SEG_CLIEN_CLUST.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# SEGMENTAÇÃO DE CLIENTES UTILIZANDO CLUSTERIZAÇÃO"
      ],
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        "id": "SdnbYfUnN9aX"
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      "source": [
        "Este projeto tem por objetivo desenvolver um modelo de Machine Learning não supervisonado para segmentar clientes frequentadores de um shopping.\n",
        "\n",
        "os dados foram extraidos do Kaggle: \n",
        "https://www.kaggle.com/shwetabh123/mall-customers"
      ],
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      "source": [
        "# Importando as bibliotecas\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importando a biblioteca \"warnings\" para ignorar mensagens de erro\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
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      "execution_count": 135,
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      "source": [
        "# Importando o arquivo CSV e criando um dataframe\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/MACHINE_LEARNNING/Mall_Customers.csv\", sep=\",\", encoding=\"iso-8859-1\")"
      ],
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      "source": [
        "# Visualizando as 5 primeiras linhas do dataframe\n",
        "df.head()"
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            "text/plain": [
              "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
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      "source": [
        "# ATRIBUTOS:\n",
        "* CustomerID: Identificação do cliente\n",
        "* Genre: Gênero\n",
        "* Age: Idade\n",
        "* Annual Income (k$): Rendimento anual\n",
        "* Spending Score (1-100): Pontuação de gastos"
      ],
      "metadata": {
        "id": "s9IvdJ1KRQtF"
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    {
      "cell_type": "markdown",
      "source": [
        "# Visualização gráfica dos dados"
      ],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Idade"
      ],
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importando o Plotly para criar representações gráficas dos atributos\n",
        "import plotly.express as px"
      ],
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      "execution_count": 138,
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      "source": [
        "# Criando um histograma com o atributo Age ( Idade )\n",
        "hist = px.histogram( df, x = \"Age\", nbins = 60)\n",
        "hist.update_layout (width = 600, height = 400, title_text = \"Distribuição de Idades\")\n",
        "hist.show()"
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O gráfico mostra uma distribuição diversificada entre as idades, sendo a \n",
        "menor idade 18 anos com 4 contagens e a idade máxima 70 anos com 2 contagens."
      ],
      "metadata": {
        "id": "0X5uGveNV8U9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gênero"
      ],
      "metadata": {
        "id": "ULqFpPOKTslQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Agora veremos as distribuições de gênero\n",
        "hist = px.histogram( df, x = \"Genre\", nbins = 60)\n",
        "hist.update_layout (width = 600, height = 400, title_text = \"Distribuição de gênero\")\n",
        "hist.show()\n",
        "df[\"Genre\"].value_counts() # contagem por tipo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "HBf2PgiDTvxh",
        "outputId": "bf7c6ee7-c9e6-4e1a-f9cc-95996ab8c58d"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Female    112\n",
              "Male       88\n",
              "Name: Genre, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizando a quantidade de linhas e colunas\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCmRBIaBWYdg",
        "outputId": "bb150b34-002d-4f70-a979-c50a3b224929"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploração e tratamento dos dados"
      ],
      "metadata": {
        "id": "3-yjynv5oEhM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Alterando o nome das colunas"
      ],
      "metadata": {
        "id": "DALTWafqoMbw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
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          "height": 0
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        "outputId": "c01f3895-a13e-462b-d14c-6665ee26dae6"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Alterando o nome da coluna Genre para Gênero\n",
        "df.rename(columns={\"Genre\":\"genero\"},inplace=True)"
      ],
      "metadata": {
        "id": "9gVaUKY8osy1"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Alterando o nome da coluna Age para Idade\n",
        "df.rename(columns={\"Age\":\"idade\"},inplace=True)"
      ],
      "metadata": {
        "id": "UKe63QirpPOL"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Alterando o nome da coluna Annual Income (k$) para Rendimento \n",
        "df.rename(columns={\"Annual Income (k$)\":\"rendimento\"},inplace=True)"
      ],
      "metadata": {
        "id": "mIcjxZLYpWyL"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Alterando o nome da coluna Spending Score (1-100) para Pontuação\n",
        "df.rename(columns={\"Spending Score (1-100)\":\"pontuacao\"},inplace=True)"
      ],
      "metadata": {
        "id": "bfMki5cgpqWO"
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      "execution_count": 146,
      "outputs": []
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      "execution_count": 147,
      "outputs": [
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            "text/plain": [
              "   CustomerID  genero  idade  rendimento  pontuacao\n",
              "0           1    Male     19          15         39\n",
              "1           2    Male     21          15         81\n",
              "2           3  Female     20          16          6\n",
              "3           4  Female     23          16         77\n",
              "4           5  Female     31          17         40"
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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>CustomerID</th>\n",
              "      <th>genero</th>\n",
              "      <th>idade</th>\n",
              "      <th>rendimento</th>\n",
              "      <th>pontuacao</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f0f40d7-8c15-4daa-bd62-829a50bf2400')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8f0f40d7-8c15-4daa-bd62-829a50bf2400 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8f0f40d7-8c15-4daa-bd62-829a50bf2400');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Obs.: não alterei a coluna CustomerID porque ela será irrelevante e será excluída"
      ],
      "metadata": {
        "id": "rsASfU_4p_tD"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Valores Missing ( NAN )"
      ],
      "metadata": {
        "id": "Ls5HDGRhgrEs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvS2NxM0g1Qe",
        "outputId": "95d4edfe-b9b4-4382-d7b9-a12bd83b85d6"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID    0\n",
              "genero        0\n",
              "idade         0\n",
              "rendimento    0\n",
              "pontuacao     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificamos que não existem valores nulos."
      ],
      "metadata": {
        "id": "eOwCcxo9g8_T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Análise dos tipos de atributos:\n",
        "* object = string\n",
        "* int64 = números inteiros\n",
        "* float64 = números reais\n",
        "* complex = números complexos\n",
        "\n",
        "\n",
        "Obs.: Lembrando que não é possivel desenvolver modelos de machine learning usando atributos do tipo OBJECT"
      ],
      "metadata": {
        "id": "youGMOjghI9D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpgDJMIAhFA1",
        "outputId": "2cc7a0a4-bbb1-4829-81e7-f62517d77e38"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID     int64\n",
              "genero        object\n",
              "idade          int64\n",
              "rendimento     int64\n",
              "pontuacao      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dados estatísticos"
      ],
      "metadata": {
        "id": "1n_Ng8bRh6sM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "X-zz_gSniAsm",
        "outputId": "b36d7355-5762-41f2-8a40-cd2ac00b2944"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       CustomerID       idade  rendimento   pontuacao\n",
              "count  200.000000  200.000000  200.000000  200.000000\n",
              "mean   100.500000   38.850000   60.560000   50.200000\n",
              "std     57.879185   13.969007   26.264721   25.823522\n",
              "min      1.000000   18.000000   15.000000    1.000000\n",
              "25%     50.750000   28.750000   41.500000   34.750000\n",
              "50%    100.500000   36.000000   61.500000   50.000000\n",
              "75%    150.250000   49.000000   78.000000   73.000000\n",
              "max    200.000000   70.000000  137.000000   99.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-85869b96-36fe-49a4-82a5-293274357ea1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>idade</th>\n",
              "      <th>rendimento</th>\n",
              "      <th>pontuacao</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>38.850000</td>\n",
              "      <td>60.560000</td>\n",
              "      <td>50.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>57.879185</td>\n",
              "      <td>13.969007</td>\n",
              "      <td>26.264721</td>\n",
              "      <td>25.823522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.750000</td>\n",
              "      <td>28.750000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>34.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>61.500000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>150.250000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>73.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>99.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85869b96-36fe-49a4-82a5-293274357ea1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-85869b96-36fe-49a4-82a5-293274357ea1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85869b96-36fe-49a4-82a5-293274357ea1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos verificar que:\n",
        "* A amostra possui 200 elementos\n",
        "* A média de idade dos frequentadores do shopping fica em 38, e a mediana 36\n",
        "* A idade mínima é 18 e a máxima é 70\n",
        "* O rendimento máximo encontrado foi de $137000\n",
        "* A pontuação média fica em 50.2"
      ],
      "metadata": {
        "id": "0GYwBJiziNi2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Análise de Outliers ( Dados discrepantes )\n",
        "\n",
        "Para essa análise utilizamos o gráfico boxplot"
      ],
      "metadata": {
        "id": "GKv7InBtj5e6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importando o Plotly para criar representações gráficas dos atributos\n",
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "foy99mP4j_xr"
      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "boxplot = px.box( df, y = \"rendimento\")\n",
        "boxplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "B1ZLedUYkG-z",
        "outputId": "2b7c41f0-2eac-4d95-c945-026b9925b2c1"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obs.: Rendimentos possui um valor fora do padrão de 137 mil, acima do valor máximo de 129 mil. Por não ser um valor tão distante podemos manter no modelo."
      ],
      "metadata": {
        "id": "gyT7PpXjk7uN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "boxplot = px.box( df, y = \"idade\")\n",
        "boxplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "IGRV7U9LkpZ7",
        "outputId": "d7c6bb95-efcb-409a-c227-97fcb29fd1b0"
      },
      "execution_count": 154,
      "outputs": [
        {
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    {
      "cell_type": "code",
      "source": [
        "boxplot = px.box( df, y = \"pontuacao\")\n",
        "boxplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "csfo5waaksZb",
        "outputId": "b655facc-7218-4685-aa73-f970fb756d9e"
      },
      "execution_count": 155,
      "outputs": [
        {
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                       {\"responsive\": true}                    ).then(function(){\n",
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              "</body>\n",
              "</html>"
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nenhum dado discrepante encontrado nos outros atributos."
      ],
      "metadata": {
        "id": "Ad8yIfs2uG0a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pré-processamento\n",
        "\n",
        "O pré-processamento é uma etapa fundamental que pode melhorar a performance dos algoritmos de análise, através da redução de dimensionalidade e eliminação de ruidos que interfiram no funcionamento dos algoritmos. "
      ],
      "metadata": {
        "id": "Es-rZ7cDub9y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nURaWjFp9qyp",
        "outputId": "49d6594c-b786-40c6-ec80-5c552f5a73a3"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID  genero  idade  rendimento  pontuacao\n",
              "0           1    Male     19          15         39\n",
              "1           2    Male     21          15         81\n",
              "2           3  Female     20          16          6\n",
              "3           4  Female     23          16         77\n",
              "4           5  Female     31          17         40"
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              "\n",
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              "      <td>39</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
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              "      <td>6</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-138bc1cb-bacd-4b48-9e93-c69fdc3844a8');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Excluindo o atributo CustomerID\n",
        "\n",
        "O atributo CustomerID não possui relevância para o modelo pois ele apenas emumera os elementos. Deste modo ele deve ser excluído."
      ],
      "metadata": {
        "id": "AuRtL2fQ9zaP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando um novo dataframe para receber os dados, excluindo o atributo CustomerID."
      ],
      "metadata": {
        "id": "cjtglbyaACu3"
      },
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df.drop(labels=\"CustomerID\",axis=1) # lembrando que axis significa eixo e 1 equivale a coluna.\n",
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IByoaTNmDSYP",
        "outputId": "1ed7ce03-d6f9-4d64-fcac-98c34c44b33c"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   genero  idade  rendimento  pontuacao\n",
              "0    Male     19          15         39\n",
              "1    Male     21          15         81\n",
              "2  Female     20          16          6\n",
              "3  Female     23          16         77\n",
              "4  Female     31          17         40"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>genero</th>\n",
              "      <th>idade</th>\n",
              "      <th>rendimento</th>\n",
              "      <th>pontuacao</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Transformando dados categóricos em dados numéricos"
      ],
      "metadata": {
        "id": "fN5i4wUkGQje"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2[\"genero\"].replace({\"Female\":0,\"Male\":1},inplace=True) # Inplace = True altera os dados de forma permanente no dataframe atual\n",
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "enzaFelZDtin",
        "outputId": "eee439af-b704-4a0d-e170-792d6bc26bd4"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   genero  idade  rendimento  pontuacao\n",
              "0       1     19          15         39\n",
              "1       1     21          15         81\n",
              "2       0     20          16          6\n",
              "3       0     23          16         77\n",
              "4       0     31          17         40"
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c26e7306-27d0-4781-9705-285522c25439');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Escalonamento\n",
        "\n",
        "O objetivo do escalonamento é mudar os valores das colunas numéricas no conjunto de dados para usar uma escala comum, sem distorcer as diferenças nos intervalos de valores nem perder informações. O escalonamento ( normalização ) também é necessário para alguns algoritmos para modelar os dados corretamente.\n",
        "O escalonamento considera média próxima de zero e desvio padrão próximo de 1."
      ],
      "metadata": {
        "id": "r80Ib1PWHoqV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "qui_IkwfG9Fc"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "escala = StandardScaler()\n",
        "df2_esc = escala.fit_transform(df2) # Criando um novo dataframe com os dados escalonados"
      ],
      "metadata": {
        "id": "rmiEzW0YIQGf"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2_esc"
      ],
      "metadata": {
        "id": "hmnEoRQNIdf3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12eb6c8f-5b90-4f18-cf6b-5038cbf06318"
      },
      "execution_count": 162,
      "outputs": [
        {
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              "       [ 1.12815215,  2.23553238, -0.4412386 ,  0.18634349],\n",
              "       [-0.88640526,  0.58489852, -0.4412386 , -0.3183368 ],\n",
              "       [-0.88640526,  1.51786549, -0.40306917, -0.04658587],\n",
              "       [-0.88640526,  1.51786549, -0.40306917,  0.22516505],\n",
              "       [ 1.12815215,  1.4460988 , -0.25039146, -0.12422899],\n",
              "       [ 1.12815215, -0.92220196, -0.25039146,  0.14752193],\n",
