero como NaNs. Assim, não faria sentido completar com a média a renda ou mesmo aleatoriezar valores de gênero, pois isso apenas diluiria os dados relevantes e não seria mantida nenhuma informação relevante na base de dados. O melhor a se fazer realmente é descartar os dados incoerentes."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A renda anual deve ser convertida para algum tipo numérico para que seja utilizada estatisticamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1          112000\n",
       "3        10000000\n",
       "5           70000\n",
       "8           53000\n",
       "12          51000\n",
       "           ...   \n",
       "16995       54000\n",
       "16996       72000\n",
       "16997       73000\n",
       "16998       50000\n",
       "16999       82000\n",
       "Name: renda_anual, Length: 14825, dtype: int32"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removendo a vírgula e a notação científica e convertendo para inteiro\n",
    "clientes['renda_anual'] = clientes['renda_anual'].str.replace(',', '').astype(float).astype(int)\n",
    "clientes['renda_anual']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "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>idade</th>\n",
       "      <th>renda_anual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14825.000000</td>\n",
       "      <td>1.482500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.393524</td>\n",
       "      <td>1.235028e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.383705</td>\n",
       "      <td>7.591561e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>4.900000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>6.400000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>8.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              idade   renda_anual\n",
       "count  14825.000000  1.482500e+04\n",
       "mean      54.393524  1.235028e+05\n",
       "std       17.383705  7.591561e+05\n",
       "min       18.000000  3.000000e+04\n",
       "25%       42.000000  4.900000e+04\n",
       "50%       55.000000  6.400000e+04\n",
       "75%       66.000000  8.000000e+04\n",
       "max      101.000000  1.000000e+07"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os valores máximo e mínimo parecem coerentes para idade e renda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "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>genero</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14825</td>\n",
       "      <td>14825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>14825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>M</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>8484</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genero                                id\n",
       "count   14825                             14825\n",
       "unique      3                             14825\n",
       "top         M  0610b486422d4921ae7d2bf64640c50b\n",
       "freq     8484                                 1"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes.describe(include = ['object'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Não há IDs repetidos. \n",
    "Há 3 descrições descrições de gênero:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'M', 'O'], dtype=object)"
      ]
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes['genero'].unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Data frame de eventos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "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",
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       "\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>Unnamed: 0</th>\n",
       "      <th>cliente</th>\n",
       "      <th>tempo_decorrido</th>\n",
       "      <th>valor</th>\n",
       "      <th>id_oferta</th>\n",
       "      <th>recompensa</th>\n",
       "      <th>tipo_evento</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oferta recebida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oferta recebida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oferta recebida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8ec6ce2a7e7949b1bf142def7d0e0586</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oferta recebida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>oferta recebida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306529</th>\n",
       "      <td>306530</td>\n",
       "      <td>b3a1272bc9904337b331bf348c3e8c17</td>\n",
       "      <td>714</td>\n",
       "      <td>1.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transacao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306530</th>\n",
       "      <td>306531</td>\n",
       "      <td>68213b08d99a4ae1b0dcb72aebd9aa35</td>\n",
       "      <td>714</td>\n",
       "      <td>9.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transacao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306531</th>\n",
       "      <td>306532</td>\n",
       "      <td>a00058cf10334a308c68e7631c529907</td>\n",
       "      <td>714</td>\n",
       "      <td>3.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transacao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306532</th>\n",
       "      <td>306533</td>\n",
       "      <td>76ddbd6576844afe811f1a3c0fbb5bec</td>\n",
       "      <td>714</td>\n",
       "      <td>3.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transacao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306533</th>\n",
       "      <td>306534</td>\n",
       "      <td>c02b10e8752c4d8e9b73f918558531f7</td>\n",
       "      <td>714</td>\n",
       "      <td>4.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transacao</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>306534 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0                           cliente  tempo_decorrido  valor  \\\n",
       "0                1  78afa995795e4d85b5d9ceeca43f5fef                0    NaN   \n",
       "1                2  a03223e636434f42ac4c3df47e8bac43                0    NaN   \n",
       "2                3  e2127556f4f64592b11af22de27a7932                0    NaN   \n",
       "3                4  8ec6ce2a7e7949b1bf142def7d0e0586                0    NaN   \n",
       "4                5  68617ca6246f4fbc85e91a2a49552598                0    NaN   \n",
       "...            ...                               ...              ...    ...   \n",
       "306529      306530  b3a1272bc9904337b331bf348c3e8c17              714   1.59   \n",
       "306530      306531  68213b08d99a4ae1b0dcb72aebd9aa35              714   9.53   \n",
       "306531      306532  a00058cf10334a308c68e7631c529907              714   3.61   \n",
       "306532      306533  76ddbd6576844afe811f1a3c0fbb5bec              714   3.53   \n",
       "306533      306534  c02b10e8752c4d8e9b73f918558531f7              714   4.05   \n",
       "\n",
       "                               id_oferta  recompensa      tipo_evento  \n",
       "0       9b98b8c7a33c4b65b9aebfe6a799e6d9         NaN  oferta recebida  \n",
       "1       0b1e1539f2cc45b7b9fa7c272da2e1d7         NaN  oferta recebida  \n",
       "2       2906b810c7d4411798c6938adc9daaa5         NaN  oferta recebida  \n",
       "3       fafdcd668e3743c1bb461111dcafc2a4         NaN  oferta recebida  \n",
       "4       4d5c57ea9a6940dd891ad53e9dbe8da0         NaN  oferta recebida  \n",
       "...                                  ...         ...              ...  \n",
       "306529                               NaN         NaN        transacao  \n",
       "306530                               NaN         NaN        transacao  \n",
       "306531                               NaN         NaN        transacao  \n",
       "306532                               NaN         NaN        transacao  \n",
       "306533                               NaN         NaN        transacao  \n",
       "\n",
       "[306534 rows x 7 columns]"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(306534, 7)"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventos.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cliente  tempo_decorrido  valor  id_oferta  recompensa  tipo_evento\n",
