Analise_rfm.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rrV4_PD_drbS"
      },
      "source": [
        "# Segmentação de Clientes com base em análise RFM(Recência, Frequência e Valor Monetário)\n",
        "\n",
        "**Objetivo:** Realizar a análise de cliente pelo método RFM :\n",
        "\n",
        "- Recência: quão recente um cliente fez a compra;\n",
        "- Frequência: com que frequência um cliente faz a compra;\n",
        "- Valor Monetário: quanto dinheiro um cliente gasta em compras.\n",
        "\n",
        "De acordo com essas métricas, é possível segmentar os clientes em grupos para entender quais deles compram muitas coisas com frequência, que compram poucas coisas, mas frequentemente, e que não compram nada há muito tempo.\n",
        "\n",
        "**Link do dataset** Online Retail II - UCI Machine Learning Repository\n",
        "\n",
        ">**Referência:** Formação Cientista de Dados (https://www.datascienceacademy.com.br/)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "YyqHUGzJTB9_",
        "vscode": {
          "languageId": "r"
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      "outputs": [],
      "source": [
        "# Configurando dados de saída\n",
        "options(scipen = 999,\n",
        "        digits = 5,\n",
        "        repos = 'http://cran.rstudio.com/',\n",
        "        repr.plot.width = 20,\n",
        "        repr.plot.height = 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a44BrvCFUOVi",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [],
      "source": [
        "if(!require('pacman'))\n",
        "install.packages('pacman')\n",
        "library('pacman')\n",
        "pacman::p_load('tidyverse', 'ggplot2', 'caret', 'plotly', 'readxl', 'rfm', 'stats','factoextra','plyr')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-NIJIfa5UJRC"
      },
      "source": [
        "# 1. Importação da base de dados e bibliotecas\n",
        "\n",
        "Esta seção apresenta a importando das bibliotecas necessárias para manipulações e tratamento dos dados e também a importação do arquivo excel (.xls) da base de dados."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "VtYzNjZJTqsh",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [],
      "source": [
        "# Carregar os dados da planilha\n",
        "plan1 <- read_excel('/content/online_retail_II.xlsx', sheet = 'Year 2009-2010')\n",
        "plan2 <- read_excel('/content/online_retail_II.xlsx', sheet = 'Year 2010-2011')\n",
        "df <- rbind(plan1, plan2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDCeh1WAUuxL"
      },
      "source": [
        "# 2. Definição das variáveis\n",
        "\n",
        "- InvoiceNo: Número da fatura. Nominal. Um número integral de 6 dígitos atribuído exclusivamente a cada transação. Se esse código começar com a letra 'c', ele indica um cancelamento;\n",
        "\n",
        "- StockCode: Código do produto (item). Nominal. Um número integral de 5 dígitos atribuído exclusivamente a cada produto distinto;\n",
        "\n",
        "- Description: Nome do produto (item);\n",
        "\n",
        "- Quantity: As quantidades de cada produto (item) por transação. Numérico;\n",
        "\n",
        "- InvoiceDate: Data e hora invictas. Numérico. O dia e a hora em que uma transação foi gerada;\n",
        "\n",
        "- UnitPrice: Preço unitário. Numérico. Preço do produto por unidade em libras esterlinas (Â£);\n",
        "\n",
        "- CustomerID: Número do cliente. Nominal. Um número integral de 5 dígitos atribuído exclusivamente a cada cliente;\n",
        "\n",
        "- Country: Nome do país. Nominal. O nome do país onde o cliente reside.\n",
        "\n",
        "****\n",
        "\n",
        "# 3. Análise Exploratória das variáveis\n",
        "\n",
        "Nesta seção será explorado as variáveis para identificar os possíveis padrões e comportamentos, valores nulos, outliers e engenharia de atributos, se necessário."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
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      "outputs": [
        {
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 9</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>Invoice</th><th scope=col>StockCode</th><th scope=col>Description</th><th scope=col>Quantity</th><th scope=col>InvoiceDate</th><th scope=col>Price</th><th scope=col>Customer ID</th><th scope=col>Country</th><th scope=col>Total_Price</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>489434</td><td>85048 </td><td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td><td>12</td><td>2009-12-01 07:45:00</td><td>6.95</td><td>13085</td><td>United Kingdom</td><td> 83.4</td></tr>\n",
              "\t<tr><td>489434</td><td>79323P</td><td>PINK CHERRY LIGHTS                 </td><td>12</td><td>2009-12-01 07:45:00</td><td>6.75</td><td>13085</td><td>United Kingdom</td><td> 81.0</td></tr>\n",
              "\t<tr><td>489434</td><td>79323W</td><td>WHITE CHERRY LIGHTS                </td><td>12</td><td>2009-12-01 07:45:00</td><td>6.75</td><td>13085</td><td>United Kingdom</td><td> 81.0</td></tr>\n",
              "\t<tr><td>489434</td><td>22041 </td><td>RECORD FRAME 7\" SINGLE SIZE        </td><td>48</td><td>2009-12-01 07:45:00</td><td>2.10</td><td>13085</td><td>United Kingdom</td><td>100.8</td></tr>\n",
              "\t<tr><td>489434</td><td>21232 </td><td>STRAWBERRY CERAMIC TRINKET BOX     </td><td>24</td><td>2009-12-01 07:45:00</td><td>1.25</td><td>13085</td><td>United Kingdom</td><td> 30.0</td></tr>\n",
              "\t<tr><td>489434</td><td>22064 </td><td>PINK DOUGHNUT TRINKET POT          </td><td>24</td><td>2009-12-01 07:45:00</td><td>1.65</td><td>13085</td><td>United Kingdom</td><td> 39.6</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A tibble: 6 × 9\n",
              "\\begin{tabular}{lllllllll}\n",
              " Invoice & StockCode & Description & Quantity & InvoiceDate & Price & Customer ID & Country & Total\\_Price\\\\\n",
              " <chr> & <chr> & <chr> & <dbl> & <dttm> & <dbl> & <dbl> & <fct> & <dbl>\\\\\n",
              "\\hline\n",
              "\t 489434 & 85048  & 15CM CHRISTMAS GLASS BALL 20 LIGHTS & 12 & 2009-12-01 07:45:00 & 6.95 & 13085 & United Kingdom &  83.4\\\\\n",
              "\t 489434 & 79323P & PINK CHERRY LIGHTS                  & 12 & 2009-12-01 07:45:00 & 6.75 & 13085 & United Kingdom &  81.0\\\\\n",
              "\t 489434 & 79323W & WHITE CHERRY LIGHTS                 & 12 & 2009-12-01 07:45:00 & 6.75 & 13085 & United Kingdom &  81.0\\\\\n",
              "\t 489434 & 22041  & RECORD FRAME 7\" SINGLE SIZE         & 48 & 2009-12-01 07:45:00 & 2.10 & 13085 & United Kingdom & 100.8\\\\\n",
              "\t 489434 & 21232  & STRAWBERRY CERAMIC TRINKET BOX      & 24 & 2009-12-01 07:45:00 & 1.25 & 13085 & United Kingdom &  30.0\\\\\n",
              "\t 489434 & 22064  & PINK DOUGHNUT TRINKET POT           & 24 & 2009-12-01 07:45:00 & 1.65 & 13085 & United Kingdom &  39.6\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A tibble: 6 × 9\n",