              "       [-0.88640526,  0.44136514, -0.25039146,  0.10870037],\n",
              "       [ 1.12815215,  0.08253169, -0.25039146, -0.08540743],\n",
              "       [-0.88640526, -1.13750203, -0.25039146,  0.06987881],\n",
              "       [-0.88640526,  0.7284319 , -0.25039146, -0.3183368 ],\n",
              "       [ 1.12815215,  1.30256542, -0.25039146,  0.03105725],\n",
              "       [ 1.12815215, -0.06100169, -0.25039146,  0.18634349],\n",
              "       [ 1.12815215,  2.02023231, -0.25039146, -0.35715836],\n",
              "       [-0.88640526,  0.51313183, -0.25039146, -0.24069368],\n",
              "       [-0.88640526, -1.28103541, -0.25039146,  0.26398661],\n",
              "       [ 1.12815215,  0.65666521, -0.25039146, -0.16305055],\n",
              "       [-0.88640526,  1.15903204, -0.13588317,  0.30280817],\n",
              "       [-0.88640526, -1.20926872, -0.13588317,  0.18634349],\n",
              "       [-0.88640526, -0.34806844, -0.09771374,  0.38045129],\n",
              "       [-0.88640526,  0.80019859, -0.09771374, -0.16305055],\n",
              "       [-0.88640526,  2.091999  , -0.05954431,  0.18634349],\n",
              "       [ 1.12815215, -1.49633548, -0.05954431, -0.35715836],\n",
              "       [ 1.12815215,  0.65666521, -0.02137488, -0.04658587],\n",
              "       [-0.88640526,  0.08253169, -0.02137488, -0.39597992],\n",
              "       [-0.88640526, -0.49160182, -0.02137488, -0.3183368 ],\n",
              "       [ 1.12815215, -1.06573534, -0.02137488,  0.06987881],\n",
              "       [-0.88640526,  0.58489852, -0.02137488, -0.12422899],\n",
              "       [-0.88640526, -0.85043527, -0.02137488, -0.00776431],\n",
              "       [ 1.12815215,  0.65666521,  0.01679455, -0.3183368 ],\n",
              "       [ 1.12815215, -1.3528021 ,  0.01679455, -0.04658587],\n",
              "       [-0.88640526, -1.13750203,  0.05496398, -0.35715836],\n",
              "       [-0.88640526,  0.7284319 ,  0.05496398, -0.08540743],\n",
              "       [ 1.12815215,  2.02023231,  0.05496398,  0.34162973],\n",
              "       [ 1.12815215, -0.92220196,  0.05496398,  0.18634349],\n",
              "       [ 1.12815215,  0.7284319 ,  0.05496398,  0.22516505],\n",
              "       [-0.88640526, -1.28103541,  0.05496398, -0.3183368 ],\n",
              "       [-0.88640526,  1.94846562,  0.09313341, -0.00776431],\n",
              "       [ 1.12815215,  1.08726535,  0.09313341, -0.16305055],\n",
              "       [ 1.12815215,  2.091999  ,  0.09313341, -0.27951524],\n",
              "       [ 1.12815215,  1.94846562,  0.09313341, -0.08540743],\n",
              "       [ 1.12815215,  1.87669894,  0.09313341,  0.06987881],\n",
              "       [-0.88640526, -1.42456879,  0.09313341,  0.14752193],\n",
              "       [-0.88640526, -0.06100169,  0.13130284, -0.3183368 ],\n",
              "       [ 1.12815215, -1.42456879,  0.13130284, -0.16305055],\n",
              "       [-0.88640526, -1.49633548,  0.16947227, -0.08540743],\n",
              "       [-0.88640526, -1.42456879,  0.16947227, -0.00776431],\n",
              "       [-0.88640526,  1.73316556,  0.16947227, -0.27951524],\n",
              "       [-0.88640526,  0.7284319 ,  0.16947227,  0.34162973],\n",
              "       [-0.88640526,  0.87196528,  0.24581112, -0.27951524],\n",
              "       [-0.88640526,  0.80019859,  0.24581112,  0.26398661],\n",
              "       [ 1.12815215, -0.85043527,  0.24581112,  0.22516505],\n",
              "       [-0.88640526, -0.06100169,  0.24581112, -0.39597992],\n",
              "       [-0.88640526,  0.08253169,  0.32214998,  0.30280817],\n",
              "       [ 1.12815215,  0.010765  ,  0.32214998,  1.58391968],\n",
              "       [-0.88640526, -1.13750203,  0.36031941, -0.82301709],\n",
              "       [-0.88640526, -0.56336851,  0.36031941,  1.04041783],\n",
              "       [ 1.12815215,  0.29783176,  0.39848884, -0.59008772],\n",
              "       [ 1.12815215,  0.08253169,  0.39848884,  1.73920592],\n",
              "       [ 1.12815215,  1.4460988 ,  0.39848884, -1.52180518],\n",
              "       [ 1.12815215, -0.06100169,  0.39848884,  0.96277471],\n",
              "       [ 1.12815215,  0.58489852,  0.39848884, -1.5994483 ],\n",
              "       [ 1.12815215,  0.010765  ,  0.39848884,  0.96277471],\n",
              "       [-0.88640526, -0.99396865,  0.43665827, -0.62890928],\n",
              "       [-0.88640526, -0.56336851,  0.43665827,  0.80748846],\n",
              "       [ 1.12815215, -1.3528021 ,  0.4748277 , -1.75473454],\n",
              "       [-0.88640526, -0.70690189,  0.4748277 ,  1.46745499],\n",
              "       [-0.88640526,  0.36959845,  0.4748277 , -1.67709142],\n",
              "       [ 1.12815215, -0.49160182,  0.4748277 ,  0.88513158],\n",
              "       [ 1.12815215, -1.42456879,  0.51299713, -1.56062674],\n",
              "       [-0.88640526, -0.27630176,  0.51299713,  0.84631002],\n",
              "       [-0.88640526,  1.30256542,  0.55116656, -1.75473454],\n",
              "       [ 1.12815215, -0.49160182,  0.55116656,  1.6615628 ],\n",
              "       [-0.88640526, -0.77866858,  0.58933599, -0.39597992],\n",
              "       [-0.88640526, -0.49160182,  0.58933599,  1.42863343],\n",
              "       [ 1.12815215, -0.99396865,  0.62750542, -1.48298362],\n",
              "       [ 1.12815215, -0.77866858,  0.62750542,  1.81684904],\n",
              "       [ 1.12815215,  0.65666521,  0.62750542, -0.55126616],\n",
              "       [-0.88640526, -0.49160182,  0.62750542,  0.92395314],\n",
              "       [-0.88640526, -0.34806844,  0.66567484, -1.09476801],\n",
              "       [ 1.12815215, -0.34806844,  0.66567484,  1.54509812],\n",
              "       [ 1.12815215,  0.29783176,  0.66567484, -1.28887582],\n",
              "       [ 1.12815215,  0.010765  ,  0.66567484,  1.46745499],\n",
              "       [-0.88640526,  0.36959845,  0.66567484, -1.17241113],\n",
              "       [-0.88640526, -0.06100169,  0.66567484,  1.00159627],\n",
              "       [-0.88640526,  0.58489852,  0.66567484, -1.32769738],\n",
              "       [-0.88640526, -0.85043527,  0.66567484,  1.50627656],\n",
              "       [ 1.12815215, -0.13276838,  0.66567484, -1.91002079],\n",
              "       [-0.88640526, -0.6351352 ,  0.66567484,  1.07923939],\n",
              "       [ 1.12815215, -0.34806844,  0.66567484, -1.91002079],\n",
              "       [-0.88640526, -0.6351352 ,  0.66567484,  0.88513158],\n",
              "       [-0.88640526,  1.23079873,  0.70384427, -0.59008772],\n",
              "       [-0.88640526, -0.70690189,  0.70384427,  1.27334719],\n",
              "       [ 1.12815215, -1.42456879,  0.78018313, -1.75473454],\n",
              "       [-0.88640526, -0.56336851,  0.78018313,  1.6615628 ],\n",
              "       [ 1.12815215,  0.80019859,  0.93286085, -0.93948177],\n",
              "       [-0.88640526, -0.20453507,  0.93286085,  0.96277471],\n",
              "       [ 1.12815215,  0.22606507,  0.97103028, -1.17241113],\n",
              "       [-0.88640526, -0.41983513,  0.97103028,  1.73920592],\n",
              "       [-0.88640526, -0.20453507,  1.00919971, -0.90066021],\n",
              "       [ 1.12815215, -0.49160182,  1.00919971,  0.49691598],\n",
              "       [ 1.12815215,  0.08253169,  1.00919971, -1.44416206],\n",
              "       [ 1.12815215, -0.77866858,  1.00919971,  0.96277471],\n",
              "       [ 1.12815215, -0.20453507,  1.00919971, -1.56062674],\n",
              "       [ 1.12815215, -0.20453507,  1.00919971,  1.62274124],\n",
              "       [-0.88640526,  0.94373197,  1.04736914, -1.44416206],\n",
              "       [-0.88640526, -0.6351352 ,  1.04736914,  1.38981187],\n",
              "       [ 1.12815215,  1.37433211,  1.04736914, -1.36651894],\n",
              "       [ 1.12815215, -0.85043527,  1.04736914,  0.72984534],\n",
              "       [ 1.12815215,  1.4460988 ,  1.23821628, -1.4053405 ],\n",
              "       [ 1.12815215, -0.27630176,  1.23821628,  1.54509812],\n",
              "       [-0.88640526, -0.13276838,  1.390894  , -0.7065524 ],\n",
              "       [-0.88640526, -0.49160182,  1.390894  ,  1.38981187],\n",
              "       [ 1.12815215,  0.51313183,  1.42906343, -1.36651894],\n",
              "       [-0.88640526, -0.70690189,  1.42906343,  1.46745499],\n",
              "       [-0.88640526,  0.15429838,  1.46723286, -0.43480148],\n",
              "       [ 1.12815215, -0.6351352 ,  1.46723286,  1.81684904],\n",
              "       [-0.88640526,  1.08726535,  1.54357172, -1.01712489],\n",
              "       [ 1.12815215, -0.77866858,  1.54357172,  0.69102378],\n",
              "       [-0.88640526,  0.15429838,  1.61991057, -1.28887582],\n",
              "       [-0.88640526, -0.20453507,  1.61991057,  1.35099031],\n",
              "       [-0.88640526, -0.34806844,  1.61991057, -1.05594645],\n",
              "       [-0.88640526, -0.49160182,  1.61991057,  0.72984534],\n",
              "       [ 1.12815215, -0.41983513,  2.00160487, -1.63826986],\n",
              "       [-0.88640526, -0.06100169,  2.00160487,  1.58391968],\n",
              "       [-0.88640526,  0.58489852,  2.26879087, -1.32769738],\n",
              "       [-0.88640526, -0.27630176,  2.26879087,  1.11806095],\n",
              "       [-0.88640526,  0.44136514,  2.49780745, -0.86183865],\n",
              "       [ 1.12815215, -0.49160182,  2.49780745,  0.92395314],\n",
              "       [ 1.12815215, -0.49160182,  2.91767117, -1.25005425],\n",
              "       [ 1.12815215, -0.6351352 ,  2.91767117,  1.27334719]])"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# K-means \n",
        "\n",
        "K-Means é um algoritmo de clusterização (ou agrupamento) disponível na biblioteca Scikit-Learn.\n",
        "\n",
        "É um algoritmo de aprendizado não supervisionado (ou seja, que não precisa de inputs de confirmação externos) que avalia e clusteriza os dados de acordo com suas características, como por exemplo:\n",
        "\n",
        "* lojas/centro logistico\n",
        "* clientes/produtos ou serviços semelhantes\n",
        "* clientes/características semelhantes\n",
        "* séries/gênero da série ou faixa etaria\n",
        "* usuarios de uma rede social/usuario influenciador\n",
        "* paciente/sintoma ou característica semelhante"
      ],
      "metadata": {
        "id": "42x8gDdx8tn6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K-means com dois atributos\n",
        "\n",
        "Inicialmente usaremos apenas dois atributos para o modelo, que neste caso será os atributos rendimento e pontuacao"
      ],
      "metadata": {
        "id": "K6rq-bUwwnuO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando um novo dataframe para receber apenas os atributos rendimento e pontuacao usando os indices\n",
        "dados = df2.iloc[:,[2,3]].values\n",
        "dados"
      ],
      "metadata": {
        "id": "p6pi8X5EwzJt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07c9d992-4399-447b-bd65-1b00fa8b0226"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [137,  18],\n",
              "       [137,  83]])"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Método Elbow - identifica o número de clusters adequado para o modelo\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "xldarycozP_V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss = []\n",
        "for i in range (1, 11):\n",
        "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state= 5, max_iter=300)\n",
        "    kmeans.fit(dados)\n",
        "    # inertia - Método para gerar o wcss\n",
        "    wcss.append(kmeans.inertia_)"
      ],
      "metadata": {
        "id": "ug1cadkTzcn-"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "o61F5vG21eBC"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.lineplot(range(1,11), wcss, marker=\"o\", color=\"red\")\n",
        "plt.title(\"Método Elbow\")\n",
        "plt.xlabel(\"Número de clusters\")\n",
        "plt.ylabel(\"WCSS\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "hwg_3mg22CbZ",
        "outputId": "331426ba-03bb-492b-8646-0e95851b755a"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'WCSS')"
            ]
          },
          "metadata": {},
          "execution_count": 166
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Através do gráfico é possivel identificar o ponto de inflexão, que neste caso seria o 4\n",
        "# O ponto de inflexão representa a quantidade ideal de clusters"
      ],
      "metadata": {
        "id": "tcru88UR3X8j"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Agrupamento"
      ],
      "metadata": {
        "id": "ZV0D8wxd3SC5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters = 4, init = \"k-means++\", random_state = 5, max_iter = 300)"
      ],
      "metadata": {
        "id": "qiAGemYr3Vsa"
      },
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans1 = kmeans.fit(dados)"
      ],
      "metadata": {
        "id": "7qhY9y784CVa"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Coordenadas dos centróides\n",
        "centroides = kmeans1.cluster_centers_\n",
        "centroides"
      ],
      "metadata": {
        "id": "iviIvlbC4Qjq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d188f78b-f65c-4b09-ad20-31c715d0961a"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[48.26      , 56.48      ],\n",
              "       [86.53846154, 82.12820513],\n",
              "       [26.30434783, 20.91304348],\n",
              "       [87.        , 18.63157895]])"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classificação dos dados\n",
        "classificacao = kmeans1.labels_\n",
        "classificacao"
      ],
      "metadata": {
        "id": "EsEg01pD4crz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb6e666a-28f7-459e-b003-6c35bb709930"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0,\n",
              "       2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0,\n",
              "       2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 1, 3, 1, 3, 1, 3, 1,\n",
              "       3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1,\n",
              "       3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1,\n",
              "       3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1,\n",
              "       3, 1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Usando como exemplo o grupo da 3º linha\n",
        "dados[3,:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f-i1IrEU5Dzx",
        "outputId": "98d1f6c8-a128-4be2-d4ab-0f1cc4f707e3"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([16, 77])"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizando a classificação do 3º elemento \n",
        "classificacao[3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VEu9NNzB5SSS",
        "outputId": "431589e5-15f3-4e12-8f46-e8879f084c61"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizando graficamente com PLOTLY os agrupamentos por grupo"
      ],
      "metadata": {
        "id": "HgWH1sPi6A2J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "import plotly.graph_objects as go"
      ],
      "metadata": {
        "id": "lzzD-8wY6GQZ"
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graf1 = px.scatter( x = dados[:,0], y = dados[:,1], color = classificacao)\n",
        "graf2 = px.scatter( x = centroides[:,0], y = centroides[:,1], size = [15,15,15,15])\n",
        "graf3 = go.Figure( data = graf1.data + graf2.data)\n",
        "graf3.update_layout( width=800, height = 500, title_text=\"Agrupamento K-means\")\n",
        "graf3.update_xaxes(title=\"Rendimento anual (K$)\")\n",
        "graf3.update_yaxes(title=\"Pontuação\")\n",
        "graf3.show()"
      ],
      "metadata": {
        "id": "syPD7G6X6Uz1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "93c6cf6c-8c6f-4198-c901-5d173fe6a22e"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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        "Visualizando o agrupamento por uma lista"
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        "agrupamento"
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              "     Grupo\n",
              "0        2\n",
              "1        0\n",
              "2        2\n",
              "3        0\n",
              "4        2\n",
              "..     ...\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3ed31aa5-d96b-4be0-afbd-167be0042210');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Juntando o atributo Grupo ao dataframe original. Será criado um novo dataframe para manter a integridade dos dados\n",
        "df3 = pd.concat([df,agrupamento],axis=1)\n",
        "df3"
      ],
      "metadata": {
        "id": "AUumtnSU-PIA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "e53f78e9-9b3b-4218-cebd-4290ac100405"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     CustomerID  genero  idade  rendimento  pontuacao  Grupo\n",
              "0             1    Male     19          15         39      2\n",
              "1             2    Male     21          15         81      0\n",
              "2             3  Female     20          16          6      2\n",
              "3             4  Female     23          16         77      0\n",
              "4             5  Female     31          17         40      2\n",
              "..          ...     ...    ...         ...        ...    ...\n",
              "195         196  Female     35         120         79      1\n",
              "196         197  Female     45         126         28      3\n",
              "197         198    Male     32         126         74      1\n",
              "198         199    Male     32         137         18      3\n",
              "199         200    Male     30         137         83      1\n",
              "\n",