       "False    False            False  True       True        False          138953\n",
       "                          True   False      True        False          134002\n",
       "                                            False       False           33579\n",
       "dtype: int64"
      ]
     },
     "execution_count": 516,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventos = eventos.drop(columns='Unnamed: 0')\n",
    "eventos.isna().value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retirando os registros dos IDs que continham problemas de falta de informação já no DF de clientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        68be06ca386d4c31939f3a4f0e3dd783\n",
       "2        38fe809add3b4fcf9315a9694bb96ff5\n",
       "4        a03223e636434f42ac4c3df47e8bac43\n",
       "6        8ec6ce2a7e7949b1bf142def7d0e0586\n",
       "7        68617ca6246f4fbc85e91a2a49552598\n",
       "                       ...               \n",
       "16980    5c686d09ca4d475a8f750f2ba07e0440\n",
       "16982    d9ca82f550ac4ee58b6299cf1e5c824a\n",
       "16989    ca45ee1883624304bac1e4c8a114f045\n",
       "16991    a9a20fa8b5504360beb4e7c8712f8306\n",
       "16994    c02b10e8752c4d8e9b73f918558531f7\n",
       "Name: id, Length: 2175, dtype: object"
      ]
     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Série contendo todos os registros com erros obtida na seção 2.2 \n",
    "ids_removidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cliente  tempo_decorrido  valor  id_oferta  recompensa  tipo_evento\n",
       "False    False            False  True       True        False          123957\n",
       "                          True   False      True        False          116361\n",
       "                                            False       False           32444\n",
       "dtype: int64"
      ]
     },
     "execution_count": 518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Indexar o DF usando  isin() para remover as linhas com valores na série 'ids_removidos'\n",
    "eventos = eventos[~eventos['cliente'].isin(ids_removidos)]\n",
    "eventos.isna().value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É possível notar que os IDs que foram excluídos realmente se tratavam de erros, tendo em vista que nenhum registro foi apagado em meio a todos presentes na série 'ids_removidos'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cliente             object\n",
       "tempo_decorrido      int64\n",
       "valor              float64\n",
       "id_oferta           object\n",
       "recompensa         float64\n",
       "tipo_evento         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventos.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "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>tempo_decorrido</th>\n",
       "      <th>valor</th>\n",
       "      <th>recompensa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>272762.000000</td>\n",
       "      <td>123957.000000</td>\n",
       "      <td>32444.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.707694</td>\n",
       "      <td>13.996325</td>\n",
       "      <td>4.948249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>200.359087</td>\n",
       "      <td>31.749420</td>\n",
       "      <td>2.905376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>186.000000</td>\n",
       "      <td>3.660000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>408.000000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>528.000000</td>\n",
       "      <td>19.130000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>714.000000</td>\n",
       "      <td>1062.280000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tempo_decorrido          valor    recompensa\n",
       "count    272762.000000  123957.000000  32444.000000\n",
       "mean        366.707694      13.996325      4.948249\n",
       "std         200.359087      31.749420      2.905376\n",
       "min           0.000000       0.050000      2.000000\n",
       "25%         186.000000       3.660000      2.000000\n",
       "50%         408.000000      10.800000      5.000000\n",
       "75%         528.000000      19.130000      5.000000\n",
       "max         714.000000    1062.280000     10.000000"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventos.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As três variáveis numéricas parecem estar com valores coerentes, considerando o projeto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "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>cliente</th>\n",
       "      <th>id_oferta</th>\n",
       "      <th>tipo_evento</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>272762</td>\n",
       "      <td>148805</td>\n",
       "      <td>272762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>14825</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>94de646f7b6041228ca7dec82adb97d2</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>transacao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>51</td>\n",
       "      <td>18062</td>\n",
       "      <td>123957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 cliente                         id_oferta  \\\n",
       "count                             272762                            148805   \n",
       "unique                             14825                                10   \n",
       "top     94de646f7b6041228ca7dec82adb97d2  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "freq                                  51                             18062   \n",
       "\n",
       "       tipo_evento  \n",
       "count       272762  \n",
       "unique           4  \n",
       "top      transacao  \n",
       "freq        123957  "
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descrição de variáveis não numéricas\n",
    "eventos.describe(include = ['object'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Há exatamente o mesmo número de registros não nulos de clientes no DF eventos, bem como de IDs no DF clientes (14825 números únicos). O que indica que as suposições anteriores de que os registros problemáricos não passavam de erros era verdadeira."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fafdcd668e3743c1bb461111dcafc2a4    18062\n",
       "2298d6c36e964ae4a3e7e9706d1fb8c2    17920\n",
       "f19421c1d4aa40978ebb69ca19b0e20d    16989\n",
       "ae264e3637204a6fb9bb56bc8210ddfd    16241\n",
       "4d5c57ea9a6940dd891ad53e9dbe8da0    16232\n",
       "9b98b8c7a33c4b65b9aebfe6a799e6d9    14372\n",
       "2906b810c7d4411798c6938adc9daaa5    14002\n",
       "5a8bc65990b245e5a138643cd4eb9837    12516\n",
       "0b1e1539f2cc45b7b9fa7c272da2e1d7    12327\n",
       "3f207df678b143eea3cee63160fa8bed    10144\n",
       "Name: id_oferta, dtype: int64"
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualização dos 10 IDs de oferta existentes na base de dados\n",
    "eventos['id_oferta'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transacao             123957\n",
       "oferta recebida        66501\n",
       "oferta visualizada     49860\n",
       "oferta concluída       32444\n",
       "Name: tipo_evento, dtype: int64"
      ]
     },
     "execution_count": 523,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Contagem dos tipos de evento\n",
    "eventos['tipo_evento'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depreende-se que os valores correspondem exatamente às secções obtidas ao contarem-se as quantidades de na tabela. O que é um forte indicativo de que as categorias de oferta estão correlacionadas com colunas NaN ou não. Segue a checagem da conjectura:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cliente  tempo_decorrido  valor  id_oferta  recompensa  tipo_evento\n",
       "False    False            False  True       True        False          123957\n",
       "dtype: int64"
      ]
     },
     "execution_count": 524,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NaNs em evento 'transacao'\n",