              "\n",
              "| Invoice &lt;chr&gt; | StockCode &lt;chr&gt; | Description &lt;chr&gt; | Quantity &lt;dbl&gt; | InvoiceDate &lt;dttm&gt; | Price &lt;dbl&gt; | Customer ID &lt;dbl&gt; | Country &lt;fct&gt; | Total_Price &lt;dbl&gt; |\n",
              "|---|---|---|---|---|---|---|---|---|\n",
              "| 489434 | 85048  | 15CM CHRISTMAS GLASS BALL 20 LIGHTS | 12 | 2009-12-01 07:45:00 | 6.95 | 13085 | United Kingdom |  83.4 |\n",
              "| 489434 | 79323P | PINK CHERRY LIGHTS                  | 12 | 2009-12-01 07:45:00 | 6.75 | 13085 | United Kingdom |  81.0 |\n",
              "| 489434 | 79323W | WHITE CHERRY LIGHTS                 | 12 | 2009-12-01 07:45:00 | 6.75 | 13085 | United Kingdom |  81.0 |\n",
              "| 489434 | 22041  | RECORD FRAME 7\" SINGLE SIZE         | 48 | 2009-12-01 07:45:00 | 2.10 | 13085 | United Kingdom | 100.8 |\n",
              "| 489434 | 21232  | STRAWBERRY CERAMIC TRINKET BOX      | 24 | 2009-12-01 07:45:00 | 1.25 | 13085 | United Kingdom |  30.0 |\n",
              "| 489434 | 22064  | PINK DOUGHNUT TRINKET POT           | 24 | 2009-12-01 07:45:00 | 1.65 | 13085 | United Kingdom |  39.6 |\n",
              "\n"
            ],
            "text/plain": [
              "  Invoice StockCode Description                         Quantity\n",
              "1 489434  85048     15CM CHRISTMAS GLASS BALL 20 LIGHTS 12      \n",
              "2 489434  79323P    PINK CHERRY LIGHTS                  12      \n",
              "3 489434  79323W    WHITE CHERRY LIGHTS                 12      \n",
              "4 489434  22041     RECORD FRAME 7\" SINGLE SIZE         48      \n",
              "5 489434  21232     STRAWBERRY CERAMIC TRINKET BOX      24      \n",
              "6 489434  22064     PINK DOUGHNUT TRINKET POT           24      \n",
              "  InvoiceDate         Price Customer ID Country        Total_Price\n",
              "1 2009-12-01 07:45:00 6.95  13085       United Kingdom  83.4      \n",
              "2 2009-12-01 07:45:00 6.75  13085       United Kingdom  81.0      \n",
              "3 2009-12-01 07:45:00 6.75  13085       United Kingdom  81.0      \n",
              "4 2009-12-01 07:45:00 2.10  13085       United Kingdom 100.8      \n",
              "5 2009-12-01 07:45:00 1.25  13085       United Kingdom  30.0      \n",
              "6 2009-12-01 07:45:00 1.65  13085       United Kingdom  39.6      "
            ]
          },
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          "output_type": "display_data"
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        {
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 9</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>StockCode</th><th scope=col>Price</th><th scope=col>Total_Price</th><th scope=col>Country</th><th scope=col>Customer ID</th><th scope=col>Description</th><th scope=col>InvoiceDate</th><th scope=col>Invoice</th><th scope=col>Quantity</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>85048 </td><td>6.95</td><td> 83.4</td><td>United Kingdom</td><td>13085</td><td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td><td>2009-12-01 07:45:00</td><td>489434</td><td>12</td></tr>\n",
              "\t<tr><td>79323P</td><td>6.75</td><td> 81.0</td><td>United Kingdom</td><td>13085</td><td>PINK CHERRY LIGHTS                 </td><td>2009-12-01 07:45:00</td><td>489434</td><td>12</td></tr>\n",
              "\t<tr><td>79323W</td><td>6.75</td><td> 81.0</td><td>United Kingdom</td><td>13085</td><td>WHITE CHERRY LIGHTS                </td><td>2009-12-01 07:45:00</td><td>489434</td><td>12</td></tr>\n",
              "\t<tr><td>22041 </td><td>2.10</td><td>100.8</td><td>United Kingdom</td><td>13085</td><td>RECORD FRAME 7\" SINGLE SIZE        </td><td>2009-12-01 07:45:00</td><td>489434</td><td>48</td></tr>\n",
              "\t<tr><td>21232 </td><td>1.25</td><td> 30.0</td><td>United Kingdom</td><td>13085</td><td>STRAWBERRY CERAMIC TRINKET BOX     </td><td>2009-12-01 07:45:00</td><td>489434</td><td>24</td></tr>\n",
              "\t<tr><td>22064 </td><td>1.65</td><td> 39.6</td><td>United Kingdom</td><td>13085</td><td>PINK DOUGHNUT TRINKET POT          </td><td>2009-12-01 07:45:00</td><td>489434</td><td>24</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A tibble: 6 × 9\n",
              "\\begin{tabular}{lllllllll}\n",
              " StockCode & Price & Total\\_Price & Country & Customer ID & Description & InvoiceDate & Invoice & Quantity\\\\\n",
              " <chr> & <dbl> & <dbl> & <fct> & <dbl> & <chr> & <dttm> & <chr> & <dbl>\\\\\n",
              "\\hline\n",
              "\t 85048  & 6.95 &  83.4 & United Kingdom & 13085 & 15CM CHRISTMAS GLASS BALL 20 LIGHTS & 2009-12-01 07:45:00 & 489434 & 12\\\\\n",
              "\t 79323P & 6.75 &  81.0 & United Kingdom & 13085 & PINK CHERRY LIGHTS                  & 2009-12-01 07:45:00 & 489434 & 12\\\\\n",
              "\t 79323W & 6.75 &  81.0 & United Kingdom & 13085 & WHITE CHERRY LIGHTS                 & 2009-12-01 07:45:00 & 489434 & 12\\\\\n",
              "\t 22041  & 2.10 & 100.8 & United Kingdom & 13085 & RECORD FRAME 7\" SINGLE SIZE         & 2009-12-01 07:45:00 & 489434 & 48\\\\\n",
              "\t 21232  & 1.25 &  30.0 & United Kingdom & 13085 & STRAWBERRY CERAMIC TRINKET BOX      & 2009-12-01 07:45:00 & 489434 & 24\\\\\n",
              "\t 22064  & 1.65 &  39.6 & United Kingdom & 13085 & PINK DOUGHNUT TRINKET POT           & 2009-12-01 07:45:00 & 489434 & 24\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A tibble: 6 × 9\n",
              "\n",
              "| StockCode &lt;chr&gt; | Price &lt;dbl&gt; | Total_Price &lt;dbl&gt; | Country &lt;fct&gt; | Customer ID &lt;dbl&gt; | Description &lt;chr&gt; | InvoiceDate &lt;dttm&gt; | Invoice &lt;chr&gt; | Quantity &lt;dbl&gt; |\n",
              "|---|---|---|---|---|---|---|---|---|\n",
              "| 85048  | 6.95 |  83.4 | United Kingdom | 13085 | 15CM CHRISTMAS GLASS BALL 20 LIGHTS | 2009-12-01 07:45:00 | 489434 | 12 |\n",
              "| 79323P | 6.75 |  81.0 | United Kingdom | 13085 | PINK CHERRY LIGHTS                  | 2009-12-01 07:45:00 | 489434 | 12 |\n",
              "| 79323W | 6.75 |  81.0 | United Kingdom | 13085 | WHITE CHERRY LIGHTS                 | 2009-12-01 07:45:00 | 489434 | 12 |\n",
              "| 22041  | 2.10 | 100.8 | United Kingdom | 13085 | RECORD FRAME 7\" SINGLE SIZE         | 2009-12-01 07:45:00 | 489434 | 48 |\n",
              "| 21232  | 1.25 |  30.0 | United Kingdom | 13085 | STRAWBERRY CERAMIC TRINKET BOX      | 2009-12-01 07:45:00 | 489434 | 24 |\n",
              "| 22064  | 1.65 |  39.6 | United Kingdom | 13085 | PINK DOUGHNUT TRINKET POT           | 2009-12-01 07:45:00 | 489434 | 24 |\n",
              "\n"
            ],
            "text/plain": [
              "  StockCode Price Total_Price Country        Customer ID\n",
              "1 85048     6.95   83.4       United Kingdom 13085      \n",
              "2 79323P    6.75   81.0       United Kingdom 13085      \n",