              "[200 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-62a0fdf5-dea3-46e1-b323-383c80814001\">\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "      <td>2</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>120</td>\n",
              "      <td>79</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>Female</td>\n",
              "      <td>45</td>\n",
              "      <td>126</td>\n",
              "      <td>28</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>126</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>137</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62a0fdf5-dea3-46e1-b323-383c80814001')\"\n",
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              "          const element = document.querySelector('#df-62a0fdf5-dea3-46e1-b323-383c80814001');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizando os dados"
      ],
      "metadata": {
        "id": "cM3daP6r_OWo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Buscando os dados da linha 150\n",
        "df3.loc[150,:]"
      ],
      "metadata": {
        "id": "iDkQhDkz_Rjc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "705a5d0a-dd18-496d-cd50-de97044d2030"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID     151\n",
              "genero        Male\n",
              "idade           43\n",
              "rendimento      78\n",
              "pontuacao       17\n",
              "Grupo            3\n",
              "Name: 150, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Buscando os dados usando o CustomerID\n",
        "df3.loc[df3.CustomerID == 92]"
      ],
      "metadata": {
        "id": "i6FBitxl_xHP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "89cce209-ce0e-4a07-e121-876d5536e65c"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    CustomerID genero  idade  rendimento  pontuacao  Grupo\n",
              "91          92   Male     18          59         41      0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-851810c3-9cfe-48ea-88bd-d4d471cc38a4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>59</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-851810c3-9cfe-48ea-88bd-d4d471cc38a4')\"\n",
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              "      </button>\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-851810c3-9cfe-48ea-88bd-d4d471cc38a4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-851810c3-9cfe-48ea-88bd-d4d471cc38a4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando um dataframe apenas com o grupo 0\n",
        "grupo0 = df3.loc[df3.Grupo==0]\n",
        "grupo0 "
      ],
      "metadata": {
        "id": "FK4AbU-pAEiM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "8cef7737-3ce8-496e-8308-f956f2108df4"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     CustomerID  genero  idade  rendimento  pontuacao  Grupo\n",
              "1             2    Male     21          15         81      0\n",
              "3             4  Female     23          16         77      0\n",
              "5             6  Female     22          17         76      0\n",
              "7             8  Female     23          18         94      0\n",
              "9            10  Female     30          19         72      0\n",
              "..          ...     ...    ...         ...        ...    ...\n",
              "118         119  Female     51          67         43      0\n",
              "119         120  Female     50          67         57      0\n",
              "120         121    Male     27          67         56      0\n",
              "121         122  Female     38          67         40      0\n",
              "122         123  Female     40          69         58      0\n",
              "\n",
              "[100 rows x 6 columns]"
            ],
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              "\n",
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              "      <th></th>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Female</td>\n",
              "      <td>22</td>\n",
              "      <td>17</td>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>18</td>\n",
              "      <td>94</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "      <td>30</td>\n",
              "      <td>19</td>\n",
              "      <td>72</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>119</td>\n",
              "      <td>Female</td>\n",
              "      <td>51</td>\n",
              "      <td>67</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>120</td>\n",
              "      <td>Female</td>\n",
              "      <td>50</td>\n",
              "      <td>67</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>121</td>\n",
              "      <td>Male</td>\n",
              "      <td>27</td>\n",
              "      <td>67</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>122</td>\n",
              "      <td>Female</td>\n",
              "      <td>38</td>\n",
              "      <td>67</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>123</td>\n",
              "      <td>Female</td>\n",
              "      <td>40</td>\n",
              "      <td>69</td>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-282c6cb6-fbbf-4e45-8aa7-5cca8d238b64')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-282c6cb6-fbbf-4e45-8aa7-5cca8d238b64 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-282c6cb6-fbbf-4e45-8aa7-5cca8d238b64');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizando a quantidade de linhas e colunas\n",
        "grupo0.shape"
      ],
      "metadata": {
        "id": "x_RH6ZZZAmRr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "967a4e57-f53c-4c1e-d8be-3b7953681aee"
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando um dataframe apenas com o grupo 1\n",
        "grupo1 = df3.loc[df3.Grupo==1]\n",
        "grupo1 "
      ],
      "metadata": {
        "id": "PsllWbKwAxFX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "96953c0d-b7c0-4e51-f11c-39c931c679fc"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     CustomerID  genero  idade  rendimento  pontuacao  Grupo\n",
              "123         124    Male     39          69         91      1\n",
              "125         126  Female     31          70         77      1\n",
              "127         128    Male     40          71         95      1\n",
              "129         130    Male     38          71         75      1\n",
              "131         132    Male     39          71         75      1\n",
              "133         134  Female     31          72         71      1\n",
              "135         136  Female     29          73         88      1\n",
              "137         138    Male     32          73         73      1\n",
              "139         140  Female     35          74         72      1\n",
              "141         142    Male     32          75         93      1\n",
              "143         144  Female     32          76         87      1\n",
              "145         146    Male     28          77         97      1\n",
              "147         148  Female     32          77         74      1\n",
              "149         150    Male     34          78         90      1\n",
              "151         152    Male     39          78         88      1\n",
              "153         154  Female     38          78         76      1\n",
              "155         156  Female     27          78         89      1\n",
              "157         158  Female     30          78         78      1\n",
              "159         160  Female     30          78         73      1\n",
              "161         162  Female     29          79         83      1\n",
              "163         164  Female     31          81         93      1\n",
              "165         166  Female     36          85         75      1\n",
              "167         168  Female     33          86         95      1\n",
              "169         170    Male     32          87         63      1\n",
              "171         172    Male     28          87         75      1\n",
              "173         174    Male     36          87         92      1\n",
              "175         176  Female     30          88         86      1\n",
              "177         178    Male     27          88         69      1\n",
              "179         180    Male     35          93         90      1\n",
              "181         182  Female     32          97         86      1\n",
              "183         184  Female     29          98         88      1\n",
              "185         186    Male     30          99         97      1\n",
              "187         188    Male     28         101         68      1\n",
              "189         190  Female     36         103         85      1\n",
              "191         192  Female     32         103         69      1\n",
              "193         194  Female     38         113         91      1\n",
              "195         196  Female     35         120         79      1\n",
              "197         198    Male     32         126         74      1\n",
              "199         200    Male     30         137         83      1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1f2a7a4b-fcff-4916-97c4-5bee35e88ecf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>genero</th>\n",
              "      <th>idade</th>\n",
              "      <th>rendimento</th>\n",
              "      <th>pontuacao</th>\n",
              "      <th>Grupo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>124</td>\n",
              "      <td>Male</td>\n",
              "      <td>39</td>\n",
              "      <td>69</td>\n",
              "      <td>91</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>126</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>70</td>\n",
              "      <td>77</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>128</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>71</td>\n",
              "      <td>95</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>130</td>\n",
              "      <td>Male</td>\n",
              "      <td>38</td>\n",
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              "      <td>75</td>\n",
              "      <td>1</td>\n",
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              "      <th>131</th>\n",
              "      <td>132</td>\n",
              "      <td>Male</td>\n",
              "      <td>39</td>\n",
              "      <td>71</td>\n",
              "      <td>75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>134</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>72</td>\n",
              "      <td>71</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>136</td>\n",
              "      <td>Female</td>\n",
              "      <td>29</td>\n",
              "      <td>73</td>\n",
              "      <td>88</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>138</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>73</td>\n",
              "      <td>73</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>140</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>74</td>\n",
              "      <td>72</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>142</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>75</td>\n",
              "      <td>93</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>144</td>\n",
              "      <td>Female</td>\n",
              "      <td>32</td>\n",
              "      <td>76</td>\n",
              "      <td>87</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>146</td>\n",
              "      <td>Male</td>\n",
              "      <td>28</td>\n",
              "      <td>77</td>\n",
              "      <td>97</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>148</td>\n",
              "      <td>Female</td>\n",
              "      <td>32</td>\n",
              "      <td>77</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>150</td>\n",
              "      <td>Male</td>\n",
              "      <td>34</td>\n",
              "      <td>78</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>152</td>\n",
              "      <td>Male</td>\n",
              "      <td>39</td>\n",
              "      <td>78</td>\n",
              "      <td>88</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>154</td>\n",
              "      <td>Female</td>\n",
              "      <td>38</td>\n",
              "      <td>78</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>156</td>\n",
              "      <td>Female</td>\n",
              "      <td>27</td>\n",
              "      <td>78</td>\n",
              "      <td>89</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>158</td>\n",
              "      <td>Female</td>\n",
              "      <td>30</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>160</td>\n",
              "      <td>Female</td>\n",
              "      <td>30</td>\n",
              "      <td>78</td>\n",
              "      <td>73</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>162</td>\n",
              "      <td>Female</td>\n",
              "      <td>29</td>\n",
              "      <td>79</td>\n",
              "      <td>83</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>164</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>81</td>\n",
              "      <td>93</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>166</td>\n",
              "      <td>Female</td>\n",
              "      <td>36</td>\n",
              "      <td>85</td>\n",
              "      <td>75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>168</td>\n",
              "      <td>Female</td>\n",
              "      <td>33</td>\n",
              "      <td>86</td>\n",
              "      <td>95</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>170</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>87</td>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>172</td>\n",
              "      <td>Male</td>\n",
              "      <td>28</td>\n",
              "      <td>87</td>\n",
              "      <td>75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>174</td>\n",
              "      <td>Male</td>\n",
              "      <td>36</td>\n",
              "      <td>87</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>176</td>\n",
              "      <td>Female</td>\n",
              "      <td>30</td>\n",
              "      <td>88</td>\n",
              "      <td>86</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>178</td>\n",
              "      <td>Male</td>\n",
              "      <td>27</td>\n",
              "      <td>88</td>\n",
              "      <td>69</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>180</td>\n",
              "      <td>Male</td>\n",
              "      <td>35</td>\n",
              "      <td>93</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>182</td>\n",
              "      <td>Female</td>\n",
              "      <td>32</td>\n",
              "      <td>97</td>\n",
              "      <td>86</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>184</td>\n",
              "      <td>Female</td>\n",
              "      <td>29</td>\n",
              "      <td>98</td>\n",
              "      <td>88</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>186</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>99</td>\n",
              "      <td>97</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>188</td>\n",
              "      <td>Male</td>\n",
              "      <td>28</td>\n",
              "      <td>101</td>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>190</td>\n",
              "      <td>Female</td>\n",
              "      <td>36</td>\n",
              "      <td>103</td>\n",
              "      <td>85</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>192</td>\n",
              "      <td>Female</td>\n",
              "      <td>32</td>\n",
              "      <td>103</td>\n",
              "      <td>69</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>194</td>\n",
              "      <td>Female</td>\n",
              "      <td>38</td>\n",
              "      <td>113</td>\n",
              "      <td>91</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>120</td>\n",
              "      <td>79</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>126</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grupo1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nILZj4LuA5Ft",
        "outputId": "15310c67-1870-4455-e52c-06a5905e5b63"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K-means com todos os atributos\n",
        "\n",
        "Agora trabalharemos com todos os atributos do dataframe para criar o modelo. O dataframe utilizado será o df2_esc com os dados escalonados ( normalizados )"
      ],
      "metadata": {
        "id": "kunjfeei9iI9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2_esc"
      ],
      "metadata": {
        "id": "025G5GcZ-ETb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd95c543-4bfb-4bd7-f5e3-e681c30c9cad"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [-0.88640526, -0.27630176,  0.51299713,  0.84631002],\n",
              "       [-0.88640526,  1.30256542,  0.55116656, -1.75473454],\n",
              "       [ 1.12815215, -0.49160182,  0.55116656,  1.6615628 ],\n",
              "       [-0.88640526, -0.77866858,  0.58933599, -0.39597992],\n",
              "       [-0.88640526, -0.49160182,  0.58933599,  1.42863343],\n",
              "       [ 1.12815215, -0.99396865,  0.62750542, -1.48298362],\n",
              "       [ 1.12815215, -0.77866858,  0.62750542,  1.81684904],\n",
              "       [ 1.12815215,  0.65666521,  0.62750542, -0.55126616],\n",
              "       [-0.88640526, -0.49160182,  0.62750542,  0.92395314],\n",
              "       [-0.88640526, -0.34806844,  0.66567484, -1.09476801],\n",
              "       [ 1.12815215, -0.34806844,  0.66567484,  1.54509812],\n",