    "eventos[eventos['tipo_evento'] == 'transacao'].isna().value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como o esperado, em todos os casos que há 'id_oferta' e 'recompensa' iguais a NaN, o 'tipo_evento' é 'transacao'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cliente  tempo_decorrido  valor  id_oferta  recompensa  tipo_evento\n",
       "False    False            True   False      False       False          32444\n",
       "dtype: int64"
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NaNs em evento 'oferta concluída'\n",
    "eventos[eventos['tipo_evento'] == 'oferta concluída'].isna().value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seguindo a lógica da célula imediatamente anterior, todos os casos em que o único NaN do registro ocorre na coluna 'valor', o evento 'oferta concluída' é o valor da coluna 'tipo_evento'.\n",
    "\n",
    "Como consequência, em todos os demais casos ocorre NaN nas colunas de 'valor' e 'recompensa'. São todos registros que  possuem como valor na coluna de 'tipo_evento' como 'oferta visualizada' ou 'oferta recebida'."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Análise Exploratória de Dados (Clientes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estilização das visualizações\n",
    "tema = sns.set_theme(style='darkgrid')\n",
    "cores = sns.color_palette(\"husl\", 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['genero', 'idade', 'id', 'membro_desde', 'renda_anual'], dtype='object')"
      ]
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Observação das colunas presentes\n",
    "clientes.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico de pizza sobre gênero\n",
    "\n",
    "clientes['genero'].value_counts().plot\\\n",
    "  .pie(autopct = '%1.1f%%', explode=(0.01, 0.01, 0.01), figsize =(6, 6),colors=cores)\\\n",
    "  .set_title('Gênero em porcentagem').set_fontsize('20')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função genérica para boxplot\n",
    "\n",
    "def boxplot(coluna, max_y, titulo, data):\n",
    "  f, ax = plt.subplots(figsize=(8, 6))\n",
    "  fig = sns.boxplot(y=coluna, data=data)\n",
    "  fig.set_title(titulo).set_fontsize('16')\n",
    "  fig.axis(ymin = 0, ymax = max_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função genérica para violinplot\n",
    "\n",
    "def violinplot(coluna, max_y, titulo, data):\n",
    "  f, ax = plt.subplots(figsize=(8, 6))\n",
    "  fig = sns.violinplot(y=coluna, data=data)\n",
    "  fig.set_title(titulo).set_fontsize('16')\n",
    "  fig.axis(ymin = 0, ymax = max_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separar as colunas por tipos para facilitar a análise\n",
    "\n",
    "categoricas_clientes = clientes.dtypes == 'object'\n",
    "categoricas_clientes = list(categoricas_clientes[categoricas_clientes].index)\n",
    "categoricas_clientes.remove('id') # Coluna não importante para análise\n",
    "\n",
    "numericas_clientes = clientes.dtypes != 'object'\n",
    "numericas_clientes = list(numericas_clientes[numericas_clientes].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['idade', 'membro_desde', 'renda_anual']"
      ]
     },
     "execution_count": 532,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numericas_clientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot(coluna=numericas_clientes[0],\n",
    "        data=clientes,\n",
    "        max_y=1.1*max(clientes[numericas_clientes[0]]),\n",
    "        titulo=numericas_clientes[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "violinplot(coluna=numericas_clientes[0],\n",
    "        data=clientes,\n",
    "        max_y=1.1*max(clientes[numericas_clientes[0]]),\n",
    "        titulo=numericas_clientes[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot(coluna=numericas_clientes[2],\n",
    "        data=clientes,\n",
    "        max_y=200000,\n",
    "        titulo=numericas_clientes[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "violinplot(coluna=numericas_clientes[2],\n",
    "        data=clientes,\n",
    "        max_y=500000,\n",
    "        titulo=numericas_clientes[2])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Agrupamento ou clusterização"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A estratégia para o presente tópico foi baseada principalmente em:\n",
    "- Curso de clusterização com python da Alura: https://www.alura.com.br/curso-online-cluster-analysis\n",
    "- Livro de ML: GERON, A. Mãos à Obra Aprendizado de Máquina com Scikit-learn & TensorFlow: Conceitos. Ferramentas e Técnicas Para a Construção de Sistemas Inteligentes, p. 576, 2019\n",
    "- Página web: https://towardsdatascience.com/customer-segmentation-using-k-means-clustering-d33964f238c3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Preparação dos dados"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para agrupar os clientes é preciso que o DF esteja segundo o padrão requerido pelo algoritmo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "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>genero</th>\n",
       "      <th>idade</th>\n",
       "      <th>id</th>\n",
       "      <th>membro_desde</th>\n",
       "      <th>renda_anual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>112000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>75</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>10000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>68</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M</td>\n",
       "      <td>65</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>53000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M</td>\n",
       "      <td>58</td>\n",
       "      <td>2eeac8d8feae4a8cad5a6af0499a211d</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>51000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>16995</th>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>6d5f3a774f3d4714ab0c092238f3a1d7</td>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>54000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16996</th>\n",
       "      <td>M</td>\n",
       "      <td>61</td>\n",
       "      <td>2cb4f97358b841b9a9773a7aa05a9d77</td>\n",
       "      <td>2018-07-13</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16997</th>\n",
       "      <td>M</td>\n",
       "      <td>49</td>\n",
       "      <td>01d26f638c274aa0b965d24cefe3183f</td>\n",
       "      <td>2017-01-26</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>16998</th>\n",
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       "      <td>83</td>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>2016-03-07</td>\n",
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       "    <tr>\n",
       "      <th>16999</th>\n",
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       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>82000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      genero  idade                                id membro_desde  \\\n",
       "1          F     55  0610b486422d4921ae7d2bf64640c50b   2017-07-15   \n",
       "3          F     75  78afa995795e4d85b5d9ceeca43f5fef   2017-05-09   \n",
       "5          M     68  e2127556f4f64592b11af22de27a7932   2018-04-26   \n",
       "8          M     65  389bc3fa690240e798340f5a15918d5c   2018-02-09   \n",
       "12         M     58  2eeac8d8feae4a8cad5a6af0499a211d   2017-11-11   \n",
       "...      ...    ...                               ...          ...   \n",
       "16995      F     45  6d5f3a774f3d4714ab0c092238f3a1d7   2018-06-04   \n",
       "16996      M     61  2cb4f97358b841b9a9773a7aa05a9d77   2018-07-13   \n",
       "16997      M     49  01d26f638c274aa0b965d24cefe3183f   2017-01-26   \n",
       "16998      F     83  9dc1421481194dcd9400aec7c9ae6366   2016-03-07   \n",
       "16999      F     62  e4052622e5ba45a8b96b59aba68cf068   2017-07-22   \n",
       "\n",
       "       renda_anual  \n",
       "1           112000  \n",
       "3         10000000  \n",
       "5            70000  \n",
       "8            53000  \n",
       "12           51000  \n",
       "...            ...  \n",
       "16995        54000  \n",