              "3 79323W    6.75   81.0       United Kingdom 13085      \n",
              "4 22041     2.10  100.8       United Kingdom 13085      \n",
              "5 21232     1.25   30.0       United Kingdom 13085      \n",
              "6 22064     1.65   39.6       United Kingdom 13085      \n",
              "  Description                         InvoiceDate         Invoice Quantity\n",
              "1 15CM CHRISTMAS GLASS BALL 20 LIGHTS 2009-12-01 07:45:00 489434  12      \n",
              "2 PINK CHERRY LIGHTS                  2009-12-01 07:45:00 489434  12      \n",
              "3 WHITE CHERRY LIGHTS                 2009-12-01 07:45:00 489434  12      \n",
              "4 RECORD FRAME 7\" SINGLE SIZE         2009-12-01 07:45:00 489434  48      \n",
              "5 STRAWBERRY CERAMIC TRINKET BOX      2009-12-01 07:45:00 489434  24      \n",
              "6 PINK DOUGHNUT TRINKET POT           2009-12-01 07:45:00 489434  24      "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Carregar as 10 primeiras entradas do dataset\n",
        "head(df)\n",
        "head(sample(df))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "ex27Ib0LTqxy",
        "outputId": "46ac09de-a18c-4299-82d6-82914ac5e51d",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>1067371</li><li>8</li></ol>\n"
            ],
            "text/latex": [
              "\\begin{enumerate*}\n",
              "\\item 1067371\n",
              "\\item 8\n",
              "\\end{enumerate*}\n"
            ],
            "text/markdown": [
              "1. 1067371\n",
              "2. 8\n",
              "\n",
              "\n"
            ],
            "text/plain": [
              "[1] 1067371       8"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Analisar as dimensões do dataset (linhas x colunas)\n",
        "dim(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
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        "id": "-Bn8wdk7Tq2A",
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      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>'Invoice'</li><li>'StockCode'</li><li>'Description'</li><li>'Quantity'</li><li>'InvoiceDate'</li><li>'Price'</li><li>'Customer ID'</li><li>'Country'</li></ol>\n"
            ],
            "text/latex": [
              "\\begin{enumerate*}\n",
              "\\item 'Invoice'\n",
              "\\item 'StockCode'\n",
              "\\item 'Description'\n",
              "\\item 'Quantity'\n",
              "\\item 'InvoiceDate'\n",
              "\\item 'Price'\n",
              "\\item 'Customer ID'\n",
              "\\item 'Country'\n",
              "\\end{enumerate*}\n"
            ],
            "text/markdown": [
              "1. 'Invoice'\n",
              "2. 'StockCode'\n",
              "3. 'Description'\n",
              "4. 'Quantity'\n",
              "5. 'InvoiceDate'\n",
              "6. 'Price'\n",
              "7. 'Customer ID'\n",
              "8. 'Country'\n",
              "\n",
              "\n"
            ],
            "text/plain": [
              "[1] \"Invoice\"     \"StockCode\"   \"Description\" \"Quantity\"    \"InvoiceDate\"\n",
              "[6] \"Price\"       \"Customer ID\" \"Country\"    "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Verificar o nome das variáveis(colunas)\n",
        "names(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
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        "outputId": "044214c0-5d43-48ed-bfe0-9aad5be6343c",
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tibble [1,067,371 × 8] (S3: tbl_df/tbl/data.frame)\n",
            " $ Invoice    : chr [1:1067371] \"489434\" \"489434\" \"489434\" \"489434\" ...\n",
            " $ StockCode  : chr [1:1067371] \"85048\" \"79323P\" \"79323W\" \"22041\" ...\n",
            " $ Description: chr [1:1067371] \"15CM CHRISTMAS GLASS BALL 20 LIGHTS\" \"PINK CHERRY LIGHTS\" \"WHITE CHERRY LIGHTS\" \"RECORD FRAME 7\\\" SINGLE SIZE\" ...\n",
            " $ Quantity   : num [1:1067371] 12 12 12 48 24 24 24 10 12 12 ...\n",
            " $ InvoiceDate: POSIXct[1:1067371], format: \"2009-12-01 07:45:00\" \"2009-12-01 07:45:00\" ...\n",
            " $ Price      : num [1:1067371] 6.95 6.75 6.75 2.1 1.25 1.65 1.25 5.95 2.55 3.75 ...\n",
            " $ Customer ID: num [1:1067371] 13085 13085 13085 13085 13085 ...\n",
            " $ Country    : chr [1:1067371] \"United Kingdom\" \"United Kingdom\" \"United Kingdom\" \"United Kingdom\" ...\n"
          ]
        }
      ],
      "source": [
        "# Verificar o tipo das variáveis\n",
        "str(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "pOkl-XUHTq6A",
        "outputId": "e9a25c78-7150-4919-82e5-6321a8f3ddd2",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\n",
              "           Australia              Austria              Bahrain \n",
              "                1913                  938                  126 \n",
              "             Belgium              Bermuda               Brazil \n",
              "                3123                   34                   94 \n",
              "              Canada      Channel Islands               Cyprus \n",
              "                 228                 1664                 1176 \n",
              "      Czech Republic              Denmark                 EIRE \n",
              "                  30                  817                17866 \n",
              "  European Community              Finland               France \n",
              "                  61                 1049                14330 \n",
              "             Germany               Greece            Hong Kong \n",
              "               17624                  663                  364 \n",
              "             Iceland               Israel                Italy \n",
              "                 253                  371                 1534 \n",
              "               Japan                Korea              Lebanon \n",
              "                 582                   63                   58 \n",
              "           Lithuania                Malta          Netherlands \n",
              "                 189                  299                 5140 \n",
              "             Nigeria               Norway               Poland \n",
              "                  32                 1455                  535 \n",
              "            Portugal                  RSA         Saudi Arabia \n",
              "                2620                  169                   10 \n",
              "           Singapore                Spain               Sweden \n",
              "                 346                 3811                 1364 \n",
              "         Switzerland             Thailand United Arab Emirates \n",
              "                3189                   76                  500 \n",
              "      United Kingdom          Unspecified                  USA \n",