              "       [ 1.12815215,  0.29783176,  0.66567484, -1.28887582],\n",
              "       [ 1.12815215,  0.010765  ,  0.66567484,  1.46745499],\n",
              "       [-0.88640526,  0.36959845,  0.66567484, -1.17241113],\n",
              "       [-0.88640526, -0.06100169,  0.66567484,  1.00159627],\n",
              "       [-0.88640526,  0.58489852,  0.66567484, -1.32769738],\n",
              "       [-0.88640526, -0.85043527,  0.66567484,  1.50627656],\n",
              "       [ 1.12815215, -0.13276838,  0.66567484, -1.91002079],\n",
              "       [-0.88640526, -0.6351352 ,  0.66567484,  1.07923939],\n",
              "       [ 1.12815215, -0.34806844,  0.66567484, -1.91002079],\n",
              "       [-0.88640526, -0.6351352 ,  0.66567484,  0.88513158],\n",
              "       [-0.88640526,  1.23079873,  0.70384427, -0.59008772],\n",
              "       [-0.88640526, -0.70690189,  0.70384427,  1.27334719],\n",
              "       [ 1.12815215, -1.42456879,  0.78018313, -1.75473454],\n",
              "       [-0.88640526, -0.56336851,  0.78018313,  1.6615628 ],\n",
              "       [ 1.12815215,  0.80019859,  0.93286085, -0.93948177],\n",
              "       [-0.88640526, -0.20453507,  0.93286085,  0.96277471],\n",
              "       [ 1.12815215,  0.22606507,  0.97103028, -1.17241113],\n",
              "       [-0.88640526, -0.41983513,  0.97103028,  1.73920592],\n",
              "       [-0.88640526, -0.20453507,  1.00919971, -0.90066021],\n",
              "       [ 1.12815215, -0.49160182,  1.00919971,  0.49691598],\n",
              "       [ 1.12815215,  0.08253169,  1.00919971, -1.44416206],\n",
              "       [ 1.12815215, -0.77866858,  1.00919971,  0.96277471],\n",
              "       [ 1.12815215, -0.20453507,  1.00919971, -1.56062674],\n",
              "       [ 1.12815215, -0.20453507,  1.00919971,  1.62274124],\n",
              "       [-0.88640526,  0.94373197,  1.04736914, -1.44416206],\n",
              "       [-0.88640526, -0.6351352 ,  1.04736914,  1.38981187],\n",
              "       [ 1.12815215,  1.37433211,  1.04736914, -1.36651894],\n",
              "       [ 1.12815215, -0.85043527,  1.04736914,  0.72984534],\n",
              "       [ 1.12815215,  1.4460988 ,  1.23821628, -1.4053405 ],\n",
              "       [ 1.12815215, -0.27630176,  1.23821628,  1.54509812],\n",
              "       [-0.88640526, -0.13276838,  1.390894  , -0.7065524 ],\n",
              "       [-0.88640526, -0.49160182,  1.390894  ,  1.38981187],\n",
              "       [ 1.12815215,  0.51313183,  1.42906343, -1.36651894],\n",
              "       [-0.88640526, -0.70690189,  1.42906343,  1.46745499],\n",
              "       [-0.88640526,  0.15429838,  1.46723286, -0.43480148],\n",
              "       [ 1.12815215, -0.6351352 ,  1.46723286,  1.81684904],\n",
              "       [-0.88640526,  1.08726535,  1.54357172, -1.01712489],\n",
              "       [ 1.12815215, -0.77866858,  1.54357172,  0.69102378],\n",
              "       [-0.88640526,  0.15429838,  1.61991057, -1.28887582],\n",
              "       [-0.88640526, -0.20453507,  1.61991057,  1.35099031],\n",
              "       [-0.88640526, -0.34806844,  1.61991057, -1.05594645],\n",
              "       [-0.88640526, -0.49160182,  1.61991057,  0.72984534],\n",
              "       [ 1.12815215, -0.41983513,  2.00160487, -1.63826986],\n",
              "       [-0.88640526, -0.06100169,  2.00160487,  1.58391968],\n",
              "       [-0.88640526,  0.58489852,  2.26879087, -1.32769738],\n",
              "       [-0.88640526, -0.27630176,  2.26879087,  1.11806095],\n",
              "       [-0.88640526,  0.44136514,  2.49780745, -0.86183865],\n",
              "       [ 1.12815215, -0.49160182,  2.49780745,  0.92395314],\n",
              "       [ 1.12815215, -0.49160182,  2.91767117, -1.25005425],\n",
              "       [ 1.12815215, -0.6351352 ,  2.91767117,  1.27334719]])"
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando o quantidade de linhas e colunas. Observe que agora trabalharemos com 4 atributos.\n",
        "df2_esc.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ddrVZGG-Pe3",
        "outputId": "e0494484-6df4-4686-e0ca-353caae579c8"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Método Elbow - identifica o número de clusters adequado para o modelo"
      ],
      "metadata": {
        "id": "0QFBac4d-q8q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss = []\n",
        "for i in range (1, 11):\n",
        "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state= 5, max_iter=300)\n",
        "    kmeans.fit(df2_esc)\n",
        "    # inertia - Método para gerar o wcss\n",
        "    wcss.append(kmeans.inertia_)"
      ],
      "metadata": {
        "id": "Fx7b876x-r5V"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "aiaXDC8K-5dk"
      },
      "execution_count": 187,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.lineplot(range(1,11), wcss, marker=\"o\", color=\"red\")\n",
        "plt.title(\"Método Elbow\")\n",
        "plt.xlabel(\"Número de clusters\")\n",
        "plt.ylabel(\"WCSS\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "7Ez2igs0_C8a",
        "outputId": "643ba39e-413d-473c-c936-84db8858eab1"
      },
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'WCSS')"
            ]
          },
          "metadata": {},
          "execution_count": 188
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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g6ohYFXgROIYcAq+PiOOAKcBni2tvAQ4EJgHvFdeqWlZZBY49No+OQX4cMgTGjoUNNyy3NkmSOpiqBrKU0mNAfTOn9m3m2gS40ry1zJ3bGMYaTJli41hJkkpgp/6OqmtXqKtb9FhdHbz/fjn1SJLUgRnIOqpevWD06MZQVlcHl1+ee5T96EewYEGp5UmS1JFUew2Z2qpOnWDgwLxmbO7cPGK21lqwxRa5LcY//5l7lvXqVXalkiS1e46QdWSdOuUF/HV1+bFbN7jiirwZ+X33wQ475EdJklRVBjItKgK+8pU8ctatG+y9N5xzju0wJEmqIgOZmrfddjB+PBx+OAwfDoceCq+/XnZVkiS1SwYyfbi114brroNf/Qpuvx123BEefHDp75MkScvFQKYli8gbkd9/f15z9vGPw0UXQVquLUglSdISGMi0bAYNgkcegQMOgJNOgiOOgDlzyq5KkqR2wUCmZbfuuvCXv8C55+YeZjvumEOaJElaKQYyLZ8IOOUUuPdemDcPdtsNfvtbpzAlSVoJBjKtmN12g0cfzW0xvvENOOooeOedsquSJKkmGci04tZfH26+GX7603w35qBBMGFC2VVJklRzDGRaOZ06wemnw5135kX+u+yS98SUJEnLzECmlrHXXnkKc/BgOOYYOPZYeO+9squSJKkmGMjUcjbcEO64A77//TxKtssu8OyzZVclSVKbZyBTy+rcGX70I7j1Vpg5E+rrYeTIsquSJKlNM5CpOj71qTyFud128IUv5Dsx33+/7KokSWqTDGSqnr594a674Lvfzb3KdtsNXnih7KokSWpzDGSqrlVWgXPOgZtugsmTc3f/UaPKrkqSpDbFQKbWccgheZulLbeEz3wGTj45d/qXJEkGMrWifv3gn/+EE06ACy+EPfaAl18uuypJkkpnIFPrWnVVuOgi+NOf4KmnYIcdcrd/SZI6MAOZynHEEXkKc5NN4OCDYfhwmD+/7KokSSqFgUzl6d8f/vUvGDYsL/zfZx+YNq3sqiRJanUGMpVr9dXhd7+DP/4xj5jtsEPu9i9JUgdiIFPbcNRRMG4c9OqVm8qeeSYsWFB2VZIktQoDmdqOj3wEHnwQvvSlvP3Spz4Fr75adlWSJFWdgUxtS7dueWPy3/8e7r8ftt8e7rmn7KokSaqqqgayiJgcERMi4rGIGF8c6xERd0TE88XjusXxiIiLI2JSRDwRETtWsza1cccck0fL1l47L/b/2c9g4cKyq5IkqSqWGMgiYlBEbFjx+ksRMboITj2W8Tv2Tiltn1KqL14PB+5MKQ0A7ixeAxwADCh+hgG/WZ5fRO3QRz8K48fDZz8Lp5+e22O8/nrZVUmS1OKWNkL2O2AeQETsAZwNXAnMAUas4HcOAa4onl8BHFZx/MqUjQW6R8RGK/gdai/WWguuuQb+93/hzjvzXZgPPFB2VZIktailBbLOKaU3iuefA0aklG5MKX0f6L8Mn5+A2yPi4YgYVhzbIKU0o3g+E9igeN4HeKXivVOLY+roIuAb38g9y7p0yVsuXXABpFR2ZZIktYilBrKI6FI83xcYU3GuSzPXN/WxlNKO5OnIbxWjbP+RUkrk0LbMImJYRIyPiPGzZ89enreq1u20U+5VdvDB8O1vw+GHw1tvlV2VJEkrbWmBbCRwT0SMBv4PuA8gIvqTpy2XKKU0rXicBfwZ2Bl4tWEqsnicVVw+Ddi44u19i2NNP3NESqk+pVTfs2fPpZWg9qZ7dxg1Ko+Q/e1vsOOO8PDDZVclSdJKWWIgSyn9FDgFuJw82tUwmtUJOH5J742IbhGxVsNz4JPAk8BNwNDisqHA6OL5TcCXirstBwNzKqY2pUYRcNJJcN99ef/L3XbLa8ycwpQk1aglTjtGxBrAwymlD4rXWwIHAlNSSqOW8tkbAH+OiIbvuSaldFtEjAOuj4jjgCnAZ4vrbyk+exLwHnDMiv1K6jAGD4ZHH82NZL/1Lbj3XrjkknwjgCRJNSTSEkYVIuJe4LiU0vPFNOVDwNXA1sBDKaXTWqfM5tXX16fx48eXWYLagoUL4ec/hzPOyBuW/+lPuWWGJEltSEQ8XNEGbBFLW0O2bkrp+eL5UGBkSul48iL9g1uwRmnFdeoEw4fDmDHwzjuwyy65079TmJKkGrG0QFb5b7R9gDsAUkrzANumq23Zc888hbn77nDccfDjH8P06TBlCsycaad/SVKbtbTWFU9ExLnkux37A7cDRET3ahcmrZANNoC//z2PkG22WV7wP2UK1NXB6NEwcGAeUZMkqQ1Z2r+Zvgq8BvQDPplSeq84vjVwbhXrklZc585wyCF5lGzKlHxsyhQYMgRmzVryeyVJKsHSRsjWBP6aUprY5Pgc4LbqlCS1gLlzG8NYgylTciPZDTds/j2SJJVkaSNkvwTWa+Z4D+Cili9HaiFdu+Zpykp1dfD00/C5z8G0xXoOS5JUmqUFsv4ppXubHkwp3QfYV0BtV69eec1YQyhrWEP2xhtw002w1VZw/vnwwQfl1ilJEksPZEvqsLlKSxYitahOnfIC/rFjYfLk/DhwYF5XNnFi3qD8lFPy/pj33192tZKkDm5pgWxSRBzY9GBEHAC8WJ2SpBbSqVNeL1ZXlx8b7q7cbLO8D+af/5zXlH3sY3DsseBm9ZKkkiwtkJ0EXBgRl0fE8cXPFeT1YydWvzypSiLgsMPymrJTT4WrroItt4QRI+xXJklqdUsLZAcBRwP3A3XFzz3AR1NKz1W5Nqn6unWDs8+Gxx/P2y197Wuw667wyCNlVyZJ6kCWFsj6AhcCPwcGAfOAWcAaVa5Lal1bbw133ZVHyiZPhkGD4IQTYM6csiuTJHUASwxkKaX/TintBmwAnAa8ARwDPBkRT7VCfVLriYCjj4Znn4VvfAN+9as8jXn11e6LKUmqqmXdQ2Z1YG1gneJnOvBgtYqSStW9ew5j48bBJpvkkLbPPnm9mSRJVbDEQBYRIyLifuA6YFfgX8D/SynVp5SOaY0CpdLstBM88AD85jfw2GOw3XZw2mnw7rtlVyZJameWNkK2CdAVmEneYHwq8Fa1i5LajM6d4etfz9OYRx2VbwDYeuvcZFaSpBaytDVk+5MX8zdsJH4KMC4ibo+IH1a7OKnN6NUL/vAHuPdeWHvt3DLjkEPgpZfKrkyS1A4sdQ1Zyp4EbgFuJbfA2Bz7kKkj+vjHc0uMc8/Nd2VuvTX89Kd5M3NJklbQ0taQnRAR10bEy+T+YwcDzwCHkzcYlzqeVVbJ2y498wwcfDB873u5h9k//lF2ZZKkGrW0EbJ+wJ+AXVJKm6eUvphS+k1K6fGUku3M1bH17Qt/+hPceissWAD77Qef/zxMn152ZZKkGrO0NWTfTindmFKa0VoFSTVn//3hySfhrLPy/phbbQUXXQTz55ddmSSpRixrHzJJS7LaanDmmTmY7b47nHQS1NfnthmSJC2FgUxqSf37wy23wI03wuuvw267wVe/mp9LkvQhDGRSS4uAww/Pnf2/8x24/PK8BdOll8JCl15KkhZnIJOqZc014ec/h0cfhY98JI+U7b577vovSVIFA5lUbdtumxvKXn45vPBC3pLppJPg7bfLrkyS1EYYyKTWEAFDh+YtmL72Nbj44nw35rXXQkplVydJKpmBTGpN664L//u/8OCD0Lt37lu23345qEmSOiwDmVSGQYNyKPv1r2H8eBg4MHf8f++9siuTJJWg6oEsIjpHxKMR8bfi9aYR8WBETIqI6yJi1eJ41+L1pOJ8v2rXJpWqc2f45jfz6NiRR+Y9MbfZBv7617IrkyS1stYYITsReLri9TnABSml/sCbwHHF8eOAN4vjFxTXSe3fBhvAlVfC3XfDGmvAoYfCkCEweXLZlUmSWklVA1lE9AUOAi4tXgewD3BDcckVwGHF8yHFa4rz+xbXSx3DnnvmFhnnnJM3Kt96a/jZz2DevLIrkyRVWbVHyC4Evgs0dMNcD3grpdSwyd9UoE/xvA/wCkBxfk5xvdRxrLoqfPe7uans/vvD6afDdtvBmDFlVyZJqqKqBbKIOBiYlVJ6uIU/d1hEjI+I8bNnz27Jj5bajk02gVGj4Oab8wjZvvvCUUfBjBllVyZJqoJqjpDtDhwaEZOBa8lTlRcB3SOiS3FNX2Ba8XwasDFAcX4dYLENAFNKI1JK9Sml+p49e1axfKkNOPDAvGH5D34AN9yQe5f98pcwf/7S3ytJqhlVC2QppdNSSn1TSv2AI4ExKaWjgLuAI4rLhgKji+c3Fa8pzo9JyY6ZEquvDj/8YQ5mu+wCJ5wAO++c22YsXAgzZ8KUKfnRvTIlqSaV0YfsVODbETGJvEbssuL4ZcB6xfFvA8NLqE1quwYMgL//Ha6/Hl59FU48ER54AAYPhn798uOECYYySapBUcuDUPX19Wn8+PFllyG1vnfegYkTc/+yKVMaj9fVwdixsOGG5dUmSWpWRDycUqpv7pyd+qVatNZasNFGi4YxyK9nz4bXXiunLknSCjGQSbWqa9c8Ilaprg4mTcr7ZH760/CXv9jHTJJqgIFMqlW9esHo0Y2hrK4uv95mGzj++Ly+7NOfzuHs+OPznpk1vERBktoz15BJtWzhQpg1C+bOzSNmvXpBp+K/s+bPh9tvhyuuyEFt7tzc/f9LX4Kjj4Y+fZb82ZKkFuUaMqm96tQpL+Cvq8uPnSr+X7pLl9zH7LrrckPZ3/0OuneH4cNz49lPfQquuQbee6+8+iVJgIFM6hjWXReGDYP774fnnstbMj3zTO7+v+GGcNxxcO+9tsyQpJIYyKSOZsAA+PGP4aWX4K674DOfyb3N9twT+veHs86CF14ou0pJ6lAMZFJH1akT7LUX/OEPucv/VVfB5pvDj36Ug9nHPw6XXgpz5pRdqSS1ewYySdCtW17of8cduZfZ//xP7mf21a/mKc3Pfx5uuw0WLCi7Uklqlwxkkha18cZw2mnw9NN5v8xjj81bNh1wQD733e/mfTUlSS3GQCapeRF5E/Nf/zrfpXnjjTBoEFxwAQwcCDvtBBdfnEfSJEkrxUAmaem6doXDD8/9zKZNgwsvzE1mTzwxN54dMgRGjcq9ziRJy81AJmn59OqVg9gjj8ATT8BJJ8FDD+W7NXv3hv/6r/y6hptOS1JrM5BJWnEDB8IvfgGvvAK33gqf/CRcdhnsskveFeDss2Hq1LKrlKQ2z0AmaeV16QL77w8jR+YWGiNGwHrr5ZsDNtkE9tsP/vhHePfdsiuVpDbJQCapZa2zTm6X8c9/wqRJ8P3v58cvfjG30Dj2WLj7bncFkKQKBjJJ1bP55vDDH+bO//fcA5/9LNxwA+y9dz73gx/ksCZJHZyBTFL1deoEe+yR15fNnAlXXw1bbAE/+Uneymn33fM051tvlV2pJJXCQCapda2xBnzhC7nZ7Cuv5IX/b74JX/tantI88ki45RaYPz9Pa86cmXcPmDnTaU5J7ZaBTFJ5+vSBU0+FiRNh3Li89uwf/4CDDoJDD4WxY2HwYOjXLz9OmGAok9QuRarhXkH19fVp/PjxZZchqSXNmwc33ww9esDQoXl0rEFdXQ5pG25YXn2StIIi4uGUUn1z5xwhk9S2rLoqfPrTeVSsMoxBfj11ar5Ls4b/Y1KSmjKQSWqbunbNI2KV6upg+vR8l+YWW8DPfpZfS1KNM5BJapt69cp7ZzaEsrq6/PoTn4Arr8zrz04/PTeePfTQfO6DD8qtWZJWkGvIJLVdCxfCrFl50/KuXXNI61Tx35HPPw+//z1cfnm+C3ODDfK6s+OOyyNoktSGLGkNmYFMUu2bPz/vpXnppfmGgAUL4OMfz8HsiCOgW7eyK5QkF/VLaue6dIFDDsnTlg29zWbOhC9/GXr3hq9/HcaP90YASW2WgUxS+7LRRrm32bPP5u2aDjssrzkbNAi23x4uvhjeeKPsKiVpEQYySe1TRN6u6YorYMYM+M1vckuNE0/Moe3zn89NaG00K6kNqFogi4jVIuKhiHg8IiZGxA+L45tGxIMRMSkirouIVYvjXYvXk4rz/apVm6QOZp118rTluHHw2GN5m6a//x322y9vcv7jH+epTkkqSTVHyOYC+6SUtgO2B/aPiMHAOcAFKb3PIY4AABJUSURBVKX+wJvAccX1xwFvFscvKK6TpJa13XZ52nL6dLjmmhzIfvCD3FbjgAPgxhvzbgGS1IqqFshS9u/i5SrFTwL2AW4ojl8BHFY8H1K8pji/b0REteqT1MGttlrjtOULL8AZZ+S9Mo84Avr2hVNOgaeeKrtKSR1EVdeQRUTniHgMmAXcAbwAvJVSml9cMhXoUzzvA7wCUJyfA6xXzfokCYDNNsvTllOmwC235LVnF18M22wDu+0Gl10G//730j9HklZQVQNZSmlBSml7oC+wM7DVyn5mRAyLiPERMX727NkrXaMk/Ufnznna8oYbYNo0OPdceOst+MpX8obmX/kKPPCA7TMktbhWucsypfQWcBewK9A9IroUp/oC04rn04CNAYrz6wCvN/NZI1JK9Sml+p49e1a9dkkdVK9eedpy4kS4/3743Ofg2mvziNk228D554P/USiphVTzLsueEdG9eL46sB/wNDmYHVFcNhQYXTy/qXhNcX5MquVtBCS1DxGN05YzZsAll+S7Nk85Je+necQReZeABQvKrlRSDava1kkR8VHyIv3O5OB3fUrpRxGxGXAt0AN4FDg6pTQ3IlYDrgJ2AN4Ajkwpvbik73DrJEmlmTgx76N55ZXw2mv5RoBjjsk/m25adnWS2iD3spSkapk3D266Ke+jefvteX3ZJz6R99E87LB8N6ck4V6WklQ9q66apy1vuw0mT4Yf/hCefz631OjTJ+8M8MQTZVcpqY0zkElSS9lkk9xk9sUX82jZfvvBb3+bm9EOGgS/+x3MmVN2lZLaIAOZJLW0Tp1yGLv22rwjwIUXwty5efumjTaCoUPhvvvyjQAzZ+b+ZzNnuq+m1IEZyCSpmtZbL09bPv44PPQQfPGL8Oc/w3e+k0PZ4MHQr19+nDDBUCZ1UC7ql6TW9u67eVrzkEPy6FiDurq8U0Dv3tC9e3n1SaoKF/VLUlvSrRusvfaiYQzy69mz86jazjvD8OF5Ldq775ZTp6RWYyCTpDJ07ZpHxCrV1eU1Zt/7Xj5/3nnwqU/Buuvm/TXPOgvuvTevR5PUrjhlKUllWLgwrxkbMiSPjNXVwejRMHBgvikA8sjYP/8JY8bkn0ceye9bfXX42Mdgn33yz447QpcuS/4+SaWzMawktUULF8KsWXnEq2vXvH9mpyVMXLz5Zh4hGzMG7rorBzrI05977tkY0LbddsmfI6kUBjJJao9mzYK7724cQXv++Xx8/fVh770bA9qAAXlPTkmlMpBJUkfwyit55GzMGLjzTpg6NR/v06cxnO299+Jr1yS1CgOZJHU0KcELLzSOno0Zk+/gBNh888YRtL33hg03LLdWqYMwkElSR5cSTJzYGM7uvrtxG6ett24cQdtzT+jRo9RSpfbKQCZJWtSCBfDoo40B7b774L338lqzHXZoDGgf/zisuWbZ1UrtgoFMkrRk8+blrZ0aAtoDD+RjXbrkJrUNAW3XXWG11cquVqpJBjJJ0vJ57z34178abxIYNy6PqnXtCrvv3hjQ6uthlVXKrlaqCQYySdLKefvtPK3ZMIL22GP5+Jpr5l0EGgLadtst2gNteXutSe3YkgKZrZ0lSUu39tpw0EH5B+C11+CeexoD2i235OM9esBee+VwdsAB8M47S96NQBLgCJkkqSVMn75oD7QpU2DUKDj55EU3Ua+rg7FjbbWhDskpS0lS63rppbwObdttFz83bly+w3OvvaB/f3cRUIfhlKUkqXVtuinMnJlHxJqOkM2cCcOG5de9e+dg1vBjQFMHZSCTJFVHr155zVjTNWTbbgvPPJOb0zbsxXnNNfk9BjR1UE5ZSpKqZ1nuskwJnnuuMaDdfXceRQMDmtoV15BJkmqHAU3tlIFMklS7DGhqJwxkkqT2w4CmGmUgkyS1XwY01QgDmSSp4zCgqY0ykEmSOi4DmtqIUgJZRGwMXAlsACRgRErpoojoAVwH9AMmA59NKb0ZEQFcBBwIvAd8OaX0yJK+w0AmSVpuBjSVpKxAthGwUUrpkYhYC3gYOAz4MvBGSunsiBgOrJtSOjUiDgSOJweyXYCLUkq7LOk7DGSSpJW2MgFtWfqsSYU2MWUZEaOBXxU/e6WUZhSh7e6U0pYR8bvi+cji+mcbrvuwzzSQSZJa3LIGtE9+Et56a/GdCAYONJSpWUsKZK3yfzER0Q/YAXgQ2KAiZM0kT2kC9AFeqXjb1OKYJEmtJwK23BK+9jUYORKmT89bPf32t7DHHnmrp2HD4JFHGsMY5MchQ/KImbScqr6XZUSsCdwInJRSejsqhnpTSikilmuILiKGAcMANtlkk5YsVZKkxTUEtIaQ1jCC9sEHi26cDvn1pElw9NF5pGzbbfPPNtvAmmuWU79qQlUDWUSsQg5jV6eURhWHX42IjSqmLBv+U2IasHHF2/sWxxaRUhoBjIA8ZVm14iVJak5DQJs5M09TVoayurr8+PbbMGIEvPde47lNN20MaA0/W20Fq67auvWrTapaICvumrwMeDqldH7FqZuAocDZxePoiuP/FRHXkhf1z1nS+jFJkkrVq1deM9bcGrKHHsoL/l96CZ58Mv9MmJAfb70V5s/Pn9GlC2yxRWNAaxhV23RT6Ny53N9Praqad1l+DLgPmAAsLA6fTl5Hdj2wCTCF3PbijSLA/QrYn9z24piU0hJX7LuoX5JUqhW5y3LevDzl2RDQGn5efLHxmtVXh623XnTac9tt800FtuGoWW3iLstqMJBJktqNf/8bnnpq0ZA2YULjHZ4A6667aEAbODCvT+vRo7y6tcyWFMiqvqhfkiQtgzXXhJ13zj+VXnsNJk5cdNrzmmtgzpzGa3r3XnTKc9tt8wjbGmu07u+gFWYgkySpLVt/fdhzz/zTICWYNm3xac9f/xrefz9fEwGbb774iNqAAbDKKuX8LvpQBjJJkmpNBPTtm38OOKDx+IIF8MILi097/vWv+RzkMLbVVovfSFBXt/j6N3ciaDWuIZMkqb17/3149tlFpz2ffHLRlh3duuX1aA0Bbdddc0uOT3/anQhaiIv6JUnS4t5+u3F9WuWI2uzZMGoUnHzy4n3Wbr01t+TYdFOnPpeTi/olSdLi1l47j4Ttuuuix2fNgjffbH4nglmz8l6eXbrAZpvlPmpbbrno44Yb2p5jORnIJEnSonr1yuvHmtuJoE8fuPzy3Evt2Wfz4z/+0XgzAcBaa+Vg1jSsDRiQz2kxBjJJkrS4D9uJYLPNoH//Ra9duBBeeWXRkPbcc/DAA3Dttfmu0AYbbbT4iNoWW3T4KVDXkEmSpOa1xF2W77+fN1xvCGkNge3ZZ+H11xuva5gCrQxp7WwK1DVkkiRp+XXqlMPQylhttcYWG029/jo8//yiIe255+COO5qfAm0a1trRFKiBTJIklWO99fLP4MGLHv+wKdB//QtGjlx0CrR37+bXq/Xrt2xToG2k15qBTJIktS2dOuU1a3V1sN9+i557//3c/LbpqNqoUXmbqQZNp0ArR9capkAXLsxtPpqukyuh15qBTJIk1Y7VVssNbLfZZvFzb7yx+Kjas89++BToBRfAF7/YeCfplCk5nI0du/JTtcvJQCZJktqHHj3y9OeSpkArA1unTs33Wps7t/VqLhjIJElS+/ZhU6AzZzbfa61r19YvsdW/UZIkqS1o6LVWV5dfN6wh69Wr1UtxhEySJHVMnTrlBfxjx3qXpSRJUmlaotdaS5RRdgGSJEkdnYFMkiSpZAYySZKkkhnIJEmSSmYgkyRJKpmBTJIkqWQGMkmSpJIZyCRJkkoWKaWya1hhETEbmLLUC7U06wOvlV2EVop/w9rm36/2+Tesfa3xN6xLKfVs7kRNBzK1jIgYn1KqL7sOrTj/hrXNv1/t829Y+8r+GzplKUmSVDIDmSRJUskMZAIYUXYBWmn+DWubf7/a59+w9pX6N3QNmSRJUskcIZMkSSqZgayDioiNI+KuiHgqIiZGxIll16QVExGdI+LRiPhb2bVo+UVE94i4ISKeiYinI2LXsmvSsouIk4t/hj4ZESMjYrWya9LSRcTvI2JWRDxZcaxHRNwREc8Xj+u2Zk0Gso5rPnBKSmlrYDDwrYjYuuSatGJOBJ4uuwitsIuA21JKWwHb4d+yZkREH+AEoD6ltC3QGTiy3Kq0jC4H9m9ybDhwZ0ppAHBn8brVGMg6qJTSjJTSI8Xzd8j/EuhTblVaXhHRFzgIuLTsWrT8ImIdYA/gMoCU0ryU0lvlVqXl1AVYPSK6AGsA00uuR8sgpXQv8EaTw0OAK4rnVwCHtWZNBjIREf2AHYAHy61EK+BC4LvAwrIL0QrZFJgN/KGYdr40IrqVXZSWTUppGnAu8DIwA5iTUrq93Kq0EjZIKc0ons8ENmjNLzeQdXARsSZwI3BSSuntsuvRsouIg4FZKaWHy65FK6wLsCPwm5TSDsC7tPI0iVZcscZoCDlY9wa6RcTR5VallpByC4pWbUNhIOvAImIVchi7OqU0qux6tNx2Bw6NiMnAtcA+EfHHckvScpoKTE0pNYxO30AOaKoNnwBeSinNTil9AIwCdiu5Jq24VyNiI4DicVZrfrmBrIOKiCCvW3k6pXR+2fVo+aWUTksp9U0p9SMvJB6TUvK/zmtISmkm8EpEbFkc2hd4qsSStHxeBgZHxBrFP1P3xZsyatlNwNDi+VBgdGt+uYGs49od+CJ5VOWx4ufAsouSOqDjgasj4glge+B/Sq5Hy6gY2bwBeASYQP53qh37a0BEjAQeALaMiKkRcRxwNrBfRDxPHv08u1VrslO/JElSuRwhkyRJKpmBTJIkqWQGMkmSpJIZyCRJkkpmIJNUmoj4VtGcWJI6NAOZpBYXESkizqt4/d8RcVaTa44G1ksp/bu16/swETE5ItZfyc/YKyL+toLvPSki1liZ75dUmwxkkqphLnD4UsJNZ+DH1fjyYqPnWnQSeYPqZRYRnatUi6RWZCCTVA3zyQ0yT256IiIuj4gjUkpXpJRSRPy7OL5XRNwTEaMj4sWIODsijoqIhyJiQkRsXlzXMyJujIhxxc/uxfGzIuKqiLgfuCoi+kXEmIh4IiLujIhNmqllvYi4PSImRsSlQFScO7r47sci4nfNBZ+IGBQR/4qIx4tr12py/qyI+O+K108WdXWLiJuL9z0ZEZ+LiBPI+yHeFRF3Fdd/MiIeiIhHIuJPDdO7xUjeORHxCPD/IuKEiHiq+F2vXe6/lqTSGcgkVcuvgaMiYp3leM92wNeBj5B3ktgipbQzcCm5oz3ARcAFKaVBwGeKcw22Bj6RUvo88EvgipTSR4GrgYub+b4zgX+mlLYB/gxsAhARHwE+B+yeUtoeWAAcVfnGiFgVuA44MaW0Hbmz9/8t4++5PzA9pbRdSmlb4LaU0sXAdGDvlNLexeji94rfZ0dgPPDtis94PaW0Y0rpWvKG5DsUv+vXl7EGSW1IrQ7rS2rjUkpvR8SVwAkse1AZl1KaARARLwC3F8cnAHsXzz8BbJ23DgRg7YobA25KKTV8167A4cXzq4CfN/N9ezRck1K6OSLeLI7vC+wEjCu+Z3UW32h4S2BGSmlcw+9b1L0sv+cE4LyIOAf4W0rpvmauGUwOmPcXn7kqeauXBtdVPH+CvP3SX4C/LEsBktoWA5mkarqQvM/fHyqOzacYnY+ITuSg0WBuxfOFFa8X0vjPq07A4JTS+5VfVISWd1uo7iCPrp22kp/zn9+1sBpASum5iNgROBD4SUTcmVL6UTM13FGM9jWn8nc9iBwuDwHOiIiBKaX5K1m7pFbklKWkqkkpvQFcDxxXcXgyefQJ4FBgleX82NtpnL4kIrb/kOv+BRxZPD8KaG4U6l7gC8XnHACsWxy/EzgiInoV53pERF2T9z4LbBQRg4pr1mrmZoLJwI7F+R2BTYvnvYH3Ukp/BH7RcA3wDtCwDm0ssHtE9C/e0y0itmj6CxShduOU0l3AqcA6gK1EpBpjIJNUbecBlXdbXgLsGRGPk6cVl3dU6wSgvljA/hQfvmbqeOCYiHiCvB7txGau+SGwR0RMJE9dvgyQUnqKvH7r9uL9dwAbVb4xpTSPvM7sl8XvcgfFCFiFG4Eexef/F/BccXwg8FBEPEZex/aT4vgI4LaIuCulNBv4MjCyqOEBYKtmfofOwB8jYgLwKHBxSumtD/nfRFIbFSmlsmuQJEnq0BwhkyRJKpmBTJIkqWQGMkmSpJIZyCRJkkpmIJMkSSqZgUySJKlkBjJJkqSSGcgkSZJK9v8BfzYH+LszCO8AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Neste gráfico podemos ver que o ponto de inflexão fica entre 5 e 6. Lembrando que o ponto de inflexão define a melhor quantidade de clusters"
      ],
      "metadata": {