       "16996        72000  \n",
       "16997        73000  \n",
       "16998        50000  \n",
       "16999        82000  \n",
       "\n",
       "[14825 rows x 5 columns]"
      ]
     },
     "execution_count": 537,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65200.77351065273"
      ]
     },
     "execution_count": 538,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Substituindo os outliers de 'renda_anual' pelo valor médio da coluna sem eles\n",
    "\n",
    "renda_media = clientes[clientes['renda_anual'] != 10000000]['renda_anual'].mean()\n",
    "renda_media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2018-05-12</td>\n",
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       "      <td>2018-07-12</td>\n",
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       "      <th>16462</th>\n",
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       "      <td>76</td>\n",
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       "      <td>2015-12-10</td>\n",
       "      <td>65200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      genero  idade                                id membro_desde  \\\n",
       "3          F     75  78afa995795e4d85b5d9ceeca43f5fef   2017-05-09   \n",
       "21         M     64  4b0da7e80e5945209a1fdddfe813dbe0   2017-09-09   \n",
       "390        F     41  5084c3bf96464d63bc54f298ce45aef0   2016-04-14   \n",
       "425        M     51  227f2d69e46a4899b70d48182822cff6   2018-05-12   \n",
       "526        F     57  f92d608f58bf40178cc3c9568750c559   2018-01-17   \n",
       "...      ...    ...                               ...          ...   \n",
       "15605      M     41  269dce9ba8004504ae3a587a7df9db13   2015-10-24   \n",
       "15943      M     65  6b1c9e0cc8a54186a4129131a1726738   2018-01-05   \n",
       "16158      M     63  156e898fd57b431683d6e72e4ab6eb9c   2017-10-18   \n",
       "16289      F     71  7b8d950572954f79b8f47809099c77e9   2018-07-12   \n",
       "16462      F     76  d0b518c8bc034ea2a2ac83e34e55be97   2015-12-10   \n",
       "\n",
       "       renda_anual  \n",
       "3            65200  \n",
       "21           65200  \n",
       "390          65200  \n",
       "425          65200  \n",
       "526          65200  \n",
       "...            ...  \n",
       "15605        65200  \n",
       "15943        65200  \n",
       "16158        65200  \n",
       "16289        65200  \n",
       "16462        65200  \n",
       "\n",
       "[87 rows x 5 columns]"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alterar o valor das células na Coluna2 que são maiores que 30\n",
    "clientes.loc[clientes['renda_anual'] == 10000000, 'renda_anual'] = int(renda_media)\n",
    "clientes[clientes['renda_anual'] == int(renda_media)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>genero</th>\n",
       "      <th>idade</th>\n",
       "      <th>id</th>\n",
       "      <th>membro_desde</th>\n",
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       "      <td>55</td>\n",
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       "      <td>2017-07-15</td>\n",
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       "      <td>2018-04-26</td>\n",
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       "      <td>65</td>\n",
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       "      <td>51000</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>F</td>\n",
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       "      <td>6d5f3a774f3d4714ab0c092238f3a1d7</td>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>54000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14821</th>\n",
       "      <td>M</td>\n",
       "      <td>61</td>\n",
       "      <td>2cb4f97358b841b9a9773a7aa05a9d77</td>\n",
       "      <td>2018-07-13</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14822</th>\n",
       "      <td>M</td>\n",
       "      <td>49</td>\n",
       "      <td>01d26f638c274aa0b965d24cefe3183f</td>\n",
       "      <td>2017-01-26</td>\n",
       "      <td>73000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14823</th>\n",
       "      <td>F</td>\n",
       "      <td>83</td>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14824</th>\n",
       "      <td>F</td>\n",
       "      <td>62</td>\n",
       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>82000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "      genero  idade                                id membro_desde  \\\n",
       "0          F     55  0610b486422d4921ae7d2bf64640c50b   2017-07-15   \n",
       "1          F     75  78afa995795e4d85b5d9ceeca43f5fef   2017-05-09   \n",
       "2          M     68  e2127556f4f64592b11af22de27a7932   2018-04-26   \n",
       "3          M     65  389bc3fa690240e798340f5a15918d5c   2018-02-09   \n",
       "4          M     58  2eeac8d8feae4a8cad5a6af0499a211d   2017-11-11   \n",
       "...      ...    ...                               ...          ...   \n",
       "14820      F     45  6d5f3a774f3d4714ab0c092238f3a1d7   2018-06-04   \n",
       "14821      M     61  2cb4f97358b841b9a9773a7aa05a9d77   2018-07-13   \n",
       "14822      M     49  01d26f638c274aa0b965d24cefe3183f   2017-01-26   \n",
       "14823      F     83  9dc1421481194dcd9400aec7c9ae6366   2016-03-07   \n",
       "14824      F     62  e4052622e5ba45a8b96b59aba68cf068   2017-07-22   \n",
       "\n",
       "       renda_anual  \n",
       "0           112000  \n",
       "1            65200  \n",
       "2            70000  \n",
       "3            53000  \n",
       "4            51000  \n",
       "...            ...  \n",
       "14820        54000  \n",
       "14821        72000  \n",
       "14822        73000  \n",
       "14823        50000  \n",
       "14824        82000  \n",
       "\n",
       "[14825 rows x 5 columns]"
      ]
     },
     "execution_count": 540,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Consertando o índice que está fora de ordem devido às retiradas de elementos\n",
    "clientes.reset_index(drop=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De acordo com Huang (1998)¹, o algoritmo k-means não pode ser utilizado para agrupar variáveis cateóricas, tendo em vista que não tem sentido calcular algum tipo de distância euclidiana entre suas categorias. O autor chega a propor uma estratégia para que seja realizado o agrupamento misto, isso é, com variáveis de tipo categórico e outras de tipo numérico. No entanto, optou-se por se valer de uma solução simplificada para o problema por duas razões. Primeiramente por questões de complexidade. O fator mais importante, porém, é que para as estratégias de marketing utilizadas pela empresa, o gênero do cliente não é um fator que apresenta relevância aparentemente. Os  eventos de publicidade são feitos com base em tipos de informação ou ofertas, e atingem o cliente em canais de comunicação diversos para cada evento (web, e-mail, celular, etc). Para ambas as categorias o gênero não parece ter relação com a funcionalidade do marketing. Ao contrário de variáveis como renda, em que promoções tendem a ter um alto impacto. A variável de idade também é de se esperar que seja relevante, tendo em vista que certas redes sociais ou aplicativos e canais de comunicação são de preferência de certa faixa etária, ou mesmo feitos com design para tipos de usuário de certa idade. \n",
    "Como publicado pelo website Sprout Social, a faixa etária está bastante relacionada à utilização de cada rede social². \n",
    "\n",
    "¹ HUANG, Zhexue. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data mining and knowledge discovery, v. 2, n. 3, p. 283-304, 1998.\n",
    "\n",
    "² https://sproutsocial.com/pt/insights/new-social-media-demographics-pt_br/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75</td>\n",
       "      <td>65200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>53000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58</td>\n",
       "      <td>51000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14820</th>\n",
       "      <td>45</td>\n",
       "      <td>54000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14821</th>\n",