              "              981330                  756                  535 \n",
              "         West Indies \n",
              "                  54 "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Transformando a variável \"country\" em factor, devido ser categórica nominal\n",
        "table(df$Country)\n",
        "df$Country <- as.factor(df$Country)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Kf1pU9FVDd_"
      },
      "source": [
        "Como trabalharemos com operações matemáticas, será necessário que as variáveis não apresentem valores nulos, bem como a dimensão da base possui 1067371 linhas, então removeremos os valores ausentes das variáveis \"Description\" e \"Customer ID\", pois não podemos inputar nenhuma informação para suprir essas ausências, visto que irá influenciar nas estatísticas de compra do cliente e portanto na análise RFM."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "E1_y-dbsTq8B",
        "outputId": "f95cb72e-b4e1-480e-ebd3-830ea99a5623",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>83.4</li><li>81</li><li>81</li><li>100.8</li><li>30</li><li>39.6</li></ol>\n"
            ],
            "text/latex": [
              "\\begin{enumerate*}\n",
              "\\item 83.4\n",
              "\\item 81\n",
              "\\item 81\n",
              "\\item 100.8\n",
              "\\item 30\n",
              "\\item 39.6\n",
              "\\end{enumerate*}\n"
            ],
            "text/markdown": [
              "1. 83.4\n",
              "2. 81\n",
              "3. 81\n",
              "4. 100.8\n",
              "5. 30\n",
              "6. 39.6\n",
              "\n",
              "\n"
            ],
            "text/plain": [
              "[1]  83.4  81.0  81.0 100.8  30.0  39.6"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Criando uma coluna de Preço Total\n",
        "df$Total_Price <- df$Quantity * df$Price\n",
        "head(df$Total_Price)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "iM-kXlNnVVxR",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [],
      "source": [
        "# Criando uma cópia do dataset original\n",
        "df_copy <- df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "CAzkChumVVz1",
        "outputId": "d7ff3d19-c7ff-45c0-9a1d-aba767a0f77d",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>805620</li><li>9</li></ol>\n"
            ],
            "text/latex": [
              "\\begin{enumerate*}\n",
              "\\item 805620\n",
              "\\item 9\n",
              "\\end{enumerate*}\n"
            ],
            "text/markdown": [
              "1. 805620\n",
              "2. 9\n",
              "\n",
              "\n"
            ],
            "text/plain": [
              "[1] 805620      9"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1] \"Foram removidas: 261751(24.52%) linhas após a limpeza\"\n"
          ]
        }
      ],
      "source": [
        "# Removendo os valores ausentes do dataset\n",
        "df_copy <- na.omit(df)\n",
        "\n",
        "# Seleciona os valores que NÃO são do tipo C na coluna \"Invoice\"\n",
        "df_copy <- df_copy[!grepl(\"C\",df_copy$Invoice),]\n",
        "dim(df_copy)\n",
        "\n",
        "# Resultado final do dataset após a limpeza\n",
        "print(paste0(\"Foram removidas: \",\n",
        "             dim(df)[1] - dim(df_copy)[1],\n",
        "             \"(\",\n",
        "             round(((dim(df)[1] - dim(df_copy)[1])/dim(df)[1]) * 100, digits = 2),\n",
        "             \"%) linhas após a limpeza\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "8qcLVyf4VV2g",
        "outputId": "ff95edc0-854c-42f6-8b2c-41c8ba1b43be",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 600,
              "width": 1200
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Verificando a disposição da variável \"Total_Price\"\n",
        "ggplot(\n",
        "  df_copy,\n",
        "  mapping = aes(x = Total_Price)) +\n",
        "  geom_density(fill = \"#ff79ae\", color = \"#282a36\", alpha = 3.5) +\n",
        "  labs(title = \"Distribuição da variável Total_Price\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "nSDIHGbeTq9x",
        "outputId": "a5e574ee-e603-4d4a-b0e4-4d48ef8eb6e7",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "805620"
            ],
            "text/latex": [
              "805620"
            ],
            "text/markdown": [
              "805620"
            ],
            "text/plain": [
              "[1] 805620"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Quantidade de transações\n",
        "length(df_copy$`Customer ID`)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "RQnKaRZdTq_r",
        "outputId": "381ea771-46d5-4995-af81-3316da41546c",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "5881"
            ],
            "text/latex": [
              "5881"
            ],
            "text/markdown": [
              "5881"
            ],
            "text/plain": [
              "[1] 5881"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Quantidade de clientes\n",
        "length(unique(df_copy$`Customer ID`))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4Rqz7LlTrBc",
        "outputId": "04d23580-d772-4356-e3a4-ced55e70b2e8",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Customer ID     Total\n",
            "            <num>     <num>\n",
            "   1:       18102 608821.65\n",
            "   2:       14646 528602.52\n",
            "   3:       14156 313946.37\n",
            "   4:       14911 295972.63\n",
            "   5:       17450 246973.09\n",
            "  ---                      \n",
            "5877:       16738      3.75\n",
            "5878:       14095      2.95\n",
            "5879:       14103      0.00\n",
            "5880:       14827      0.00\n",
            "5881:       13256      0.00\n"
          ]
        }
      ],
      "source": [
        "# Transformando em data table\n",
        "df_copy <- data.table::data.table(df_copy)\n",
        "\n",
        "# Valor Monetário por cliente\n",
        "valor_monetario <- df_copy[,.(Total = sum(Total_Price)), by = `Customer ID`][order(desc(Total))]\n",
        "print(valor_monetario)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s7atdQYaTrEw",
        "outputId": "0a22a598-5cdb-44ea-b04b-8abe0e2eefb8",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
            "      0     348     898    3017    2304  608822 \n"
          ]
        }
      ],
      "source": [
        "print(summary(valor_monetario$Total))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "oxJK4TFwTrIY",
        "outputId": "81cb12ee-083f-490c-b43d-8b14d0c69abf",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[1] \"2011-12-09 12:50:00 UTC\""