        "id": "GbC2akox_Y4S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Agrupamento"
      ],
      "metadata": {
        "id": "FskC-wjP_sx8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters = 6, init = \"k-means++\", random_state = 5, max_iter = 300)"
      ],
      "metadata": {
        "id": "ywCPGwTa_v_B"
      },
      "execution_count": 189,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans2 = kmeans.fit(df2_esc)"
      ],
      "metadata": {
        "id": "ydqlp_EC_8wh"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Coordenadas dos centróides\n",
        "centroides = kmeans2.cluster_centers_\n",
        "centroides"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hTLsF1m5ACGp",
        "outputId": "cb0ee6b6-903b-475f-8612-3651f8045b68"
      },
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.12815215,  1.22385356, -0.4498575 , -0.44231533],\n",
              "       [-0.71852548, -0.46169904,  1.09030974,  1.17305816],\n",
              "       [-0.88640526,  0.86299444, -0.43455895, -0.40762639],\n",
              "       [-0.88640526, -0.92608124, -0.70017176,  0.28182355],\n",
              "       [ 0.25517727,  0.0729628 ,  1.14279271, -1.32381522],\n",
              "       [ 1.12815215, -0.77111419, -0.08465578,  0.8197479 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Inversão do escalonamento\n",
        "escala.inverse_transform(kmeans2.cluster_centers_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ksJew6PCAJSh",
        "outputId": "a09705a0-810e-4c72-f60b-7a7ced62f25b"
      },
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.00000000e+00, 5.59032258e+01, 4.87741935e+01, 3.88064516e+01],\n",
              "       [8.33333333e-02, 3.24166667e+01, 8.91250000e+01, 8.04166667e+01],\n",
              "       [5.55111512e-17, 5.08750000e+01, 4.91750000e+01, 3.97000000e+01],\n",
              "       [1.66533454e-16, 2.59459459e+01, 4.22162162e+01, 5.74594595e+01],\n",
              "       [5.66666667e-01, 3.98666667e+01, 9.05000000e+01, 1.61000000e+01],\n",
              "       [1.00000000e+00, 2.81052632e+01, 5.83421053e+01, 7.13157895e+01]])"
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classificação dos dados\n",
        "classificacao2 = kmeans2.labels_\n",
        "classificacao2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOYLpfUKAFyL",
        "outputId": "d32c3033-6aff-412b-8dde-eac060d879ac"
      },
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 5, 3, 3, 3, 3, 2, 3, 0, 3, 0, 3, 2, 3, 0, 5, 3, 5, 0, 3, 0, 5,\n",
              "       2, 5, 2, 5, 2, 5, 2, 3, 0, 3, 0, 5, 2, 3, 2, 3, 2, 3, 2, 5, 0, 3,\n",
              "       2, 3, 2, 3, 3, 3, 2, 5, 3, 0, 2, 0, 2, 0, 3, 0, 0, 5, 2, 2, 0, 5,\n",
              "       2, 2, 5, 3, 0, 2, 2, 2, 0, 5, 2, 0, 3, 2, 0, 5, 0, 2, 3, 0, 2, 3,\n",
              "       3, 2, 2, 5, 0, 2, 3, 5, 2, 3, 0, 5, 3, 2, 0, 5, 0, 3, 2, 0, 0, 0,\n",
              "       0, 3, 2, 5, 3, 3, 2, 2, 2, 2, 5, 2, 1, 5, 3, 1, 0, 5, 0, 5, 4, 5,\n",
              "       3, 1, 4, 1, 4, 5, 4, 1, 2, 5, 3, 1, 4, 5, 0, 1, 4, 5, 4, 5, 4, 1,\n",
              "       4, 1, 4, 1, 4, 1, 2, 1, 4, 1, 4, 1, 4, 1, 4, 5, 4, 5, 4, 5, 4, 1,\n",
              "       4, 5, 4, 5, 4, 1, 4, 1, 4, 5, 4, 5, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1,\n",
              "       4, 1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizando a classificação do 4º elemento \n",
        "classificacao2[4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ruem56N9AO86",
        "outputId": "40929486-dd2e-47fa-ceb1-d98753d40dee"
      },
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizando o agrupamento por uma lista"
      ],
      "metadata": {
        "id": "vhIKAHKEDj_C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "agrupamento2 = pd.DataFrame(classificacao2, columns = [\"Grupo\"])\n",
        "agrupamento2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6KfmDWEMDk_a",
        "outputId": "4e2bd5e9-5461-447d-99a8-7d972ae99fec"
      },
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Grupo\n",
              "0        5\n",
              "1        5\n",
              "2        3\n",
              "3        3\n",
              "4        3\n",
              "..     ...\n",
              "195      1\n",
              "196      4\n",
              "197      1\n",
              "198      4\n",
              "199      1\n",
              "\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# Juntando o atributo Grupo ao dataframe original. Será criado um novo dataframe para manter a integridade dos dados\n",
        "df4 = pd.concat([df,agrupamento2],axis=1)\n",
        "df4"
      ],
      "metadata": {
        "colab": {
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          "height": 424
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        "id": "Q_oS2LYSDt6H",
        "outputId": "5766ecec-48c3-4782-efe0-ef9df5018ff9"
      },
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     CustomerID  genero  idade  rendimento  pontuacao  Grupo\n",
              "0             1    Male     19          15         39      5\n",
              "1             2    Male     21          15         81      5\n",
              "2             3  Female     20          16          6      3\n",
              "3             4  Female     23          16         77      3\n",
              "4             5  Female     31          17         40      3\n",
              "..          ...     ...    ...         ...        ...    ...\n",
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              "198         199    Male     32         137         18      4\n",
              "199         200    Male     30         137         83      1\n",
              "\n",
              "[200 rows x 6 columns]"
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              "<p>200 rows × 6 columns</p>\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-4d6c3af7-62dc-4802-b9e1-918d71b7cdb4 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-4d6c3af7-62dc-4802-b9e1-918d71b7cdb4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Acessando os dados através do índice\n",
        "df4.iloc[125,:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXlMHozhER6r",
        "outputId": "1dd9a7ae-a6b7-48d9-8a6e-6710a092e0d4"
      },
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID       126\n",
              "genero        Female\n",
              "idade             31\n",
              "rendimento        70\n",
              "pontuacao         77\n",
              "Grupo              1\n",
              "Name: 125, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K-means com PCA ( Principal Component Analysis ) ou Análise dos Componentes Principais"
      ],
      "metadata": {
        "id": "fpdmvNd6rPBt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando o dataframe df2_esc\n",
        "df2_esc"
      ],
      "metadata": {
        "id": "qBj9MbsTr2nF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73747b6f-be23-4383-e09d-2722b579bb94"
      },
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "execute_result",
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              "       [-0.88640526,  0.80019859, -0.67025518, -0.20187212],\n",
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              "       [ 1.12815215,  1.01549866, -0.55574689, -0.16305055],\n",
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              "       [ 1.12815215,  1.73316556, -0.47940803,  0.03105725],\n",
              "       [ 1.12815215, -1.49633548, -0.47940803,  0.34162973],\n",
              "       [-0.88640526,  0.29783176, -0.47940803, -0.00776431],\n",
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              "       [ 1.12815215, -1.42456879, -0.47940803,  0.34162973],\n",
              "       [-0.88640526, -0.49160182, -0.47940803, -0.12422899],\n",
              "       [ 1.12815215,  2.23553238, -0.4412386 ,  0.18634349],\n",
              "       [-0.88640526,  0.58489852, -0.4412386 , -0.3183368 ],\n",
              "       [-0.88640526,  1.51786549, -0.40306917, -0.04658587],\n",
              "       [-0.88640526,  1.51786549, -0.40306917,  0.22516505],\n",
              "       [ 1.12815215,  1.4460988 , -0.25039146, -0.12422899],\n",
              "       [ 1.12815215, -0.92220196, -0.25039146,  0.14752193],\n",
              "       [-0.88640526,  0.44136514, -0.25039146,  0.10870037],\n",
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              "       [-0.88640526, -1.13750203, -0.25039146,  0.06987881],\n",
              "       [-0.88640526,  0.7284319 , -0.25039146, -0.3183368 ],\n",
              "       [ 1.12815215,  1.30256542, -0.25039146,  0.03105725],\n",
              "       [ 1.12815215, -0.06100169, -0.25039146,  0.18634349],\n",
              "       [ 1.12815215,  2.02023231, -0.25039146, -0.35715836],\n",
              "       [-0.88640526,  0.51313183, -0.25039146, -0.24069368],\n",
              "       [-0.88640526, -1.28103541, -0.25039146,  0.26398661],\n",
              "       [ 1.12815215,  0.65666521, -0.25039146, -0.16305055],\n",
              "       [-0.88640526,  1.15903204, -0.13588317,  0.30280817],\n",
              "       [-0.88640526, -1.20926872, -0.13588317,  0.18634349],\n",
              "       [-0.88640526, -0.34806844, -0.09771374,  0.38045129],\n",
              "       [-0.88640526,  0.80019859, -0.09771374, -0.16305055],\n",
              "       [-0.88640526,  2.091999  , -0.05954431,  0.18634349],\n",
              "       [ 1.12815215, -1.49633548, -0.05954431, -0.35715836],\n",
              "       [ 1.12815215,  0.65666521, -0.02137488, -0.04658587],\n",
              "       [-0.88640526,  0.08253169, -0.02137488, -0.39597992],\n",
              "       [-0.88640526, -0.49160182, -0.02137488, -0.3183368 ],\n",
              "       [ 1.12815215, -1.06573534, -0.02137488,  0.06987881],\n",
              "       [-0.88640526,  0.58489852, -0.02137488, -0.12422899],\n",
              "       [-0.88640526, -0.85043527, -0.02137488, -0.00776431],\n",
              "       [ 1.12815215,  0.65666521,  0.01679455, -0.3183368 ],\n",
              "       [ 1.12815215, -1.3528021 ,  0.01679455, -0.04658587],\n",
              "       [-0.88640526, -1.13750203,  0.05496398, -0.35715836],\n",
              "       [-0.88640526,  0.7284319 ,  0.05496398, -0.08540743],\n",
              "       [ 1.12815215,  2.02023231,  0.05496398,  0.34162973],\n",
              "       [ 1.12815215, -0.92220196,  0.05496398,  0.18634349],\n",
              "       [ 1.12815215,  0.7284319 ,  0.05496398,  0.22516505],\n",
              "       [-0.88640526, -1.28103541,  0.05496398, -0.3183368 ],\n",
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              "       [ 1.12815215,  1.94846562,  0.09313341, -0.08540743],\n",
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              "       [-0.88640526,  1.73316556,  0.16947227, -0.27951524],\n",
              "       [-0.88640526,  0.7284319 ,  0.16947227,  0.34162973],\n",
              "       [-0.88640526,  0.87196528,  0.24581112, -0.27951524],\n",
              "       [-0.88640526,  0.80019859,  0.24581112,  0.26398661],\n",
              "       [ 1.12815215, -0.85043527,  0.24581112,  0.22516505],\n",
              "       [-0.88640526, -0.06100169,  0.24581112, -0.39597992],\n",
              "       [-0.88640526,  0.08253169,  0.32214998,  0.30280817],\n",
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              "       [-0.88640526, -1.13750203,  0.36031941, -0.82301709],\n",
              "       [-0.88640526, -0.56336851,  0.36031941,  1.04041783],\n",
              "       [ 1.12815215,  0.29783176,  0.39848884, -0.59008772],\n",
              "       [ 1.12815215,  0.08253169,  0.39848884,  1.73920592],\n",
              "       [ 1.12815215,  1.4460988 ,  0.39848884, -1.52180518],\n",
              "       [ 1.12815215, -0.06100169,  0.39848884,  0.96277471],\n",
              "       [ 1.12815215,  0.58489852,  0.39848884, -1.5994483 ],\n",
              "       [ 1.12815215,  0.010765  ,  0.39848884,  0.96277471],\n",
              "       [-0.88640526, -0.99396865,  0.43665827, -0.62890928],\n",
              "       [-0.88640526, -0.56336851,  0.43665827,  0.80748846],\n",
              "       [ 1.12815215, -1.3528021 ,  0.4748277 , -1.75473454],\n",
              "       [-0.88640526, -0.70690189,  0.4748277 ,  1.46745499],\n",
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              "       [ 1.12815215, -0.49160182,  0.4748277 ,  0.88513158],\n",
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              "       [-0.88640526, -0.27630176,  0.51299713,  0.84631002],\n",
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              "       [ 1.12815215, -0.49160182,  0.55116656,  1.6615628 ],\n",
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              "       [ 1.12815215, -0.99396865,  0.62750542, -1.48298362],\n",
              "       [ 1.12815215, -0.77866858,  0.62750542,  1.81684904],\n",
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              "       [-0.88640526, -0.49160182,  0.62750542,  0.92395314],\n",
              "       [-0.88640526, -0.34806844,  0.66567484, -1.09476801],\n",
              "       [ 1.12815215, -0.34806844,  0.66567484,  1.54509812],\n",
              "       [ 1.12815215,  0.29783176,  0.66567484, -1.28887582],\n",
              "       [ 1.12815215,  0.010765  ,  0.66567484,  1.46745499],\n",
              "       [-0.88640526,  0.36959845,  0.66567484, -1.17241113],\n",
              "       [-0.88640526, -0.06100169,  0.66567484,  1.00159627],\n",
              "       [-0.88640526,  0.58489852,  0.66567484, -1.32769738],\n",
              "       [-0.88640526, -0.85043527,  0.66567484,  1.50627656],\n",
              "       [ 1.12815215, -0.13276838,  0.66567484, -1.91002079],\n",
              "       [-0.88640526, -0.6351352 ,  0.66567484,  1.07923939],\n",
              "       [ 1.12815215, -0.34806844,  0.66567484, -1.91002079],\n",
              "       [-0.88640526, -0.6351352 ,  0.66567484,  0.88513158],\n",
              "       [-0.88640526,  1.23079873,  0.70384427, -0.59008772],\n",
              "       [-0.88640526, -0.70690189,  0.70384427,  1.27334719],\n",
              "       [ 1.12815215, -1.42456879,  0.78018313, -1.75473454],\n",
              "       [-0.88640526, -0.56336851,  0.78018313,  1.6615628 ],\n",
              "       [ 1.12815215,  0.80019859,  0.93286085, -0.93948177],\n",
              "       [-0.88640526, -0.20453507,  0.93286085,  0.96277471],\n",
              "       [ 1.12815215,  0.22606507,  0.97103028, -1.17241113],\n",
              "       [-0.88640526, -0.41983513,  0.97103028,  1.73920592],\n",
              "       [-0.88640526, -0.20453507,  1.00919971, -0.90066021],\n",
              "       [ 1.12815215, -0.49160182,  1.00919971,  0.49691598],\n",
              "       [ 1.12815215,  0.08253169,  1.00919971, -1.44416206],\n",
              "       [ 1.12815215, -0.77866858,  1.00919971,  0.96277471],\n",
              "       [ 1.12815215, -0.20453507,  1.00919971, -1.56062674],\n",
              "       [ 1.12815215, -0.20453507,  1.00919971,  1.62274124],\n",
              "       [-0.88640526,  0.94373197,  1.04736914, -1.44416206],\n",
              "       [-0.88640526, -0.6351352 ,  1.04736914,  1.38981187],\n",
              "       [ 1.12815215,  1.37433211,  1.04736914, -1.36651894],\n",
              "       [ 1.12815215, -0.85043527,  1.04736914,  0.72984534],\n",
              "       [ 1.12815215,  1.4460988 ,  1.23821628, -1.4053405 ],\n",
              "       [ 1.12815215, -0.27630176,  1.23821628,  1.54509812],\n",
              "       [-0.88640526, -0.13276838,  1.390894  , -0.7065524 ],\n",
              "       [-0.88640526, -0.49160182,  1.390894  ,  1.38981187],\n",
              "       [ 1.12815215,  0.51313183,  1.42906343, -1.36651894],\n",
              "       [-0.88640526, -0.70690189,  1.42906343,  1.46745499],\n",
              "       [-0.88640526,  0.15429838,  1.46723286, -0.43480148],\n",
              "       [ 1.12815215, -0.6351352 ,  1.46723286,  1.81684904],\n",
              "       [-0.88640526,  1.08726535,  1.54357172, -1.01712489],\n",
              "       [ 1.12815215, -0.77866858,  1.54357172,  0.69102378],\n",
              "       [-0.88640526,  0.15429838,  1.61991057, -1.28887582],\n",
              "       [-0.88640526, -0.20453507,  1.61991057,  1.35099031],\n",
              "       [-0.88640526, -0.34806844,  1.61991057, -1.05594645],\n",
              "       [-0.88640526, -0.49160182,  1.61991057,  0.72984534],\n",
              "       [ 1.12815215, -0.41983513,  2.00160487, -1.63826986],\n",
              "       [-0.88640526, -0.06100169,  2.00160487,  1.58391968],\n",
              "       [-0.88640526,  0.58489852,  2.26879087, -1.32769738],\n",
              "       [-0.88640526, -0.27630176,  2.26879087,  1.11806095],\n",
              "       [-0.88640526,  0.44136514,  2.49780745, -0.86183865],\n",
              "       [ 1.12815215, -0.49160182,  2.49780745,  0.92395314],\n",
              "       [ 1.12815215, -0.49160182,  2.91767117, -1.25005425],\n",
              "       [ 1.12815215, -0.6351352 ,  2.91767117,  1.27334719]])"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2_esc.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "689M5zhsshnl",
        "outputId": "6b112409-0b27-4e62-fd46-19e4daa9e234"
      },
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Componentes principais\n",
        "\n",
        "O que o PCA faz?\n",
        "\n",
        "PCA, ou Principal Component Analysis é uma técnica para reduzir a dimensão de um conjunto de dados preservando, de certa forma, suas propriedades. \n",
        "\n",
        "O uso da técnica PCA na redução da dimensão de dados é justificado pela fácil representação de dados multidimensionais, utilizando a informação contida na matriz de covariância dos dados, princípios da álgebra linear e estatística básica."