       "      <td>61</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14822</th>\n",
       "      <td>49</td>\n",
       "      <td>73000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14823</th>\n",
       "      <td>83</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14824</th>\n",
       "      <td>62</td>\n",
       "      <td>82000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       idade  renda_anual\n",
       "0         55       112000\n",
       "1         75        65200\n",
       "2         68        70000\n",
       "3         65        53000\n",
       "4         58        51000\n",
       "...      ...          ...\n",
       "14820     45        54000\n",
       "14821     61        72000\n",
       "14822     49        73000\n",
       "14823     83        50000\n",
       "14824     62        82000\n",
       "\n",
       "[14825 rows x 2 columns]"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para criar um perfil de clientes as colunas 'id' e 'membro_desde' não são úteis \n",
    "clientes_processado = clientes.drop(columns=['id', 'membro_desde', 'genero']).reset_index(drop=True)\n",
    "clientes_processado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.48887646e-02,  2.18347327e+00],\n",
       "       [ 1.18543027e+00, -3.58772636e-05],\n",
       "       [ 7.82740744e-01,  2.23913779e-01],\n",
       "       ...,\n",
       "       [-3.10273688e-01,  3.63882314e-01],\n",
       "       [ 1.64564687e+00, -7.09209788e-01],\n",
       "       [ 4.37578292e-01,  7.83787919e-01]])"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ajustando a escala para o modelo.\n",
    "\n",
    "# O ajuste de escala é necessário para que a magnitude dos valores não \n",
    "# desbalanceie o peso de cada coluna no processo de agrupamento.\n",
    "\n",
    "scaler = StandardScaler()\n",
    "clientes_processado = scaler.fit_transform(clientes_processado)\n",
    "clientes_processado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [],
   "source": [
    "clientes_processado = pd.DataFrame(data = clientes_processado,columns = ('idade_processada', 'renda_anual_processada'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Verificação do número ótimo de grupos através do Método de Elbow\n",
    "\n",
    "wcss = [] # Cluster feito pela diferença quadrática\n",
    "\n",
    "SEED = 101\n",
    "\n",
    "for k in range(1,15):\n",
    "    kmeans = KMeans(n_clusters=k, init='k-means++', random_state=SEED)\n",
    "    kmeans.fit(clientes_processado)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "\n",
    "plt.figure(figsize=(12,6))    \n",
    "plt.grid()\n",
    "plt.plot(range(1,15), wcss, linewidth=2, color='green', marker ='*')\n",
    "plt.xlabel('Valor de k')\n",
    "plt.xticks(np.arange(1,11,1))\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pode-se notar que o número ótimo de grupos para a base com apenas as variáveis de idade e renda anual é 5. O chamado joelho da curva se localiza no ponto de valor de k = 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'reset_index'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\USER\\Desktop\\vaga estágio\\projeto_estagio_antonio.ipynb Cell 77\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/USER/Desktop/vaga%20est%C3%A1gio/projeto_estagio_antonio.ipynb#Y131sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m km \u001b[39m=\u001b[39m KMeans(n_clusters\u001b[39m=\u001b[39m\u001b[39m5\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/USER/Desktop/vaga%20est%C3%A1gio/projeto_estagio_antonio.ipynb#Y131sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39m# Define-se a qual grupo de cada cliente\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/USER/Desktop/vaga%20est%C3%A1gio/projeto_estagio_antonio.ipynb#Y131sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m grupos \u001b[39m=\u001b[39m km\u001b[39m.\u001b[39;49mfit_predict(clientes_processado)\u001b[39m.\u001b[39;49mreset_index(drop\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'reset_index'"
     ]
    }
   ],
   "source": [
    "# Definição da função de agrupamento\n",
    "km = KMeans(n_clusters=5)\n",
    "\n",
    "# Define-se a qual grupo de cada cliente\n",
    "grupos = km.fit_predict(clientes_processado)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idade_processada</th>\n",
       "      <th>renda_anual_processada</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.034889</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.185430</td>\n",
       "      <td>-0.000036</td>\n",
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       "    <tr>\n",
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       "      <td>0.782741</td>\n",
       "      <td>0.223914</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>0.610160</td>\n",
       "      <td>-0.569241</td>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>14821</th>\n",
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       "      <th>14822</th>\n",
       "      <td>-0.310274</td>\n",
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       "      <td>1.645647</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       idade_processada  renda_anual_processada\n",
       "0              0.034889                2.183473\n",
       "1              1.185430               -0.000036\n",
       "2              0.782741                0.223914\n",
       "3              0.610160               -0.569241\n",
       "4              0.207470               -0.662554\n",
       "...                 ...                     ...\n",
       "14820         -0.540382               -0.522585\n",
       "14821          0.380051                0.317226\n",
       "14822         -0.310274                0.363882\n",
       "14823          1.645647               -0.709210\n",
       "14824          0.437578                0.783788\n",
       "\n",
       "[14825 rows x 2 columns]"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes_processado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idade_processada</th>\n",
       "      <th>renda_anual_processada</th>\n",
       "      <th>id</th>\n",
       "      <th>grupo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.034889</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>3</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0.783788</td>\n",
       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       idade_processada  renda_anual_processada  \\\n",
       "0              0.034889                2.183473   \n",
       "1              1.185430               -0.000036   \n",
       "2              0.782741                0.223914   \n",
       "3              0.610160               -0.569241   \n",
       "4              0.207470               -0.662554   \n",
       "...                 ...                     ...   \n",
       "14820         -0.540382               -0.522585   \n",
       "14821          0.380051                0.317226   \n",
       "14822         -0.310274                0.363882   \n",
       "14823          1.645647               -0.709210   \n",
       "14824          0.437578                0.783788   \n",
       "\n",
       "                                     id  grupo  \n",
       "0      0610b486422d4921ae7d2bf64640c50b      2  \n",
       "1      78afa995795e4d85b5d9ceeca43f5fef      1  \n",
       "2      e2127556f4f64592b11af22de27a7932      1  \n",
       "3      389bc3fa690240e798340f5a15918d5c      3  \n",
       "4      2eeac8d8feae4a8cad5a6af0499a211d      3  \n",
       "...                                 ...    ...  \n",
       "14820  6d5f3a774f3d4714ab0c092238f3a1d7      0  \n",
       "14821  2cb4f97358b841b9a9773a7aa05a9d77      4  \n",
       "14822  01d26f638c274aa0b965d24cefe3183f      4  \n",
       "14823  9dc1421481194dcd9400aec7c9ae6366      1  \n",
       "14824  e4052622e5ba45a8b96b59aba68cf068      4  \n",
       "\n",
       "[14825 rows x 4 columns]"
      ]
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coluna de grupo adicionada à base de dados do modelo\n",
    "clientes_processado['grupo'] = grupos\n",