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.table: 6 × 9</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>Invoice</th><th scope=col>StockCode</th><th scope=col>Description</th><th scope=col>Quantity</th><th scope=col>InvoiceDate</th><th scope=col>Price</th><th scope=col>Customer ID</th><th scope=col>Country</th><th scope=col>Total_Price</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>489434</td><td>85048 </td><td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td><td>12</td><td>2009-12-01</td><td>6.95</td><td>13085</td><td>United Kingdom</td><td> 83.4</td></tr>\n",
              "\t<tr><td>489434</td><td>79323P</td><td>PINK CHERRY LIGHTS                 </td><td>12</td><td>2009-12-01</td><td>6.75</td><td>13085</td><td>United Kingdom</td><td> 81.0</td></tr>\n",
              "\t<tr><td>489434</td><td>79323W</td><td>WHITE CHERRY LIGHTS                </td><td>12</td><td>2009-12-01</td><td>6.75</td><td>13085</td><td>United Kingdom</td><td> 81.0</td></tr>\n",
              "\t<tr><td>489434</td><td>22041 </td><td>RECORD FRAME 7\" SINGLE SIZE        </td><td>48</td><td>2009-12-01</td><td>2.10</td><td>13085</td><td>United Kingdom</td><td>100.8</td></tr>\n",
              "\t<tr><td>489434</td><td>21232 </td><td>STRAWBERRY CERAMIC TRINKET BOX     </td><td>24</td><td>2009-12-01</td><td>1.25</td><td>13085</td><td>United Kingdom</td><td> 30.0</td></tr>\n",
              "\t<tr><td>489434</td><td>22064 </td><td>PINK DOUGHNUT TRINKET POT          </td><td>24</td><td>2009-12-01</td><td>1.65</td><td>13085</td><td>United Kingdom</td><td> 39.6</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A data.table: 6 × 9\n",
              "\\begin{tabular}{lllllllll}\n",
              " Invoice & StockCode & Description & Quantity & InvoiceDate & Price & Customer ID & Country & Total\\_Price\\\\\n",
              " <chr> & <chr> & <chr> & <dbl> & <date> & <dbl> & <dbl> & <fct> & <dbl>\\\\\n",
              "\\hline\n",
              "\t 489434 & 85048  & 15CM CHRISTMAS GLASS BALL 20 LIGHTS & 12 & 2009-12-01 & 6.95 & 13085 & United Kingdom &  83.4\\\\\n",
              "\t 489434 & 79323P & PINK CHERRY LIGHTS                  & 12 & 2009-12-01 & 6.75 & 13085 & United Kingdom &  81.0\\\\\n",
              "\t 489434 & 79323W & WHITE CHERRY LIGHTS                 & 12 & 2009-12-01 & 6.75 & 13085 & United Kingdom &  81.0\\\\\n",
              "\t 489434 & 22041  & RECORD FRAME 7\" SINGLE SIZE         & 48 & 2009-12-01 & 2.10 & 13085 & United Kingdom & 100.8\\\\\n",
              "\t 489434 & 21232  & STRAWBERRY CERAMIC TRINKET BOX      & 24 & 2009-12-01 & 1.25 & 13085 & United Kingdom &  30.0\\\\\n",
              "\t 489434 & 22064  & PINK DOUGHNUT TRINKET POT           & 24 & 2009-12-01 & 1.65 & 13085 & United Kingdom &  39.6\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A data.table: 6 × 9\n",
              "\n",
              "| Invoice &lt;chr&gt; | StockCode &lt;chr&gt; | Description &lt;chr&gt; | Quantity &lt;dbl&gt; | InvoiceDate &lt;date&gt; | Price &lt;dbl&gt; | Customer ID &lt;dbl&gt; | Country &lt;fct&gt; | Total_Price &lt;dbl&gt; |\n",
              "|---|---|---|---|---|---|---|---|---|\n",
              "| 489434 | 85048  | 15CM CHRISTMAS GLASS BALL 20 LIGHTS | 12 | 2009-12-01 | 6.95 | 13085 | United Kingdom |  83.4 |\n",
              "| 489434 | 79323P | PINK CHERRY LIGHTS                  | 12 | 2009-12-01 | 6.75 | 13085 | United Kingdom |  81.0 |\n",
              "| 489434 | 79323W | WHITE CHERRY LIGHTS                 | 12 | 2009-12-01 | 6.75 | 13085 | United Kingdom |  81.0 |\n",
              "| 489434 | 22041  | RECORD FRAME 7\" SINGLE SIZE         | 48 | 2009-12-01 | 2.10 | 13085 | United Kingdom | 100.8 |\n",
              "| 489434 | 21232  | STRAWBERRY CERAMIC TRINKET BOX      | 24 | 2009-12-01 | 1.25 | 13085 | United Kingdom |  30.0 |\n",
              "| 489434 | 22064  | PINK DOUGHNUT TRINKET POT           | 24 | 2009-12-01 | 1.65 | 13085 | United Kingdom |  39.6 |\n",
              "\n"
            ],
            "text/plain": [
              "  Invoice StockCode Description                         Quantity InvoiceDate\n",
              "1 489434  85048     15CM CHRISTMAS GLASS BALL 20 LIGHTS 12       2009-12-01 \n",
              "2 489434  79323P    PINK CHERRY LIGHTS                  12       2009-12-01 \n",
              "3 489434  79323W    WHITE CHERRY LIGHTS                 12       2009-12-01 \n",
              "4 489434  22041     RECORD FRAME 7\" SINGLE SIZE         48       2009-12-01 \n",
              "5 489434  21232     STRAWBERRY CERAMIC TRINKET BOX      24       2009-12-01 \n",
              "6 489434  22064     PINK DOUGHNUT TRINKET POT           24       2009-12-01 \n",
              "  Price Customer ID Country        Total_Price\n",
              "1 6.95  13085       United Kingdom  83.4      \n",
              "2 6.75  13085       United Kingdom  81.0      \n",
              "3 6.75  13085       United Kingdom  81.0      \n",
              "4 2.10  13085       United Kingdom 100.8      \n",
              "5 1.25  13085       United Kingdom  30.0      \n",
              "6 1.65  13085       United Kingdom  39.6      "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Cálculo da Recência\n",
        "max(df_copy$InvoiceDate)\n",
        "data_ref <- as.Date.character(\"25/12/2011\", \"%d/%m/%Y\")\n",
        "\n",
        "converter_data <- function(x){\n",
        "  options(digits.secs = 3)\n",
        "  return(as.Date(as.POSIXct(x$InvoiceDate,'GMT')))\n",
        "}\n",
        "\n",
        "# Alterando a coluna InvoiceDate para ocultar o horário\n",
        "df_copy$InvoiceDate <- converter_data(df_copy)\n",
        "head(df_copy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "3XNwKAIvVnI5",
        "outputId": "3eba1568-b867-4432-99a1-33fad75b4dff",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.table: 6 × 5</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>Customer ID</th><th scope=col>Recencia</th><th scope=col>Frequencia</th><th scope=col>Monetario</th><th scope=col>Primeira_compra</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;date&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>13085</td><td>173</td><td>  84</td><td>  2433.28</td><td>2009-12-01</td></tr>\n",
              "\t<tr><td>13078</td><td> 19</td><td> 801</td><td> 29532.45</td><td>2009-12-01</td></tr>\n",
              "\t<tr><td>15362</td><td>464</td><td>  40</td><td>   613.08</td><td>2009-12-01</td></tr>\n",
              "\t<tr><td>18102</td><td> 16</td><td>1058</td><td>608821.65</td><td>2009-12-01</td></tr>\n",
              "\t<tr><td>12682</td><td> 19</td><td>1039</td><td> 24033.91</td><td>2009-12-01</td></tr>\n",
              "\t<tr><td>18087</td><td>114</td><td>  88</td><td> 14761.52</td><td>2009-12-01</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A data.table: 6 × 5\n",
              "\\begin{tabular}{lllll}\n",
              " Customer ID & Recencia & Frequencia & Monetario & Primeira\\_compra\\\\\n",
              " <dbl> & <dbl> & <int> & <dbl> & <date>\\\\\n",
              "\\hline\n",
              "\t 13085 & 173 &   84 &   2433.28 & 2009-12-01\\\\\n",
              "\t 13078 &  19 &  801 &  29532.45 & 2009-12-01\\\\\n",
              "\t 15362 & 464 &   40 &    613.08 & 2009-12-01\\\\\n",