      ],
      "metadata": {
        "id": "rHsUHONbsmR2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importando a biblioteca necessária: PCA\n",
        "from sklearn.decomposition import PCA"
      ],
      "metadata": {
        "id": "5_fBUG5WsrbO"
      },
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reduzindo para 2 a quantidade de atributos necessários utilizando o PCA\n",
        "pca = PCA(n_components=2)"
      ],
      "metadata": {
        "id": "6xZPf642tAo8"
      },
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2_pca = pca.fit_transform(df2_esc)"
      ],
      "metadata": {
        "id": "YL4BfjdNuRte"
      },
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2_pca"
      ],
      "metadata": {
        "id": "qQgTgadoud6W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e59fefa-5885-48e0-d17e-144110404b83"
      },
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-4.06382715e-01, -5.20713635e-01],\n",
              "       [-1.42767287e+00, -3.67310199e-01],\n",
              "       [ 5.07605706e-02, -1.89406774e+00],\n",
              "       [-1.69451310e+00, -1.63190805e+00],\n",
              "       [-3.13108383e-01, -1.81048272e+00],\n",
              "       [-1.71744627e+00, -1.59926418e+00],\n",
              "       [ 7.90821124e-01, -1.94727112e+00],\n",
              "       [-2.14832159e+00, -1.50537369e+00],\n",
              "       [ 2.77428623e+00, -8.82987672e-01],\n",
              "       [-1.21629477e+00, -1.61640464e+00],\n",
              "       [ 2.62905084e+00, -8.61237043e-01],\n",
              "       [-1.68947038e+00, -1.54542784e+00],\n",
              "       [ 1.68582253e+00, -2.02394479e+00],\n",
              "       [-1.64607339e+00, -1.52251259e+00],\n",
              "       [ 1.17443628e+00, -6.12790961e-01],\n",
              "       [-1.32613070e+00, -2.36719149e-01],\n",
              "       [ 1.67728253e-02, -1.74344572e+00],\n",
              "       [-1.07842454e+00, -2.44715641e-01],\n",
              "       [ 1.48758780e+00, -5.72676028e-01],\n",
              "       [-1.66373169e+00, -1.43259774e+00],\n",
              "       [ 4.88090311e-01, -3.92921145e-01],\n",
              "       [-1.01895051e+00, -1.66247511e-01],\n",
              "       [ 1.35891492e+00, -1.82866936e+00],\n",
              "       [-7.22972722e-01, -1.81687017e-01],\n",
              "       [ 1.51315931e+00, -1.76451196e+00],\n",
              "       [-1.06241157e+00, -4.31150614e-02],\n",
              "       [ 5.88832908e-01, -1.62541614e+00],\n",
              "       [-2.06188227e-01, -1.71906970e-01],\n",
              "       [ 3.68426440e-01, -1.56300645e+00],\n",
              "       [-1.96420414e+00, -1.21211990e+00],\n",
              "       [ 2.54759194e+00, -5.27913522e-01],\n",
              "       [-1.68983067e+00, -1.22412321e+00],\n",
              "       [ 2.20131721e+00, -3.88195647e-01],\n",
              "       [-1.87329695e+00,  2.24855270e-01],\n",
              "       [ 1.26515693e+00, -1.58125854e+00],\n",
              "       [-1.90386557e+00, -1.10444099e+00],\n",
              "       [ 8.39344590e-01, -1.48793867e+00],\n",
              "       [-1.24644437e+00, -1.17425940e+00],\n",
              "       [ 3.02432444e-01, -1.31960089e+00],\n",
              "       [-1.79415868e+00, -1.00420352e+00],\n",
              "       [ 1.49387594e+00, -1.47013252e+00],\n",
              "       [-1.57824777e+00,  3.26252676e-01],\n",
              "       [ 1.09972892e+00, -4.75148304e-02],\n",
              "       [-8.78229151e-01, -1.08373653e+00],\n",
              "       [ 8.90421740e-01, -1.34990772e+00],\n",
              "       [-1.33047664e+00, -1.01561907e+00],\n",
              "       [ 2.19540681e-01, -1.21995587e+00],\n",
              "       [-7.02592333e-01, -1.08085693e+00],\n",
              "       [-4.70519161e-01, -1.11577393e+00],\n",
              "       [-3.71782513e-01, -1.13065684e+00],\n",
              "       [ 2.49709965e-01, -1.16611641e+00],\n",
              "       [-2.81507713e-01,  2.47898317e-01],\n",
              "       [-6.92486630e-01, -9.94947343e-01],\n",
              "       [ 1.00183656e+00,  8.36296961e-02],\n",
              "       [ 4.85517270e-01, -1.17239636e+00],\n",
              "       [ 9.16095461e-01,  9.67976267e-02],\n",
              "       [ 4.01316916e-01, -1.13059450e+00],\n",
              "       [ 1.86862991e+00, -1.76710825e-02],\n",
              "       [-8.10654470e-01, -8.89574289e-01],\n",
              "       [ 1.07827242e+00,  1.59810666e-01],\n",
              "       [ 1.65086088e+00,  7.33740960e-02],\n",
              "       [-8.40256337e-01,  4.48881521e-01],\n",
              "       [ 1.13717903e+00, -1.15401647e+00],\n",
              "       [ 3.08719686e-01, -1.02922982e+00],\n",
              "       [ 1.43815483e+00,  1.63848653e-01],\n",
              "       [-9.96758186e-01,  5.30768704e-01],\n",
              "       [ 5.44171232e-03, -9.54225943e-01],\n",
              "       [ 1.29298442e+00, -1.14827597e+00],\n",
              "       [-9.47389862e-01,  5.23327249e-01],\n",
              "       [-4.57607937e-01, -8.84390383e-01],\n",
              "       [ 1.67683173e+00,  1.56994962e-01],\n",
              "       [ 4.16021294e-01, -9.86837080e-01],\n",
              "       [ 8.70906219e-01, -1.02631905e+00],\n",
              "       [ 6.84235085e-01, -9.98271327e-01],\n",
              "       [ 1.34595784e+00,  3.52842574e-01],\n",
              "       [-4.69867989e-01,  6.26458338e-01],\n",
              "       [ 2.27835275e-02, -7.81833032e-01],\n",
              "       [ 3.81292376e-01,  4.98237051e-01],\n",
              "       [-1.03665230e+00, -6.22127824e-01],\n",
              "       [ 5.13597177e-01, -8.55673851e-01],\n",
              "       [ 1.14055197e+00,  3.83752756e-01],\n",
              "       [ 9.58845944e-02,  5.41167687e-01],\n",
              "       [ 1.90090826e+00,  2.69270021e-01],\n",
              "       [ 3.12157595e-01, -8.25335848e-01],\n",
              "       [-1.26872547e+00, -5.87210823e-01],\n",
              "       [ 8.29573578e-01,  4.30691769e-01],\n",
              "       [ 3.82433784e-01, -7.48585816e-01],\n",
              "       [-1.16671899e+00, -5.15038230e-01],\n",
              "       [-7.07867782e-01, -5.55092382e-01],\n",
              "       [ 4.55367669e-01, -7.30251123e-01],\n",
              "       [ 1.10375960e+00, -7.98926738e-01],\n",
              "       [-5.19300383e-01,  7.79947493e-01],\n",
              "       [ 7.48178745e-01,  6.17967591e-01],\n",
              "       [ 1.21223952e-01, -6.21459016e-01],\n",
              "       [-3.27057250e-01, -5.53913735e-01],\n",
              "       [-5.16662945e-01,  8.08582982e-01],\n",
              "       [ 2.80131086e-01, -6.45501482e-01],\n",
              "       [-7.87237308e-01, -4.84651914e-01],\n",
              "       [ 9.34617726e-01,  6.19129095e-01],\n",
              "       [-6.34366480e-01,  8.55537580e-01],\n",
              "       [-7.45169167e-01, -4.32528995e-01],\n",
              "       [ 3.51736123e-01, -5.97959120e-01],\n",
              "       [ 1.41903955e+00,  5.75066566e-01],\n",
              "       [-4.98392518e-01,  8.64138980e-01],\n",
              "       [ 6.10411629e-01,  6.96992316e-01],\n",
              "       [-8.70573120e-01, -4.13639266e-01],\n",
              "       [ 1.13743087e+00, -6.87241003e-01],\n",
              "       [ 1.12369414e+00,  6.48926087e-01],\n",
              "       [ 1.89485259e+00,  5.32725254e-01],\n",
              "       [ 1.66277942e+00,  5.67642254e-01],\n",
              "       [ 1.50674188e+00,  5.91110981e-01],\n",
              "       [-1.28954958e+00, -3.21465312e-01],\n",
              "       [-3.17759179e-02, -4.81725557e-01],\n",
              "       [-6.04429349e-01,  9.38586267e-01],\n",
              "       [-1.17937838e+00, -2.79646307e-01],\n",
              "       [-1.18334467e+00, -2.79074127e-01],\n",
              "       [ 1.17553272e+00, -6.34545905e-01],\n",
              "       [ 5.76993109e-02, -4.66256439e-01],\n",
              "       [ 5.82648530e-01, -4.86829980e-01],\n",
              "       [ 1.59937938e-01, -4.23293075e-01],\n",
              "       [-4.76852264e-01,  1.00675048e+00],\n",
              "       [ 2.08622326e-02, -4.02111508e-01],\n",
              "       [-3.60876912e-01, -2.86453243e-01],\n",
              "       [-8.18252351e-01,  1.11611009e+00],\n",
              "       [-4.27018733e-01, -2.46936984e-01],\n",
              "       [-1.31210277e+00, -1.14141375e-01],\n",
              "       [ 8.72125709e-01,  9.20380930e-01],\n",
              "       [-8.76017553e-01,  1.18311437e+00],\n",
              "       [ 2.30203421e+00,  7.05154010e-01],\n",
              "       [-4.41408104e-01,  1.11786092e+00],\n",
              "       [ 1.76294893e+00,  7.86437844e-01],\n",
              "       [-3.92039780e-01,  1.11041946e+00],\n",
              "       [-4.62082916e-01, -1.83367350e-01],\n",
              "       [-1.15256325e+00, -7.97638257e-02],\n",
              "       [ 5.36209096e-01,  1.02974833e+00],\n",
              "       [-1.70487623e+00,  3.24442165e-02],\n",
              "       [ 1.19570032e+00, -4.03729862e-01],\n",
              "       [-6.84747745e-01,  1.21291448e+00],\n",
              "       [ 3.53272095e-01,  1.08643311e+00],\n",
              "       [-9.82221565e-01, -4.71043747e-02],\n",
              "       [ 1.89035883e+00, -4.50063966e-01],\n",
              "       [-1.21855815e+00,  1.35146929e+00],\n",
              "       [-4.74910385e-01, -6.48138980e-02],\n",
              "       [-1.53080042e+00,  9.37407158e-02],\n",
              "       [ 5.95450970e-01,  1.13742569e+00],\n",
              "       [-1.52316497e+00,  1.45568084e+00],\n",
              "       [ 1.09090711e+00,  1.06243584e+00],\n",
              "       [-1.18435761e+00,  7.08613124e-02],\n",
              "       [ 3.00846740e-01, -1.23166897e-01],\n",
              "       [-1.04051604e+00,  1.41219361e+00],\n",
              "       [ 1.35051212e+00,  1.05272281e+00],\n",
              "       [-7.40339814e-01,  1.36697269e+00],\n",
              "       [ 8.47864590e-01, -2.05595090e-01],\n",
              "       [-9.41714425e-01,  6.34354417e-02],\n",
              "       [ 1.10263878e+00, -2.43946728e-01],\n",
              "       [-1.83144095e+00,  1.97380086e-01],\n",
              "       [ 1.48097906e+00,  1.03326246e+00],\n",
              "       [-1.38999563e+00,  1.30980723e-01],\n",
              "       [ 1.33287409e+00,  1.05558682e+00],\n",
              "       [-1.25665910e+00,  1.10946634e-01],\n",
              "       [ 1.04004275e+00, -2.05581064e-01],\n",
              "       [-1.57293263e+00,  1.87665496e-01],\n",
              "       [ 4.84983547e-01,  1.27086361e+00],\n",
              "       [-1.74133333e+00,  2.71269219e-01],\n",
              "       [ 1.45445958e+00,  1.24115857e+00],\n",
              "       [-1.01540884e+00,  2.78776132e-01],\n",
              "       [ 1.21928466e+00,  1.30585854e+00],\n",
              "       [-1.69709206e+00,  4.10446083e-01],\n",
              "       [ 2.64157486e-01,  1.44867334e-01],\n",
              "       [-4.21324839e-01,  1.58177549e+00],\n",
              "       [ 1.30698699e+00,  1.32190295e+00],\n",
              "       [-9.38805793e-01,  1.65962313e+00],\n",
              "       [ 1.18951561e+00,  1.33964832e+00],\n",
              "       [-9.97203383e-01,  1.66820738e+00],\n",
              "       [ 1.42716078e+00, -1.08217936e-03],\n",
              "       [-1.60565560e+00,  4.55127547e-01],\n",
              "       [ 2.14205006e+00,  1.22517962e+00],\n",
              "       [-8.28402441e-01,  1.67223290e+00],\n",
              "       [ 2.21692493e+00,  1.35977748e+00],\n",
              "       [-9.94630016e-01,  1.84289057e+00],\n",
              "       [ 1.77867756e-01,  4.49552248e-01],\n",
              "       [-1.50900833e+00,  7.03127688e-01],\n",
              "       [ 1.54730865e+00,  1.60656936e+00],\n",
              "       [-1.71068006e+00,  7.62674919e-01],\n",
              "       [ 1.88205612e-01,  5.06252606e-01],\n",
              "       [-1.42953569e+00,  2.08340094e+00],\n",
              "       [ 1.22953909e+00,  4.07829870e-01],\n",
              "       [-7.55384802e-01,  2.04050460e+00],\n",
              "       [ 7.73957706e-01,  5.34939526e-01],\n",
              "       [-1.28626064e+00,  8.44610416e-01],\n",
              "       [ 2.68375609e-01,  6.11070624e-01],\n",
              "       [-1.05705706e+00,  8.10267154e-01],\n",
              "       [ 1.08870927e+00,  2.11339899e+00],\n",
              "       [-1.34984935e+00,  1.14586069e+00],\n",
              "       [ 1.09288835e+00,  9.82840852e-01],\n",
              "       [-1.17957174e+00,  1.32456784e+00],\n",
              "       [ 6.72751128e-01,  1.22106095e+00],\n",
              "       [-7.23719161e-01,  2.76501038e+00],\n",
              "       [ 7.67096226e-01,  2.86193009e+00],\n",
              "       [-1.06501524e+00,  3.13725616e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Razão  das variáveis explicativa\n",
        "pca.explained_variance_ratio_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8bZOaXvNu3-c",
        "outputId": "6d908f7c-fd0d-4168-d052-f8d2b1412f6f"
      },
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.33690046, 0.26230645])"
            ]
          },
          "metadata": {},
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soma das variáveis explicativa\n",
        "pca.explained_variance_ratio_.sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UhDCTr21vDu1",
        "outputId": "6e3be5dd-a174-4e7d-ba71-41d0a5d29e57"
      },
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5992069019819846"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A soma das variáveis criadas pelo processo de PCA equivale a 59% da relação com as 4 variáveis iniciais. O ideal é que esse valor seja o mais próximo possível de 100%, o que representaria menor perda de relação com os dados originais.\n",
        "\n",
        "59% é um valor muito baixo e representa 41% de perda de representação dos dados originais, mas para fins didáticos seguirei com o processo de K-means com o PCA até o final."