    "clientes_processado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6       -0.895835\n",
       "16      -0.615897\n",
       "21       0.223914\n",
       "23      -1.502365\n",
       "26      -0.102679\n",
       "           ...   \n",
       "14807   -0.522585\n",
       "14808   -0.335960\n",
       "14813   -0.475929\n",
       "14818    0.317226\n",
       "14820   -0.522585\n",
       "Name: renda_anual_processada, Length: 3433, dtype: float64"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes_processado['renda_anual_processada'][clientes_processado['grupo'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualização da distrbuição dos grupos ou clusters num plano de idade por renda anual\n",
    "\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(clientes_processado['renda_anual_processada'][clientes_processado['grupo'] == 0], clientes_processado['idade_processada'][clientes_processado['grupo'] == 0], c='blue', s=60)\n",
    "ax.scatter(clientes_processado['renda_anual_processada'][clientes_processado['grupo'] == 1], clientes_processado['idade_processada'][clientes_processado['grupo'] == 1], c='green', s=60)\n",
    "ax.scatter(clientes_processado['renda_anual_processada'][clientes_processado['grupo'] == 2], clientes_processado['idade_processada'][clientes_processado['grupo'] == 2], c='red', s=60)\n",
    "ax.scatter(clientes_processado['renda_anual_processada'][clientes_processado['grupo'] == 3], clientes_processado['idade_processada'][clientes_processado['grupo'] == 3], c='purple', s=60)\n",
    "ax.scatter(clientes_processado['renda_anual_processada'][clientes_processado['grupo'] == 4], clientes_processado['idade_processada'][clientes_processado['grupo'] == 4], c='orange', s=60)\n",
    "\n",
    "plt.xlabel('Renda anual')\n",
    "plt.ylabel('Idade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "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>idade_processada</th>\n",
       "      <th>renda_anual_processada</th>\n",
       "      <th>id</th>\n",
       "      <th>grupo</th>\n",
       "      <th>id</th>\n",
       "      <th>renda_anual</th>\n",
       "      <th>idade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.034889</td>\n",
       "      <td>2.183473</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>2</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>112000</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.185430</td>\n",
       "      <td>-0.000036</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>1</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>65200</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.782741</td>\n",
       "      <td>0.223914</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>1</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>70000</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.610160</td>\n",
       "      <td>-0.569241</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>3</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>53000</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.207470</td>\n",
       "      <td>-0.662554</td>\n",
       "      <td>2eeac8d8feae4a8cad5a6af0499a211d</td>\n",
       "      <td>3</td>\n",
       "      <td>2eeac8d8feae4a8cad5a6af0499a211d</td>\n",
       "      <td>51000</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14820</th>\n",
       "      <td>-0.540382</td>\n",
       "      <td>-0.522585</td>\n",
       "      <td>6d5f3a774f3d4714ab0c092238f3a1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>6d5f3a774f3d4714ab0c092238f3a1d7</td>\n",
       "      <td>54000</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14821</th>\n",
       "      <td>0.380051</td>\n",
       "      <td>0.317226</td>\n",
       "      <td>2cb4f97358b841b9a9773a7aa05a9d77</td>\n",
       "      <td>4</td>\n",
       "      <td>2cb4f97358b841b9a9773a7aa05a9d77</td>\n",
       "      <td>72000</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14822</th>\n",
       "      <td>-0.310274</td>\n",
       "      <td>0.363882</td>\n",
       "      <td>01d26f638c274aa0b965d24cefe3183f</td>\n",
       "      <td>4</td>\n",
       "      <td>01d26f638c274aa0b965d24cefe3183f</td>\n",
       "      <td>73000</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14823</th>\n",
       "      <td>1.645647</td>\n",
       "      <td>-0.709210</td>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>1</td>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>50000</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14824</th>\n",
       "      <td>0.437578</td>\n",
       "      <td>0.783788</td>\n",
       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>4</td>\n",
       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>82000</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       idade_processada  renda_anual_processada  \\\n",
       "0              0.034889                2.183473   \n",
       "1              1.185430               -0.000036   \n",
       "2              0.782741                0.223914   \n",
       "3              0.610160               -0.569241   \n",
       "4              0.207470               -0.662554   \n",
       "...                 ...                     ...   \n",
       "14820         -0.540382               -0.522585   \n",
       "14821          0.380051                0.317226   \n",
       "14822         -0.310274                0.363882   \n",
       "14823          1.645647               -0.709210   \n",
       "14824          0.437578                0.783788   \n",
       "\n",
       "                                     id  grupo  \\\n",
       "0      0610b486422d4921ae7d2bf64640c50b      2   \n",
       "1      78afa995795e4d85b5d9ceeca43f5fef      1   \n",
       "2      e2127556f4f64592b11af22de27a7932      1   \n",
       "3      389bc3fa690240e798340f5a15918d5c      3   \n",
       "4      2eeac8d8feae4a8cad5a6af0499a211d      3   \n",
       "...                                 ...    ...   \n",
       "14820  6d5f3a774f3d4714ab0c092238f3a1d7      0   \n",
       "14821  2cb4f97358b841b9a9773a7aa05a9d77      4   \n",
       "14822  01d26f638c274aa0b965d24cefe3183f      4   \n",
       "14823  9dc1421481194dcd9400aec7c9ae6366      1   \n",
       "14824  e4052622e5ba45a8b96b59aba68cf068      4   \n",
       "\n",
       "                                     id  renda_anual  idade  \n",
       "0      0610b486422d4921ae7d2bf64640c50b       112000     55  \n",
       "1      78afa995795e4d85b5d9ceeca43f5fef        65200     75  \n",
       "2      e2127556f4f64592b11af22de27a7932        70000     68  \n",
       "3      389bc3fa690240e798340f5a15918d5c        53000     65  \n",
       "4      2eeac8d8feae4a8cad5a6af0499a211d        51000     58  \n",
       "...                                 ...          ...    ...  \n",
       "14820  6d5f3a774f3d4714ab0c092238f3a1d7        54000     45  \n",
       "14821  2cb4f97358b841b9a9773a7aa05a9d77        72000     61  \n",
       "14822  01d26f638c274aa0b965d24cefe3183f        73000     49  \n",
       "14823  9dc1421481194dcd9400aec7c9ae6366        50000     83  \n",
       "14824  e4052622e5ba45a8b96b59aba68cf068        82000     62  \n",
       "\n",
       "[14825 rows x 7 columns]"
      ]
     },
     "execution_count": 558,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes_processado = pd.concat([clientes_processado, \\\n",
    "                                 clientes['id'].reset_index(drop=True), \\\n",
    "                                 clientes['renda_anual'].reset_index(drop=True), \\\n",
    "                                 clientes['idade'].reset_index(drop=True)],\n",
    "                                 axis=1)\n",
    "clientes_processado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>idade_processada</th>\n",
       "      <th>renda_anual_processada</th>\n",
       "      <th>grupo</th>\n",
       "      <th>renda_anual</th>\n",
       "      <th>idade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3433.000000</td>\n",
       "      <td>3433.000000</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>3433.000000</td>\n",