              "\t 18102 &  16 & 1058 & 608821.65 & 2009-12-01\\\\\n",
              "\t 12682 &  19 & 1039 &  24033.91 & 2009-12-01\\\\\n",
              "\t 18087 & 114 &   88 &  14761.52 & 2009-12-01\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A data.table: 6 × 5\n",
              "\n",
              "| Customer ID &lt;dbl&gt; | Recencia &lt;dbl&gt; | Frequencia &lt;int&gt; | Monetario &lt;dbl&gt; | Primeira_compra &lt;date&gt; |\n",
              "|---|---|---|---|---|\n",
              "| 13085 | 173 |   84 |   2433.28 | 2009-12-01 |\n",
              "| 13078 |  19 |  801 |  29532.45 | 2009-12-01 |\n",
              "| 15362 | 464 |   40 |    613.08 | 2009-12-01 |\n",
              "| 18102 |  16 | 1058 | 608821.65 | 2009-12-01 |\n",
              "| 12682 |  19 | 1039 |  24033.91 | 2009-12-01 |\n",
              "| 18087 | 114 |   88 |  14761.52 | 2009-12-01 |\n",
              "\n"
            ],
            "text/plain": [
              "  Customer ID Recencia Frequencia Monetario Primeira_compra\n",
              "1 13085       173        84         2433.28 2009-12-01     \n",
              "2 13078        19       801        29532.45 2009-12-01     \n",
              "3 15362       464        40          613.08 2009-12-01     \n",
              "4 18102        16      1058       608821.65 2009-12-01     \n",
              "5 12682        19      1039        24033.91 2009-12-01     \n",
              "6 18087       114        88        14761.52 2009-12-01     "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Calculando o RFM\n",
        "calculo_rfm <- df_copy[,\n",
        "        .(Recencia = as.numeric(data_ref - max(InvoiceDate)),\n",
        "          Frequencia = .N,\n",
        "          Monetario = sum(Total_Price),\n",
        "          Primeira_compra = min(InvoiceDate)),\n",
        "        by = `Customer ID`]\n",
        "\n",
        "head(calculo_rfm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6TeR6lj4VnGY",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [],
      "source": [
        "# Removendo os Outliers (Valores muito distantes da média)\n",
        "Q1 <- quantile(calculo_rfm$Monetario, .25)\n",
        "Q3 <- quantile(calculo_rfm$Monetario, .75)\n",
        "IQR <- IQR(calculo_rfm$Monetario)\n",
        "\n",
        "calculo_rfm <- subset(\n",
        "                calculo_rfm,\n",
        "                calculo_rfm$Monetario >= (Q1 - 1.5*IQR) &\n",
        "                calculo_rfm$Monetario <= (Q3 + 1.5*IQR)\n",
        "                                    )\n",
        "\n",
        "head(calculo_rfm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sutzSVdrVx-K"
      },
      "source": [
        "# 4. Machine Learning - Clusterização Kmeans\n",
        "\n",
        "Nesta seção será criado um modelo de algoritmo não supervisionado do tipo Cluster Kmeans, na qual agrupa os dados em clusters (grupos) com base em suas características, sem a necessidade de rótulos prévios. O algoritmo tenta encontrar estruturas nos dados agrupando-os em um número pré-definido de clusters, minimizando a variação dentro de cada cluster e maximizando a variação entre os clusters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "tywRLZZzVnDo",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [],
      "source": [
        "# Set seed\n",
        "set.seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "ZQd6NFyvVnA3",
        "outputId": "1c88cb15-03ab-4793-cae3-ac4ee3f5597c",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.table: 6 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>Recencia</th><th scope=col>Frequencia</th><th scope=col>Monetario</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>173</td><td>  84</td><td>  2433.28</td></tr>\n",
              "\t<tr><td> 19</td><td> 801</td><td> 29532.45</td></tr>\n",
              "\t<tr><td>464</td><td>  40</td><td>   613.08</td></tr>\n",
              "\t<tr><td> 16</td><td>1058</td><td>608821.65</td></tr>\n",
              "\t<tr><td> 19</td><td>1039</td><td> 24033.91</td></tr>\n",
              "\t<tr><td>114</td><td>  88</td><td> 14761.52</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A data.table: 6 × 3\n",
              "\\begin{tabular}{lll}\n",
              " Recencia & Frequencia & Monetario\\\\\n",
              " <dbl> & <int> & <dbl>\\\\\n",
              "\\hline\n",
              "\t 173 &   84 &   2433.28\\\\\n",
              "\t  19 &  801 &  29532.45\\\\\n",
              "\t 464 &   40 &    613.08\\\\\n",
              "\t  16 & 1058 & 608821.65\\\\\n",
              "\t  19 & 1039 &  24033.91\\\\\n",
              "\t 114 &   88 &  14761.52\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A data.table: 6 × 3\n",
              "\n",
              "| Recencia &lt;dbl&gt; | Frequencia &lt;int&gt; | Monetario &lt;dbl&gt; |\n",
              "|---|---|---|\n",
              "| 173 |   84 |   2433.28 |\n",
              "|  19 |  801 |  29532.45 |\n",
              "| 464 |   40 |    613.08 |\n",
              "|  16 | 1058 | 608821.65 |\n",
              "|  19 | 1039 |  24033.91 |\n",
              "| 114 |   88 |  14761.52 |\n",
              "\n"
            ],
            "text/plain": [
              "  Recencia Frequencia Monetario\n",
              "1 173        84         2433.28\n",
              "2  19       801        29532.45\n",
              "3 464        40          613.08\n",
              "4  16      1058       608821.65\n",
              "5  19      1039        24033.91\n",
              "6 114        88        14761.52"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Cria uma lista\n",
        "resultados <- list()\n",
        "\n",
        "# Filtrando somente as colunas necessárias\n",
        "dados_rfm <- calculo_rfm %>%\n",
        "  select(Recencia, Frequencia, Monetario)\n",
        "\n",
        "head(dados_rfm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "UuEQ0N6IVm-I",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [],
      "source": [
        "# Cria o modelo e divide em 5 grupos\n",
        "# com base na distância entre os pontos\n",
        "modelo_kmeans <- kmeans(dados_rfm,\n",
        "                        centers = 5,\n",
        "                        iter.max = 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "j66wsbcGV5B5",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [],
      "source": [
        "# Plot do modelo\n",
        "resultados$plot <- fviz_cluster(modelo_kmeans,\n",
        "                                data = dados_rfm,\n",
        "                                geom = c('point'),\n",
        "                                ellipse.type = 'euclid')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "g0IILMxpV5AL",
        "outputId": "6ebebdb8-117d-46bf-e5d7-6d569ce01cec",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[1m\u001b[22mToo few points to calculate an ellipse\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "$plot\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 600,