      ],
      "metadata": {
        "id": "apKBxM-MvXuC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Método Elbow para identificar a quantidade adequada de clusters através do wcss"
      ],
      "metadata": {
        "id": "uzuGlbrNxDMs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss = []\n",
        "for i in range (1, 11):\n",
        "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state= 5, max_iter=300)\n",
        "    kmeans.fit(df2_pca)\n",
        "    # inertia - Método para gerar o wcss\n",
        "    wcss.append(kmeans.inertia_)"
      ],
      "metadata": {
        "id": "6XR9T1T7xZ4G"
      },
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "YjQOPmKVxiAj"
      },
      "execution_count": 207,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.lineplot(range(1,11), wcss, marker=\"o\", color=\"red\")\n",
        "plt.title(\"Método Elbow\")\n",
        "plt.xlabel(\"Número de clusters\")\n",
        "plt.ylabel(\"WCSS\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "FohW9sE1xvwU",
        "outputId": "dd9bb426-7c30-49e0-d49d-07196d40e302"
      },
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'WCSS')"
            ]
          },
          "metadata": {},
          "execution_count": 208
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Através do gráfico é possivel identificar o ponto de inflexão, que neste caso seria o 4\n",
        "# O ponto de inflexão representa a quantidade ideal de clusters"
      ],
      "metadata": {
        "id": "tvWwOcQrxyGx"
      },
      "execution_count": 209,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Agrupamento"
      ],
      "metadata": {
        "id": "sKfMRaasx1L4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters = 4, init = \"k-means++\", random_state = 5, max_iter = 300)"
      ],
      "metadata": {
        "id": "WARqvfH4x0BL"
      },
      "execution_count": 210,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans3 = kmeans.fit(df2_pca)"
      ],
      "metadata": {
        "id": "kcOD10HSx8VS"
      },
      "execution_count": 211,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Coordenadas dos centróides\n",
        "centroides = kmeans3.cluster_centers_\n",
        "centroides"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrvJj_qHx9wN",
        "outputId": "5d7a5a3c-a6b5-4967-8a22-6ea1bb13eab1"
      },
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.15208536, -0.48631961],\n",
              "       [ 0.80089898, -0.95285608],\n",
              "       [ 1.12773395,  0.75231371],\n",
              "       [-0.89664028,  1.25740369]])"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classificação dos dados\n",
        "classificacao3 = kmeans3.labels_\n",
        "classificacao3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "as8CprdyyA6f",
        "outputId": "cf32bac4-24ad-4fe2-8542-af83cf08b5ee"
      },
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
              "       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 2, 0,\n",
              "       1, 0, 1, 0, 0, 0, 1, 0, 0, 2, 1, 2, 1, 2, 0, 2, 2, 3, 1, 1, 2, 3,\n",
              "       1, 1, 3, 0, 2, 1, 1, 1, 2, 3, 1, 2, 0, 1, 2, 2, 2, 1, 0, 2, 1, 0,\n",
              "       0, 1, 1, 3, 2, 1, 0, 3, 1, 0, 2, 3, 0, 1, 2, 3, 2, 0, 1, 2, 2, 2,\n",
              "       2, 0, 1, 3, 0, 0, 1, 1, 1, 1, 3, 1, 0, 3, 0, 0, 2, 3, 2, 3, 2, 3,\n",
              "       0, 0, 2, 0, 1, 3, 2, 0, 1, 3, 0, 0, 2, 3, 2, 0, 1, 3, 2, 3, 1, 0,\n",
              "       1, 0, 2, 0, 2, 0, 1, 0, 2, 0, 2, 0, 2, 0, 2, 3, 2, 3, 2, 3, 2, 0,\n",
              "       2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,\n",
              "       2, 3], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizando graficamente com PLOTLY os agrupamentos por grupo"
      ],
      "metadata": {
        "id": "voX8PRvMyFGL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "import plotly.graph_objects as go"
      ],
      "metadata": {
        "id": "zroPvhW2yH4i"
      },
      "execution_count": 214,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graf1 = px.scatter( x = df2_pca[:,0], y = df2_pca[:,1], color = classificacao3)\n",
        "graf2 = px.scatter( x = centroides[:,0], y = centroides[:,1], size = [15,15,15,15])\n",
        "graf3 = go.Figure( data = graf1.data + graf2.data)\n",
        "graf3.update_layout( width=800, height = 500, title_text=\"Agrupamento K-means\")\n",
        "graf3.update_xaxes(title=\"Rendimento anual (K$)\")\n",
        "graf3.update_yaxes(title=\"Pontuação\")\n",
        "graf3.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "acUVJtvOyLCE",
        "outputId": "c1ec609e-bbc5-4182-e181-228b1dc0622c"
      },
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizando o agrupamento por uma lista"
      ],
      "metadata": {
        "id": "3mJwF3YTyQ3p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "agrupamento3 = pd.DataFrame(classificacao3, columns = [\"Grupo\"])\n",
        "agrupamento3"
      ],
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      "execution_count": 216,
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            "text/plain": [
              "     Grupo\n",
              "0        0\n",
              "1        0\n",
              "2        1\n",
              "3        0\n",
              "4        1\n",
              "..     ...\n",
              "195      3\n",
              "196      2\n",
              "197      3\n",
              "198      2\n",
              "199      3\n",
              "\n",
              "[200 rows x 1 columns]"
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              "      <th>195</th>\n",
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              "      <th>196</th>\n",
              "      <td>2</td>\n",
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              "      <th>197</th>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>3</td>\n",
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              "<p>200 rows × 1 columns</p>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "# Juntando o atributo Grupo ao dataframe original. Será criado um novo dataframe para manter a integridade dos dados\n",
        "df5 = pd.concat([df,agrupamento3],axis=1)\n",
        "df5"
      ],
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        "id": "e01gahrXyVEF",
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      },
      "execution_count": 217,
      "outputs": [
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            "text/plain": [
              "     CustomerID  genero  idade  rendimento  pontuacao  Grupo\n",
              "0             1    Male     19          15         39      0\n",
              "1             2    Male     21          15         81      0\n",
              "2             3  Female     20          16          6      1\n",
              "3             4  Female     23          16         77      0\n",
              "4             5  Female     31          17         40      1\n",
              "..          ...     ...    ...         ...        ...    ...\n",
              "195         196  Female     35         120         79      3\n",
              "196         197  Female     45         126         28      2\n",
              "197         198    Male     32         126         74      3\n",
              "198         199    Male     32         137         18      2\n",
              "199         200    Male     30         137         83      3\n",
              "\n",
              "[200 rows x 6 columns]"
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              "      <td>2</td>\n",
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              "      <td>4</td>\n",
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              "      <td>23</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>120</td>\n",
              "      <td>79</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>Female</td>\n",
              "      <td>45</td>\n",
              "      <td>126</td>\n",
              "      <td>28</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>126</td>\n",
              "      <td>74</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>137</td>\n",
              "      <td>18</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc2b37bd-f4ff-4673-9258-17364fc480b6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dc2b37bd-f4ff-4673-9258-17364fc480b6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dc2b37bd-f4ff-4673-9258-17364fc480b6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizando os dados"
      ],
      "metadata": {
        "id": "R4o60HQTyY00"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Buscando os dados da linha 125\n",
        "df5.loc[125,:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6DDdISVqybJg",
        "outputId": "b708ada1-fa21-40e8-c87e-fc5e90339aff"
      },
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID       126\n",
              "genero        Female\n",
              "idade             31\n",
              "rendimento        70\n",
              "pontuacao         77\n",
              "Grupo              0\n",
              "Name: 125, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Buscando os dados usando o CustomerID\n",
        "df5.loc[df5.CustomerID == 76]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "pbtyvfcYycX8",
        "outputId": "c35155e4-bc1c-49f3-c12f-450b69b42061"
      },
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    CustomerID genero  idade  rendimento  pontuacao  Grupo\n",
              "75          76   Male     26          54         54      3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e9a7d798-fca9-4629-b7ef-276785299dc7\">\n",
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              "      <th>pontuacao</th>\n",
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              "      <th>75</th>\n",
              "      <td>76</td>\n",
              "      <td>Male</td>\n",
              "      <td>26</td>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9a7d798-fca9-4629-b7ef-276785299dc7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e9a7d798-fca9-4629-b7ef-276785299dc7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e9a7d798-fca9-4629-b7ef-276785299dc7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Agrupamento Hierárquico ( Agglomerative Hierarchical Clustering )\n",
        "\n",
        "A análise de agrupamentos por métodos hierárquicos  é um método “não supervisionado” de reconhecimento de “padrões naturais” de comportamento em amostras com base em dados multivariados.\n",
        "\n",
        "O objetivo da técnica é reunir amostras (objetos) com base na sua “proximidade” (semelhança), reduzindo a “dimensionalidade” dos dados e permitindo a visualização de dados multidimensionais através de um gráfico bidimensional chamado dendrograma."
      ],
      "metadata": {
        "id": "CUvjocM8_hDB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Algoritmo hierárquico com PCA"
      ],
      "metadata": {
        "id": "QgPV05N1B85a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Relembrando o dataframe original\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dNwKU4xACGsx",
        "outputId": "f9e87b8b-5478-4c6c-ab94-ef9b955bfda8"
      },
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID  genero  idade  rendimento  pontuacao\n",
              "0           1    Male     19          15         39\n",
              "1           2    Male     21          15         81\n",
              "2           3  Female     20          16          6\n",
              "3           4  Female     23          16         77\n",
              "4           5  Female     31          17         40"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d421add2-29e8-433c-826a-7c843dbb9233\">\n",
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              "      <td>4</td>\n",
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              "    <tr>\n",
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              "      <td>40</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizando o dataframe escalonado\n",
        "df2_esc"
      ],
      "metadata": {
        "id": "6eumFIFvCZXh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51dc7839-788b-46c2-e318-ade8eed723ca"
      },
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.12815215, -1.42456879, -1.73899919, -0.43480148],\n",
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              "       [-0.88640526, -1.3528021 , -1.70082976, -1.71591298],\n",
              "       [-0.88640526, -1.13750203, -1.70082976,  1.04041783],\n",
              "       [-0.88640526, -0.56336851, -1.66266033, -0.39597992],\n",
              "       [-0.88640526, -1.20926872, -1.66266033,  1.00159627],\n",
              "       [-0.88640526, -0.27630176, -1.62449091, -1.71591298],\n",
              "       [-0.88640526, -1.13750203, -1.62449091,  1.70038436],\n",
              "       [ 1.12815215,  1.80493225, -1.58632148, -1.83237767],\n",
              "       [-0.88640526, -0.6351352 , -1.58632148,  0.84631002],\n",
              "       [ 1.12815215,  2.02023231, -1.58632148, -1.4053405 ],\n",
              "       [-0.88640526, -0.27630176, -1.58632148,  1.89449216],\n",
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              "       [ 1.12815215, -0.99396865, -1.39547433,  0.88513158],\n",
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              "       [-0.88640526, -1.13750203, -1.20462718,  1.42863343],\n",
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              "       [-0.88640526, -0.56336851, -0.67025518,  0.14752193],\n",
              "       [ 1.12815215,  1.4460988 , -0.67025518,  0.38045129],\n",
              "       [-0.88640526,  0.80019859, -0.67025518, -0.20187212],\n",
              "       [ 1.12815215,  0.58489852, -0.67025518, -0.35715836],\n",
              "       [-0.88640526,  0.87196528, -0.63208575, -0.00776431],\n",
              "       [ 1.12815215,  2.16376569, -0.63208575, -0.16305055],\n",
              "       [-0.88640526, -0.85043527, -0.55574689,  0.03105725],\n",
              "       [ 1.12815215,  1.01549866, -0.55574689, -0.16305055],\n",
              "       [ 1.12815215,  2.23553238, -0.55574689,  0.22516505],\n",
              "       [ 1.12815215, -1.42456879, -0.55574689,  0.18634349],\n",
              "       [-0.88640526,  2.02023231, -0.51757746,  0.06987881],\n",
              "       [-0.88640526,  1.08726535, -0.51757746,  0.34162973],\n",
              "       [ 1.12815215,  1.73316556, -0.47940803,  0.03105725],\n",
              "       [ 1.12815215, -1.49633548, -0.47940803,  0.34162973],\n",
              "       [-0.88640526,  0.29783176, -0.47940803, -0.00776431],\n",
              "       [-0.88640526,  2.091999  , -0.47940803, -0.08540743],\n",
              "       [ 1.12815215, -1.42456879, -0.47940803,  0.34162973],\n",
              "       [-0.88640526, -0.49160182, -0.47940803, -0.12422899],\n",
              "       [ 1.12815215,  2.23553238, -0.4412386 ,  0.18634349],\n",
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              "       [-0.88640526,  1.51786549, -0.40306917,  0.22516505],\n",
              "       [ 1.12815215,  1.4460988 , -0.25039146, -0.12422899],\n",
              "       [ 1.12815215, -0.92220196, -0.25039146,  0.14752193],\n",
              "       [-0.88640526,  0.44136514, -0.25039146,  0.10870037],\n",
              "       [ 1.12815215,  0.08253169, -0.25039146, -0.08540743],\n",
              "       [-0.88640526, -1.13750203, -0.25039146,  0.06987881],\n",
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              "       [ 1.12815215,  1.30256542, -0.25039146,  0.03105725],\n",
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              "       [ 1.12815215,  2.02023231, -0.25039146, -0.35715836],\n",
              "       [-0.88640526,  0.51313183, -0.25039146, -0.24069368],\n",
              "       [-0.88640526, -1.28103541, -0.25039146,  0.26398661],\n",
              "       [ 1.12815215,  0.65666521, -0.25039146, -0.16305055],\n",
              "       [-0.88640526,  1.15903204, -0.13588317,  0.30280817],\n",
              "       [-0.88640526, -1.20926872, -0.13588317,  0.18634349],\n",
              "       [-0.88640526, -0.34806844, -0.09771374,  0.38045129],\n",
              "       [-0.88640526,  0.80019859, -0.09771374, -0.16305055],\n",
              "       [-0.88640526,  2.091999  , -0.05954431,  0.18634349],\n",
              "       [ 1.12815215, -1.49633548, -0.05954431, -0.35715836],\n",
              "       [ 1.12815215,  0.65666521, -0.02137488, -0.04658587],\n",
              "       [-0.88640526,  0.08253169, -0.02137488, -0.39597992],\n",
              "       [-0.88640526, -0.49160182, -0.02137488, -0.3183368 ],\n",
              "       [ 1.12815215, -1.06573534, -0.02137488,  0.06987881],\n",
              "       [-0.88640526,  0.58489852, -0.02137488, -0.12422899],\n",
              "       [-0.88640526, -0.85043527, -0.02137488, -0.00776431],\n",
              "       [ 1.12815215,  0.65666521,  0.01679455, -0.3183368 ],\n",
              "       [ 1.12815215, -1.3528021 ,  0.01679455, -0.04658587],\n",
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              "       [-0.88640526,  0.7284319 ,  0.05496398, -0.08540743],\n",
              "       [ 1.12815215,  2.02023231,  0.05496398,  0.34162973],\n",
              "       [ 1.12815215, -0.92220196,  0.05496398,  0.18634349],\n",
              "       [ 1.12815215,  0.7284319 ,  0.05496398,  0.22516505],\n",
              "       [-0.88640526, -1.28103541,  0.05496398, -0.3183368 ],\n",
              "       [-0.88640526,  1.94846562,  0.09313341, -0.00776431],\n",
              "       [ 1.12815215,  1.08726535,  0.09313341, -0.16305055],\n",
              "       [ 1.12815215,  2.091999  ,  0.09313341, -0.27951524],\n",
              "       [ 1.12815215,  1.94846562,  0.09313341, -0.08540743],\n",
              "       [ 1.12815215,  1.87669894,  0.09313341,  0.06987881],\n",
              "       [-0.88640526, -1.42456879,  0.09313341,  0.14752193],\n",
              "       [-0.88640526, -0.06100169,  0.13130284, -0.3183368 ],\n",
              "       [ 1.12815215, -1.42456879,  0.13130284, -0.16305055],\n",
              "       [-0.88640526, -1.49633548,  0.16947227, -0.08540743],\n",
              "       [-0.88640526, -1.42456879,  0.16947227, -0.00776431],\n",
              "       [-0.88640526,  1.73316556,  0.16947227, -0.27951524],\n",
              "       [-0.88640526,  0.7284319 ,  0.16947227,  0.34162973],\n",
              "       [-0.88640526,  0.87196528,  0.24581112, -0.27951524],\n",
              "       [-0.88640526,  0.80019859,  0.24581112,  0.26398661],\n",
              "       [ 1.12815215, -0.85043527,  0.24581112,  0.22516505],\n",
              "       [-0.88640526, -0.06100169,  0.24581112, -0.39597992],\n",
              "       [-0.88640526,  0.08253169,  0.32214998,  0.30280817],\n",
              "       [ 1.12815215,  0.010765  ,  0.32214998,  1.58391968],\n",
              "       [-0.88640526, -1.13750203,  0.36031941, -0.82301709],\n",
              "       [-0.88640526, -0.56336851,  0.36031941,  1.04041783],\n",
              "       [ 1.12815215,  0.29783176,  0.39848884, -0.59008772],\n",
              "       [ 1.12815215,  0.08253169,  0.39848884,  1.73920592],\n",
              "       [ 1.12815215,  1.4460988 ,  0.39848884, -1.52180518],\n",
              "       [ 1.12815215, -0.06100169,  0.39848884,  0.96277471],\n",
              "       [ 1.12815215,  0.58489852,  0.39848884, -1.5994483 ],\n",
              "       [ 1.12815215,  0.010765  ,  0.39848884,  0.96277471],\n",
              "       [-0.88640526, -0.99396865,  0.43665827, -0.62890928],\n",
              "       [-0.88640526, -0.56336851,  0.43665827,  0.80748846],\n",
              "       [ 1.12815215, -1.3528021 ,  0.4748277 , -1.75473454],\n",
              "       [-0.88640526, -0.70690189,  0.4748277 ,  1.46745499],\n",
              "       [-0.88640526,  0.36959845,  0.4748277 , -1.67709142],\n",
              "       [ 1.12815215, -0.49160182,  0.4748277 ,  0.88513158],\n",
              "       [ 1.12815215, -1.42456879,  0.51299713, -1.56062674],\n",
              "       [-0.88640526, -0.27630176,  0.51299713,  0.84631002],\n",
              "       [-0.88640526,  1.30256542,  0.55116656, -1.75473454],\n",
              "       [ 1.12815215, -0.49160182,  0.55116656,  1.6615628 ],\n",
              "       [-0.88640526, -0.77866858,  0.58933599, -0.39597992],\n",
              "       [-0.88640526, -0.49160182,  0.58933599,  1.42863343],\n",
              "       [ 1.12815215, -0.99396865,  0.62750542, -1.48298362],\n",
              "       [ 1.12815215, -0.77866858,  0.62750542,  1.81684904],\n",
              "       [ 1.12815215,  0.65666521,  0.62750542, -0.55126616],\n",
              "       [-0.88640526, -0.49160182,  0.62750542,  0.92395314],\n",
              "       [-0.88640526, -0.34806844,  0.66567484, -1.09476801],\n",
              "       [ 1.12815215, -0.34806844,  0.66567484,  1.54509812],\n",
              "       [ 1.12815215,  0.29783176,  0.66567484, -1.28887582],\n",
              "       [ 1.12815215,  0.010765  ,  0.66567484,  1.46745499],\n",
              "       [-0.88640526,  0.36959845,  0.66567484, -1.17241113],\n",
              "       [-0.88640526, -0.06100169,  0.66567484,  1.00159627],\n",
              "       [-0.88640526,  0.58489852,  0.66567484, -1.32769738],\n",
              "       [-0.88640526, -0.85043527,  0.66567484,  1.50627656],\n",
              "       [ 1.12815215, -0.13276838,  0.66567484, -1.91002079],\n",
              "       [-0.88640526, -0.6351352 ,  0.66567484,  1.07923939],\n",
              "       [ 1.12815215, -0.34806844,  0.66567484, -1.91002079],\n",
              "       [-0.88640526, -0.6351352 ,  0.66567484,  0.88513158],\n",
              "       [-0.88640526,  1.23079873,  0.70384427, -0.59008772],\n",
              "       [-0.88640526, -0.70690189,  0.70384427,  1.27334719],\n",
              "       [ 1.12815215, -1.42456879,  0.78018313, -1.75473454],\n",
              "       [-0.88640526, -0.56336851,  0.78018313,  1.6615628 ],\n",
              "       [ 1.12815215,  0.80019859,  0.93286085, -0.93948177],\n",
              "       [-0.88640526, -0.20453507,  0.93286085,  0.96277471],\n",
              "       [ 1.12815215,  0.22606507,  0.97103028, -1.17241113],\n",
              "       [-0.88640526, -0.41983513,  0.97103028,  1.73920592],\n",
              "       [-0.88640526, -0.20453507,  1.00919971, -0.90066021],\n",
              "       [ 1.12815215, -0.49160182,  1.00919971,  0.49691598],\n",
              "       [ 1.12815215,  0.08253169,  1.00919971, -1.44416206],\n",
              "       [ 1.12815215, -0.77866858,  1.00919971,  0.96277471],\n",
              "       [ 1.12815215, -0.20453507,  1.00919971, -1.56062674],\n",
              "       [ 1.12815215, -0.20453507,  1.00919971,  1.62274124],\n",
              "       [-0.88640526,  0.94373197,  1.04736914, -1.44416206],\n",
              "       [-0.88640526, -0.6351352 ,  1.04736914,  1.38981187],\n",
              "       [ 1.12815215,  1.37433211,  1.04736914, -1.36651894],\n",
              "       [ 1.12815215, -0.85043527,  1.04736914,  0.72984534],\n",
              "       [ 1.12815215,  1.4460988 ,  1.23821628, -1.4053405 ],\n",
              "       [ 1.12815215, -0.27630176,  1.23821628,  1.54509812],\n",
              "       [-0.88640526, -0.13276838,  1.390894  , -0.7065524 ],\n",
              "       [-0.88640526, -0.49160182,  1.390894  ,  1.38981187],\n",
              "       [ 1.12815215,  0.51313183,  1.42906343, -1.36651894],\n",
              "       [-0.88640526, -0.70690189,  1.42906343,  1.46745499],\n",
              "       [-0.88640526,  0.15429838,  1.46723286, -0.43480148],\n",
              "       [ 1.12815215, -0.6351352 ,  1.46723286,  1.81684904],\n",
              "       [-0.88640526,  1.08726535,  1.54357172, -1.01712489],\n",
              "       [ 1.12815215, -0.77866858,  1.54357172,  0.69102378],\n",
              "       [-0.88640526,  0.15429838,  1.61991057, -1.28887582],\n",
              "       [-0.88640526, -0.20453507,  1.61991057,  1.35099031],\n",
              "       [-0.88640526, -0.34806844,  1.61991057, -1.05594645],\n",
              "       [-0.88640526, -0.49160182,  1.61991057,  0.72984534],\n",
              "       [ 1.12815215, -0.41983513,  2.00160487, -1.63826986],\n",
              "       [-0.88640526, -0.06100169,  2.00160487,  1.58391968],\n",
              "       [-0.88640526,  0.58489852,  2.26879087, -1.32769738],\n",
              "       [-0.88640526, -0.27630176,  2.26879087,  1.11806095],\n",
              "       [-0.88640526,  0.44136514,  2.49780745, -0.86183865],\n",
              "       [ 1.12815215, -0.49160182,  2.49780745,  0.92395314],\n",
              "       [ 1.12815215, -0.49160182,  2.91767117, -1.25005425],\n",
              "       [ 1.12815215, -0.6351352 ,  2.91767117,  1.27334719]])"
            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2_esc.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVuT_6BACaeE",
        "outputId": "caf07f05-c520-429e-874f-ea4bf109e318"
      },
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Componentes principais"
      ],
      "metadata": {
        "id": "YOt8p55oDe7-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importando a biblioteca necessária: PCA\n",
        "from sklearn.decomposition import PCA"
      ],
      "metadata": {
        "id": "ufHS-v79DeID"
      },
      "execution_count": 223,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reduzindo para 2 a quantidade de atributos necessários utilizando o PCA\n",
        "pca = PCA(n_components=2)"
      ],
      "metadata": {
        "id": "f6_ctFbnDn2v"
      },
      "execution_count": 224,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2_pca = pca.fit_transform(df2_esc)"
      ],
      "metadata": {
        "id": "KXBaEcDVDpd5"
      },
      "execution_count": 225,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2_pca"
      ],
      "metadata": {
        "id": "up3sTd0gDqrC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba3ee6cf-cf44-4280-eb17-296e9208e489"
      },
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [-9.41714425e-01,  6.34354417e-02],\n",
              "       [ 1.10263878e+00, -2.43946728e-01],\n",
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              "       [-1.60565560e+00,  4.55127547e-01],\n",
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              "       [-1.05705706e+00,  8.10267154e-01],\n",
              "       [ 1.08870927e+00,  2.11339899e+00],\n",
              "       [-1.34984935e+00,  1.14586069e+00],\n",
              "       [ 1.09288835e+00,  9.82840852e-01],\n",
              "       [-1.17957174e+00,  1.32456784e+00],\n",
              "       [ 6.72751128e-01,  1.22106095e+00],\n",
              "       [-7.23719161e-01,  2.76501038e+00],\n",
              "       [ 7.67096226e-01,  2.86193009e+00],\n",
              "       [-1.06501524e+00,  3.13725616e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dendrograma\n",
        "\n",
        "O que é um dendrograma?\n",
        "\n",
        "Representação esquemática ou diagrama que lembra a estrutura de uma árvore (ex.: dendrograma filogenético)."