       "      <td>3433.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.350889</td>\n",
       "      <td>-0.728625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49583.862511</td>\n",
       "      <td>30.910865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.441678</td>\n",
       "      <td>0.550502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11799.122211</td>\n",
       "      <td>7.677745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.093613</td>\n",
       "      <td>-1.642333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.748451</td>\n",
       "      <td>-1.222428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39000.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.345761</td>\n",
       "      <td>-0.709210</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-1.000599</td>\n",
       "      <td>-0.289304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59000.000000</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-0.482855</td>\n",
       "      <td>0.457195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75000.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       idade_processada  renda_anual_processada   grupo   renda_anual  \\\n",
       "count       3433.000000             3433.000000  3433.0   3433.000000   \n",
       "mean          -1.350889               -0.728625     0.0  49583.862511   \n",
       "std            0.441678                0.550502     0.0  11799.122211   \n",
       "min           -2.093613               -1.642333     0.0  30000.000000   \n",
       "25%           -1.748451               -1.222428     0.0  39000.000000   \n",
       "50%           -1.345761               -0.709210     0.0  50000.000000   \n",
       "75%           -1.000599               -0.289304     0.0  59000.000000   \n",
       "max           -0.482855                0.457195     0.0  75000.000000   \n",
       "\n",
       "             idade  \n",
       "count  3433.000000  \n",
       "mean     30.910865  \n",
       "std       7.677745  \n",
       "min      18.000000  \n",
       "25%      24.000000  \n",
       "50%      31.000000  \n",
       "75%      37.000000  \n",
       "max      46.000000  "
      ]
     },
     "execution_count": 559,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grupo azul: Jovem e de renda mais baixa\n",
    "clientes_processado[clientes_processado['grupo'] == 0].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os mais jovens tendem a serem mais atraídos por campanhas que utilizem digital influencers e que sejam autênticas. Os jovens são mais ativos nas plataformas Instagram, Tik Tok e YouTube. Conteúdos devem ser visualmente atraentes.\n",
    "Por possuírem renda menor é essencial informá-los sobre descontos ou promoções. \n",
    "\n",
    "Fonte: https://dwdigital.com.br/canais-de-marketing-para-alcancar-a-geracao-z/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>idade_processada</th>\n",
       "      <th>renda_anual_processada</th>\n",
       "      <th>grupo</th>\n",
       "      <th>renda_anual</th>\n",
       "      <th>idade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2401.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.271565</td>\n",
       "      <td>0.106999</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67494.127447</td>\n",
       "      <td>76.497293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.498233</td>\n",
       "      <td>0.539840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11570.592163</td>\n",
       "      <td>8.660837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.495105</td>\n",
       "      <td>-1.642333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.840268</td>\n",
       "      <td>-0.242648</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.185430</td>\n",
       "      <td>0.130601</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68000.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.645647</td>\n",
       "      <td>0.503851</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76000.000000</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.681134</td>\n",
       "      <td>1.576943</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99000.000000</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       idade_processada  renda_anual_processada   grupo   renda_anual  \\\n",
       "count       2401.000000             2401.000000  2401.0   2401.000000   \n",
       "mean           1.271565                0.106999     1.0  67494.127447   \n",
       "std            0.498233                0.539840     0.0  11570.592163   \n",
       "min            0.495105               -1.642333     1.0  30000.000000   \n",
       "25%            0.840268               -0.242648     1.0  60000.000000   \n",
       "50%            1.185430                0.130601     1.0  68000.000000   \n",
       "75%            1.645647                0.503851     1.0  76000.000000   \n",
       "max            2.681134                1.576943     1.0  99000.000000   \n",
       "\n",
       "             idade  \n",
       "count  2401.000000  \n",
       "mean     76.497293  \n",
       "std       8.660837  \n",
       "min      63.000000  \n",
       "25%      69.000000  \n",
       "50%      75.000000  \n",
       "75%      83.000000  \n",
       "max     101.000000  "
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grupo verde: Mais velhos. Renda muito diversificada porém tendendo ao cento\n",
    "clientes_processado[clientes_processado['grupo'] == 1].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tendo em vista que os mais velhos são mais ativos no facebook e WhatsApp, pode-se investir em campanhas que contatem-nos por essa rede social. Pessoas com mais idade também tendem a ser mais receptivas com campanhas presenciais.\n",
    "\n",
    "Tendem a serem fiéis com as marcas que gostam. É importante gerar confiança neste tipo de cliente e focar as campanhas nos benefícios da marca. É possível criar promoções que premiem a fidelização como presente por pontos.\n",
    "\n",
    "https://www.next4.com.br/marketing-digital-para-idosos-crescimento-exponencial-nos-ultimos-anos-e-muita-oportunidade-para-sua-empresa/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "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>idade_processada</th>\n",
       "      <th>renda_anual_processada</th>\n",
       "      <th>grupo</th>\n",
       "      <th>renda_anual</th>\n",
       "      <th>idade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2380.000000</td>\n",
       "      <td>2380.000000</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>2380.000000</td>\n",
       "      <td>2380.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.575353</td>\n",
       "      <td>1.621560</td>\n",
       "      <td>2.0</td>\n",
       "      <td>99956.302521</td>\n",
       "      <td>64.394958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.575218</td>\n",
       "      <td>0.461246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9886.056507</td>\n",
       "      <td>9.999091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.655436</td>\n",
       "      <td>0.783788</td>\n",
       "      <td>2.0</td>\n",
       "      <td>82000.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.149943</td>\n",
       "      <td>1.250350</td>\n",
       "      <td>2.0</td>\n",
       "      <td>92000.000000</td>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.495105</td>\n",
       "      <td>1.530287</td>\n",
       "      <td>2.0</td>\n",
       "      <td>98000.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.955322</td>\n",
       "      <td>1.996849</td>\n",
       "      <td>2.0</td>\n",
       "      <td>108000.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.623607</td>\n",
       "      <td>2.556723</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120000.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       idade_processada  renda_anual_processada   grupo    renda_anual  \\\n",
       "count       2380.000000             2380.000000  2380.0    2380.000000   \n",
       "mean           0.575353                1.621560     2.0   99956.302521   \n",