              "width": 1200
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Organiza os dados\n",
        "# Retornando os dados do ID para identificar os clientes\n",
        "dados_rfm$Customer_ID <- calculo_rfm$`Customer ID`\n",
        "dados_rfm$clusters <- modelo_kmeans$cluster\n",
        "\n",
        "resultados$data <- dados_rfm\n",
        "\n",
        "grafico <- resultados[1]\n",
        "grafico"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "wuIBfiibV4-a",
        "outputId": "d8eead91-d60e-4654-cf81-11889d08ac61",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 5</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>data.Recencia</th><th scope=col>data.Frequencia</th><th scope=col>data.Monetario</th><th scope=col>data.Customer_ID</th><th scope=col>data.clusters</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>173</td><td>  84</td><td>  2433.28</td><td>13085</td><td>5</td></tr>\n",
              "\t<tr><th scope=row>2</th><td> 19</td><td> 801</td><td> 29532.45</td><td>13078</td><td>1</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>464</td><td>  40</td><td>   613.08</td><td>15362</td><td>5</td></tr>\n",
              "\t<tr><th scope=row>4</th><td> 16</td><td>1058</td><td>608821.65</td><td>18102</td><td>4</td></tr>\n",
              "\t<tr><th scope=row>5</th><td> 19</td><td>1039</td><td> 24033.91</td><td>12682</td><td>1</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>114</td><td>  88</td><td> 14761.52</td><td>18087</td><td>1</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A data.frame: 6 × 5\n",
              "\\begin{tabular}{r|lllll}\n",
              "  & data.Recencia & data.Frequencia & data.Monetario & data.Customer\\_ID & data.clusters\\\\\n",
              "  & <dbl> & <int> & <dbl> & <dbl> & <int>\\\\\n",
              "\\hline\n",
              "\t1 & 173 &   84 &   2433.28 & 13085 & 5\\\\\n",
              "\t2 &  19 &  801 &  29532.45 & 13078 & 1\\\\\n",
              "\t3 & 464 &   40 &    613.08 & 15362 & 5\\\\\n",
              "\t4 &  16 & 1058 & 608821.65 & 18102 & 4\\\\\n",
              "\t5 &  19 & 1039 &  24033.91 & 12682 & 1\\\\\n",
              "\t6 & 114 &   88 &  14761.52 & 18087 & 1\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A data.frame: 6 × 5\n",
              "\n",
              "| <!--/--> | data.Recencia &lt;dbl&gt; | data.Frequencia &lt;int&gt; | data.Monetario &lt;dbl&gt; | data.Customer_ID &lt;dbl&gt; | data.clusters &lt;int&gt; |\n",
              "|---|---|---|---|---|---|\n",
              "| 1 | 173 |   84 |   2433.28 | 13085 | 5 |\n",
              "| 2 |  19 |  801 |  29532.45 | 13078 | 1 |\n",
              "| 3 | 464 |   40 |    613.08 | 15362 | 5 |\n",
              "| 4 |  16 | 1058 | 608821.65 | 18102 | 4 |\n",
              "| 5 |  19 | 1039 |  24033.91 | 12682 | 1 |\n",
              "| 6 | 114 |   88 |  14761.52 | 18087 | 1 |\n",
              "\n"
            ],
            "text/plain": [
              "  data.Recencia data.Frequencia data.Monetario data.Customer_ID data.clusters\n",
              "1 173             84              2433.28      13085            5            \n",
              "2  19            801             29532.45      13078            1            \n",
              "3 464             40               613.08      15362            5            \n",
              "4  16           1058            608821.65      18102            4            \n",
              "5  19           1039             24033.91      12682            1            \n",
              "6 114             88             14761.52      18087            1            "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tab <- as.data.frame(resultados[2])\n",
        "head(tab)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "ZsykV8enV48J",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [],
      "source": [
        "# Analisando os resultados da tabela dos cliente\n",
        "clusters_clientes <- data.table::data.table(tab)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "7zArWg5GV46L",
        "outputId": "4dc48295-bb30-4d1e-a523-0f5ec59de349",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.table: 5 × 2</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>data.clusters</th><th scope=col>Total</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>5</td><td>5551</td></tr>\n",
              "\t<tr><td>1</td><td> 295</td></tr>\n",
              "\t<tr><td>2</td><td>  27</td></tr>\n",
              "\t<tr><td>3</td><td>   6</td></tr>\n",
              "\t<tr><td>4</td><td>   2</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A data.table: 5 × 2\n",
              "\\begin{tabular}{ll}\n",
              " data.clusters & Total\\\\\n",
              " <int> & <int>\\\\\n",
              "\\hline\n",
              "\t 5 & 5551\\\\\n",
              "\t 1 &  295\\\\\n",
              "\t 2 &   27\\\\\n",
              "\t 3 &    6\\\\\n",
              "\t 4 &    2\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A data.table: 5 × 2\n",
              "\n",
              "| data.clusters &lt;int&gt; | Total &lt;int&gt; |\n",
              "|---|---|\n",
              "| 5 | 5551 |\n",
              "| 1 |  295 |\n",
              "| 2 |   27 |\n",
              "| 3 |    6 |\n",
              "| 4 |    2 |\n",
              "\n"
            ],
            "text/plain": [
              "  data.clusters Total\n",
              "1 5             5551 \n",
              "2 1              295 \n",
              "3 2               27 \n",
              "4 3                6 \n",
              "5 4                2 "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Frequência dos clientes por Clusters\n",
        "clusters_clientes[,\n",
        "                  .(Total = .N),\n",
        "                  by = data.clusters\n",
        "                  ][order(desc(Total))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "rcAK7APqV43o",
        "outputId": "e0416abb-581b-4e7d-e500-deeba792339f",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.table: 5 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>data.clusters</th><th scope=col>Media_recencia</th><th scope=col>Media_frequencia</th><th scope=col>Media_monetario</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>5</td><td>129.0</td><td>  49</td><td>   806.57</td></tr>\n",
              "\t<tr><td>1</td><td> 30.0</td><td> 476</td><td> 13370.86</td></tr>\n",
              "\t<tr><td>4</td><td> 16.5</td><td>2456</td><td>568712.08</td></tr>\n",
              "\t<tr><td>2</td><td> 23.0</td><td> 722</td><td> 70884.07</td></tr>\n",
              "\t<tr><td>3</td><td> 18.5</td><td>1718</td><td>221727.95</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A data.table: 5 × 4\n",
              "\\begin{tabular}{llll}\n",
              " data.clusters & Media\\_recencia & Media\\_frequencia & Media\\_monetario\\\\\n",
              " <int> & <dbl> & <dbl> & <dbl>\\\\\n",
              "\\hline\n",
              "\t 5 & 129.0 &   49 &    806.57\\\\\n",
              "\t 1 &  30.0 &  476 &  13370.86\\\\\n",
              "\t 4 &  16.5 & 2456 & 568712.08\\\\\n",
              "\t 2 &  23.0 &  722 &  70884.07\\\\\n",
              "\t 3 &  18.5 & 1718 & 221727.95\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A data.table: 5 × 4\n",
              "\n",
              "| data.clusters &lt;int&gt; | Media_recencia &lt;dbl&gt; | Media_frequencia &lt;dbl&gt; | Media_monetario &lt;dbl&gt; |\n",
              "|---|---|---|---|\n",
              "| 5 | 129.0 |   49 |    806.57 |\n",
              "| 1 |  30.0 |  476 |  13370.86 |\n",
              "| 4 |  16.5 | 2456 | 568712.08 |\n",
              "| 2 |  23.0 |  722 |  70884.07 |\n",
              "| 3 |  18.5 | 1718 | 221727.95 |\n",
              "\n"
            ],
            "text/plain": [
              "  data.clusters Media_recencia Media_frequencia Media_monetario\n",
              "1 5             129.0            49                806.57      \n",
              "2 1              30.0           476              13370.86      \n",
              "3 4              16.5          2456             568712.08      \n",
              "4 2              23.0           722              70884.07      \n",
              "5 3              18.5          1718             221727.95      "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Média de Recencias por clusters\n",
        "clusters_clientes[,\n",
        "                  .(Media_recencia = median(data.Recencia),\n",
        "                    Media_frequencia = median(data.Frequencia),\n",
        "                    Media_monetario = median(data.Monetario)),\n",
        "                  by = data.clusters\n",
        "                  ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "YpZIokXZcIPX",
        "outputId": "537dcd82-96ef-4a5c-960d-0d53a920114d",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>'data.Recencia'</li><li>'data.Frequencia'</li><li>'data.Monetario'</li><li>'data.Customer_ID'</li><li>'data.clusters'</li></ol>\n"
            ],
            "text/latex": [
              "\\begin{enumerate*}\n",
              "\\item 'data.Recencia'\n",
              "\\item 'data.Frequencia'\n",
              "\\item 'data.Monetario'\n",
              "\\item 'data.Customer\\_ID'\n",
              "\\item 'data.clusters'\n",
              "\\end{enumerate*}\n"
            ],
            "text/markdown": [
              "1. 'data.Recencia'\n",
              "2. 'data.Frequencia'\n",
              "3. 'data.Monetario'\n",
              "4. 'data.Customer_ID'\n",
              "5. 'data.clusters'\n",
              "\n",
              "\n"
            ],
            "text/plain": [
              "[1] \"data.Recencia\"    \"data.Frequencia\"  \"data.Monetario\"   \"data.Customer_ID\"\n",
              "[5] \"data.clusters\"   "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "names(clusters_clientes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "m4Hzi6znawqp",
        "outputId": "e5c05701-3ea1-46fc-d636-f54990dad931",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 600,
              "width": 1200
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Boxplor de Recência por clusters\n",
        "ggplot(clusters_clientes, aes(as.factor(data.clusters), data.Recencia )) +\n",
        "  geom_boxplot(aes(colour = as.factor(data.clusters))) +\n",
        "   labs(title = \"Distribuição da Recência por Clusters\",\n",
        "       x = \"Clusters\",\n",
        "       y = \"Recência\") +\n",
        "  theme(\n",
        "    plot.title = element_text(size = 20, face = \"bold\"),\n",
        "    axis.title.x = element_text(size = 16),\n",
        "    axis.title.y = element_text(size = 16)\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "WTFrndi1WHmA",
        "outputId": "ccb41a67-4a94-439b-df96-bb8072f5c264",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 600,
              "width": 1200
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Boxplor de Frequência por clusters\n",
        "ggplot(clusters_clientes, aes(as.factor(data.clusters), data.Frequencia)) +\n",
        "  geom_boxplot(aes(colour = as.factor(data.clusters))) +\n",
        "   labs(title = \"Distribuição da Frequência por Clusters\",\n",
        "       x = \"Clusters\",\n",
        "       y = \"Frequência\") +\n",
        "  theme(\n",
        "    plot.title = element_text(size = 20, face = \"bold\"),\n",
        "    axis.title.x = element_text(size = 16),\n",
        "    axis.title.y = element_text(size = 16)\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "Fh37olY2WHjQ",
        "outputId": "dbaf8833-be18-4061-94a9-c6cf570967c1",
        "vscode": {
          "languageId": "r"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 600,
              "width": 1200
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Boxplor de Valor Monetário por clusters\n",
        "ggplot(clusters_clientes, aes(as.factor(data.clusters), data.Monetario)) +\n",
        "  geom_boxplot(aes(colour = as.factor(data.clusters))) +\n",
        "   labs(title = \"Distribuição da Valor Monetário por Clusters\",\n",
        "       x = \"Clusters\",\n",
        "       y = \"Valor Monetário\") +\n",
        "  theme(\n",
        "    plot.title = element_text(size = 20, face = \"bold\"),\n",
        "    axis.title.x = element_text(size = 16),\n",
        "    axis.title.y = element_text(size = 16)\n",
        "  )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gqyMkm8ZWLjb"
      },
      "source": [
        "# 5. Análise dos Clusters:\n",
        "\n",
        "Recência Média: A recência média representa o quão recentemente os clientes de um cluster realizaram uma transação. Dentro deste contexto, o cluster 5 apresenta a melhor média (menor), indicando que são possíveis usuários mais ativos e/ou engajados com a marca. Em contrapartida, o cluster 2 apresenta uma média 7 vezes maior que o cluster 5, possivelmente este grupo não está envolvido com a marca ou estão se afastando;\n",
        "\n",
        "Frequência Média: A frequência média indica com que frequência os clientes de um cluster realizam transações. O cluster 5 apresenta uma frequência média de 228, indicando clientes possivelmente fiéis e que retornam regularmente para realizarem novas compras. O cluster 2 apresenta um frequência de 19, indicando clientes menos engajados e que realizam compras de forma esporádicas;\n",
        "\n",
        "Valor Monetário Médio: O valor monetário representa o valor médio das transações realizadas pelos clientes de um cluster. O cluster 5 apresenta uma média de 4370.4, demonstrando que os clientes estão gastando mais em cada transação, o que indica maior lealdade ou interesse em serviços e nos produtos da marca."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "R",
      "name": "ir"
    },
    "language_info": {
      "name": "R"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}