       "std            0.575218                0.461246     0.0    9886.056507   \n",
       "min           -0.655436                0.783788     2.0   82000.000000   \n",
       "25%            0.149943                1.250350     2.0   92000.000000   \n",
       "50%            0.495105                1.530287     2.0   98000.000000   \n",
       "75%            0.955322                1.996849     2.0  108000.000000   \n",
       "max            2.623607                2.556723     2.0  120000.000000   \n",
       "\n",
       "             idade  \n",
       "count  2380.000000  \n",
       "mean     64.394958  \n",
       "std       9.999091  \n",
       "min      43.000000  \n",
       "25%      57.000000  \n",
       "50%      63.000000  \n",
       "75%      71.000000  \n",
       "max     100.000000  "
      ]
     },
     "execution_count": 562,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grupo vermelho: Grupo mais abastado e com a segunda maior idade média.\n",
    "clientes_processado[clientes_processado['grupo'] == 2].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É possível chamar a atenção desses grupos com os informativos, sem dar destaque a promoções para atraí-los, tendo em vista que possuem mais renda. As estratégias de fidelização e construção de boa reputação da marca utilizadas no grupo verde, dos mais velhos, deve ser mantida. Os canais de comunicação também (WhatsApp e Facebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>idade_processada</th>\n",
       "      <th>renda_anual_processada</th>\n",
       "      <th>grupo</th>\n",
       "      <th>renda_anual</th>\n",
       "      <th>idade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3079.000000</td>\n",
       "      <td>3079.000000</td>\n",
       "      <td>3079.0</td>\n",
       "      <td>3079.000000</td>\n",
       "      <td>3079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.300814</td>\n",
       "      <td>-0.958083</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44665.800585</td>\n",
       "      <td>59.622605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.506668</td>\n",
       "      <td>0.405842</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8698.567559</td>\n",
       "      <td>8.807476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.597909</td>\n",
       "      <td>-1.642333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.080165</td>\n",
       "      <td>-1.315740</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37000.000000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.264997</td>\n",
       "      <td>-0.942491</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45000.000000</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.667687</td>\n",
       "      <td>-0.615897</td>\n",
       "      <td>3.0</td>\n",
       "      <td>52000.000000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.048336</td>\n",
       "      <td>-0.149336</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62000.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       idade_processada  renda_anual_processada   grupo   renda_anual  \\\n",
       "count       3079.000000             3079.000000  3079.0   3079.000000   \n",
       "mean           0.300814               -0.958083     3.0  44665.800585   \n",
       "std            0.506668                0.405842     0.0   8698.567559   \n",
       "min           -0.597909               -1.642333     3.0  30000.000000   \n",
       "25%           -0.080165               -1.315740     3.0  37000.000000   \n",
       "50%            0.264997               -0.942491     3.0  45000.000000   \n",
       "75%            0.667687               -0.615897     3.0  52000.000000   \n",
       "max            2.048336               -0.149336     3.0  62000.000000   \n",
       "\n",
       "             idade  \n",
       "count  3079.000000  \n",
       "mean     59.622605  \n",
       "std       8.807476  \n",
       "min      44.000000  \n",
       "25%      53.000000  \n",
       "50%      59.000000  \n",
       "75%      66.000000  \n",
       "max      90.000000  "
      ]
     },
     "execution_count": 563,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grupo roxo: idade mediana para mais e renda baixa. \n",
    "clientes_processado[clientes_processado['grupo'] == 3].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por se tratar de um grupo com renda baixa é importante informá-los de descontos e promoções. Possuem uma ampla gama de idades que vai da geração X aos mais idosos. Um misto dos canais de comunicação, desde e-mail às redes variadas é interessante neste caso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>idade_processada</th>\n",
       "      <th>renda_anual_processada</th>\n",
       "      <th>grupo</th>\n",
       "      <th>renda_anual</th>\n",
       "      <th>idade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3532.000000</td>\n",
       "      <td>3532.000000</td>\n",
       "      <td>3532.0</td>\n",
       "      <td>3532.000000</td>\n",
       "      <td>3532.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.201295</td>\n",
       "      <td>0.377998</td>\n",
       "      <td>4.0</td>\n",
       "      <td>73302.548131</td>\n",
       "      <td>50.894394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.420992</td>\n",
       "      <td>0.423501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9077.064207</td>\n",
       "      <td>7.318146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.288234</td>\n",
       "      <td>-0.429273</td>\n",
       "      <td>4.0</td>\n",
       "      <td>56000.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.482855</td>\n",
       "      <td>0.037289</td>\n",
       "      <td>4.0</td>\n",
       "      <td>66000.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.195220</td>\n",
       "      <td>0.363882</td>\n",
       "      <td>4.0</td>\n",
       "      <td>73000.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.149943</td>\n",
       "      <td>0.690476</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80000.000000</td>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.610160</td>\n",
       "      <td>1.576943</td>\n",
       "      <td>4.0</td>\n",
       "      <td>99000.000000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       idade_processada  renda_anual_processada   grupo   renda_anual  \\\n",
       "count       3532.000000             3532.000000  3532.0   3532.000000   \n",
       "mean          -0.201295                0.377998     4.0  73302.548131   \n",
       "std            0.420992                0.423501     0.0   9077.064207   \n",
       "min           -1.288234               -0.429273     4.0  56000.000000   \n",
       "25%           -0.482855                0.037289     4.0  66000.000000   \n",
       "50%           -0.195220                0.363882     4.0  73000.000000   \n",
       "75%            0.149943                0.690476     4.0  80000.000000   \n",
       "max            0.610160                1.576943     4.0  99000.000000   \n",
       "\n",
       "             idade  \n",
       "count  3532.000000  \n",
       "mean     50.894394  \n",
       "std       7.318146  \n",
       "min      32.000000  \n",
       "25%      46.000000  \n",
       "50%      51.000000  \n",
       "75%      57.000000  \n",
       "max      65.000000  "
      ]
     },
     "execution_count": 564,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grupo laranja: renda média e idade média\n",
    "clientes_processado[clientes_processado['grupo'] == 4].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A geração X é um grupo de faixa etária que consome informação no meio digital mas também de forma significativa nos veículos tradicionais como TV, rádio e jornal. É necessário mesclar neste grupo estratégias utilizadas para os grupos dos mais velhos também.\n",
    "\n",
    "https://consumidormoderno.com.br/2021/06/18/estrategias-marketing-geracao-x/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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