      ],
      "metadata": {
        "id": "TuKHUsZNEH_J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from scipy.cluster.hierarchy import dendrogram, linkage"
      ],
      "metadata": {
        "id": "xuKq1rUSEY5h"
      },
      "execution_count": 227,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dendrograma = dendrogram(linkage(df2_pca,method = \"complete\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "VbKvffSIEzUj",
        "outputId": "c485db19-1752-4b6d-ffff-d2306ecdafb2"
      },
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Vemos no gráfico que o dendrograma fez uma divisão em 3 clusters ( laranja, verde e vermelho )"
      ],
      "metadata": {
        "id": "-1crN0zlGjDV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import AgglomerativeClustering"
      ],
      "metadata": {
        "id": "mSntcZxuE_0d"
      },
      "execution_count": 229,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hier = AgglomerativeClustering(n_clusters=3,affinity=\"euclidean\", linkage=\"complete\")\n",
        "classificacao4 = hier.fit_predict(df2_pca)"
      ],
      "metadata": {
        "id": "J71OcnDGFGKQ"
      },
      "execution_count": 230,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classificacao4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMfo0YY_GNb5",
        "outputId": "f454e4da-a675-468f-9143-833924ba3123"
      },
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2,\n",
              "       1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 0, 1, 2, 2, 2, 2, 2, 1, 0, 1, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 0, 1, 2, 1, 0,\n",
              "       2, 1, 0, 2, 1, 2, 2, 2, 1, 0, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 2, 2,\n",
              "       2, 2, 1, 0, 1, 2, 2, 0, 2, 2, 1, 0, 2, 2, 1, 0, 1, 2, 1, 1, 1, 1,\n",
              "       1, 2, 2, 0, 2, 2, 1, 2, 2, 2, 0, 2, 2, 0, 2, 2, 1, 0, 1, 0, 1, 0,\n",
              "       2, 2, 1, 0, 1, 0, 1, 2, 1, 0, 2, 0, 1, 0, 1, 2, 2, 0, 1, 0, 1, 2,\n",
              "       1, 0, 1, 0, 1, 2, 1, 0, 1, 0, 1, 0, 1, 0, 2, 0, 1, 0, 1, 0, 1, 0,\n",
              "       1, 0, 1, 0, 2, 0, 1, 0, 2, 0, 1, 0, 1, 0, 2, 0, 0, 0, 1, 0, 1, 0,\n",
              "       0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Criando um gráfico de dispersão para representar os 3 clusters criados"
      ],
      "metadata": {
        "id": "vBjXic_iTW_i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "-m7ky6nHSNAp"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graf4 = px.scatter( x = df2_pca[:,0], y = df2_pca[:,1], color = classificacao4)\n",
        "graf4.update_layout( width=800, height = 500, title_text=\"Agrupamento Hierárquico\")\n",
        "graf4.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "mE6kBPtrSTx8",
        "outputId": "dabd9b0b-9db0-420d-be3c-81229164c5d3"
      },
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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      "source": [
        "Agrupando os dados na nova coluna Grupo"
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        "agrupamento4 = pd.DataFrame(classificacao4, columns = [\"Grupo\"])\n",
        "agrupamento4"
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              "     Grupo\n",
              "0        2\n",
              "1        2\n",
              "2        2\n",
              "3        2\n",
              "4        2\n",
              "..     ...\n",
              "195      0\n",
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    {
      "cell_type": "code",
      "source": [
        "# Juntando o atributo Grupo ao dataframe original. Será criado um novo dataframe para manter a integridade dos dados\n",
        "df6 = pd.concat([df,agrupamento4],axis=1)\n",
        "df6"
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      "execution_count": 235,
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            "text/plain": [
              "     CustomerID  genero  idade  rendimento  pontuacao  Grupo\n",
              "0             1    Male     19          15         39      2\n",
              "1             2    Male     21          15         81      2\n",
              "2             3  Female     20          16          6      2\n",
              "3             4  Female     23          16         77      2\n",
              "4             5  Female     31          17         40      2\n",
              "..          ...     ...    ...         ...        ...    ...\n",
              "195         196  Female     35         120         79      0\n",
              "196         197  Female     45         126         28      1\n",
              "197         198    Male     32         126         74      0\n",
              "198         199    Male     32         137         18      0\n",
              "199         200    Male     30         137         83      0\n",
              "\n",
              "[200 rows x 6 columns]"
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "      <td>2</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>120</td>\n",
              "      <td>79</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>Female</td>\n",
              "      <td>45</td>\n",
              "      <td>126</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>126</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>137</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c1734e08-337d-43c8-ad1a-3a5236f32e91')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c1734e08-337d-43c8-ad1a-3a5236f32e91 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c1734e08-337d-43c8-ad1a-3a5236f32e91');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Acessando os dados através do índice\n",
        "df4.iloc[98,:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pdt2oXY9U8l2",
        "outputId": "7c8b8fc8-8dd5-4496-d5a5-8b87630bc406"
      },
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID      99\n",
              "genero        Male\n",
              "idade           48\n",
              "rendimento      61\n",
              "pontuacao       42\n",
              "Grupo            0\n",
              "Name: 98, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Agrupamento hierárquico com todos atributos"
      ],
      "metadata": {
        "id": "wsC4EBD49wOu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Usaremos agora o dataframe df2_esc escalonado para criar o modelo\n",
        "df2_esc.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1CGy6yN29-iw",
        "outputId": "59698bc6-4615-444b-ccfd-99aeb3220121"
      },
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Criando o dendrograma"
      ],
      "metadata": {
        "id": "dW4zHSRG-_Gd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from scipy.cluster.hierarchy import dendrogram, linkage"
      ],
      "metadata": {
        "id": "jQwbJq5O_CSp"
      },
      "execution_count": 238,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dendrograma = dendrogram(linkage(df2_esc,method = \"average\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "1BxWjaTY_Dnb",
        "outputId": "2e3dd327-0fce-410d-e1f7-87a9c437adee"
      },
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Usando como método 'average' ou média no dendrograma e usando a base completa e escalonada fica dividida em 7 grupos ( laranja, verde, vermelho, lilás, marrom, rosa e cinza ), demostrando que a quantidade ideal de clusters será 7. Outros métodos como 'complete', 'single' e 'ward' separam em quantidades diferentes, mas para fins didáticos usarei o método 'average'."
      ],
      "metadata": {
        "id": "ETZIN6uG_Qlj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import AgglomerativeClustering"
      ],
      "metadata": {
        "id": "ZUdy84az_rqt"
      },
      "execution_count": 240,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hier = AgglomerativeClustering(n_clusters=7,affinity=\"euclidean\", linkage=\"average\") # o linkage deve ser o mesmo do método\n",
        "classificacao5 = hier.fit_predict(df2_esc)"
      ],
      "metadata": {
        "id": "GqLl7eo7_xc1"
      },
      "execution_count": 241,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classificacao5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l8KVqodg_3Fj",
        "outputId": "0b5f1319-7b78-45bf-f1fa-576f6939dd5c"
      },
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 5, 4, 0, 4, 0, 4, 0, 1, 0, 1, 0, 4, 0, 5, 5, 4, 5, 1, 0, 5, 5,\n",
              "       4, 5, 4, 5, 4, 5, 4, 0, 1, 0, 1, 5, 4, 0, 4, 0, 4, 0, 4, 5, 1, 0,\n",
              "       4, 0, 4, 0, 0, 0, 4, 5, 0, 1, 4, 1, 4, 1, 0, 1, 1, 5, 4, 4, 1, 5,\n",
              "       4, 4, 5, 0, 1, 4, 4, 4, 1, 5, 4, 1, 0, 4, 1, 1, 1, 4, 0, 1, 4, 0,\n",
              "       4, 4, 4, 5, 1, 4, 4, 5, 4, 0, 1, 5, 0, 4, 1, 5, 1, 0, 4, 1, 1, 1,\n",
              "       1, 0, 4, 5, 0, 0, 4, 4, 4, 4, 5, 4, 4, 2, 0, 0, 3, 2, 3, 2, 3, 2,\n",
              "       0, 0, 3, 0, 6, 2, 3, 0, 6, 2, 0, 0, 3, 2, 3, 0, 6, 2, 3, 2, 6, 0,\n",
              "       6, 0, 3, 0, 3, 0, 6, 0, 3, 0, 3, 0, 3, 0, 6, 2, 3, 2, 3, 2, 6, 0,\n",
              "       3, 2, 3, 2, 6, 0, 3, 0, 6, 2, 6, 2, 6, 0, 6, 0, 3, 0, 6, 0, 6, 2,\n",
              "       3, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agrupamento"
      ],
      "metadata": {
        "id": "NvTkqt_SAFsP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "agrupamento4 = pd.DataFrame(classificacao5, columns = [\"Grupo\"])\n",
        "agrupamento4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "hSRhO0zkCBkx",
        "outputId": "76f84082-345e-46ba-fb19-f29f00fb6000"
      },
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Grupo\n",
              "0        5\n",
              "1        5\n",
              "2        4\n",
              "3        0\n",
              "4        4\n",
              "..     ...\n",
              "195      0\n",
              "196      6\n",
              "197      2\n",
              "198      3\n",
              "199      2\n",
              "\n",
              "[200 rows x 1 columns]"
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6692a191-f00c-4709-8fd2-f0d2485f6a50 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6692a191-f00c-4709-8fd2-f0d2485f6a50');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Juntando o atributo Grupo ao dataframe original. Será criado um novo dataframe para manter a integridade dos dados\n",
        "df7 = pd.concat([df,agrupamento4],axis=1)\n",
        "df7"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5wwh66BUCLYb",
        "outputId": "56292e4c-42f8-4852-ab46-e00e2305ace7"
      },
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     CustomerID  genero  idade  rendimento  pontuacao  Grupo\n",
              "0             1    Male     19          15         39      5\n",
              "1             2    Male     21          15         81      5\n",
              "2             3  Female     20          16          6      4\n",
              "3             4  Female     23          16         77      0\n",
              "4             5  Female     31          17         40      4\n",
              "..          ...     ...    ...         ...        ...    ...\n",
              "195         196  Female     35         120         79      0\n",
              "196         197  Female     45         126         28      6\n",
              "197         198    Male     32         126         74      2\n",
              "198         199    Male     32         137         18      3\n",
              "199         200    Male     30         137         83      2\n",
              "\n",
              "[200 rows x 6 columns]"
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>Male</td>\n",
              "      <td>21</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
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              "      <th>4</th>\n",
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              "      <td>Female</td>\n",
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              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
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              "      <td>28</td>\n",
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              "    <tr>\n",
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              "      <td>198</td>\n",
              "      <td>Male</td>\n",
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              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
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              "      <td>18</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4b3b00b6-e2b1-4583-ad1a-52452b60b35a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4b3b00b6-e2b1-4583-ad1a-52452b60b35a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Acessando os dados através do índice\n",
        "df7.iloc[50,:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F-igRd_xCSAL",
        "outputId": "c02051c6-7e78-4b72-cf35-d7e05c12562a"
      },
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID        51\n",
              "genero        Female\n",
              "idade             49\n",
              "rendimento        42\n",
              "pontuacao         52\n",
              "Grupo              4\n",
              "Name: 50, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DBSCAN - Density Based Spatial Clustering of Application with Noise\n",
        "\n",
        "DBSCAN, ou traduzindo: Clusterização Espacial Baseada em Densidade de Aplicações com Ruído) é um método de clusterização não paramétrico baseado em densidade, proposto por ESTER et al (1996),  que é significativamente efetivo para  identificar clusters de formato arbitrário e de diferentes tamanhos, identificar e separar os ruídos dos dados e detectar clusters “naturais” e seus arranjos dentro do espaço de dados, sem qualquer informação preliminar sobre os grupos. \n",
        "\n",
        "O método requer somente um parâmetro de entrada, mas dá suporte para determinar um apropriado valor para ele.  "
      ],
      "metadata": {
        "id": "j-ELeINeibkX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DBSCAN com PCA\n",
        "\n",
        "Inicialmente utilizaremos o algoritmo DBSCAN associado ao PCA, reduzindo a quantidade de variáveis para a análise.\n",
        "\n",
        "Usaremos o dataframe df2_esc escalonado."
      ],
      "metadata": {
        "id": "722EyedKjOmu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2_esc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CUUIqKn2jnrL",
        "outputId": "018177ef-05e0-490f-eb19-18a6d50fc4a3"
      },
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [-0.88640526,  0.44136514, -0.25039146,  0.10870037],\n",
              "       [ 1.12815215,  0.08253169, -0.25039146, -0.08540743],\n",
              "       [-0.88640526, -1.13750203, -0.25039146,  0.06987881],\n",
              "       [-0.88640526,  0.7284319 , -0.25039146, -0.3183368 ],\n",
              "       [ 1.12815215,  1.30256542, -0.25039146,  0.03105725],\n",
              "       [ 1.12815215, -0.06100169, -0.25039146,  0.18634349],\n",
              "       [ 1.12815215,  2.02023231, -0.25039146, -0.35715836],\n",
              "       [-0.88640526,  0.51313183, -0.25039146, -0.24069368],\n",
              "       [-0.88640526, -1.28103541, -0.25039146,  0.26398661],\n",
              "       [ 1.12815215,  0.65666521, -0.25039146, -0.16305055],\n",
              "       [-0.88640526,  1.15903204, -0.13588317,  0.30280817],\n",
              "       [-0.88640526, -1.20926872, -0.13588317,  0.18634349],\n",
              "       [-0.88640526, -0.34806844, -0.09771374,  0.38045129],\n",
              "       [-0.88640526,  0.80019859, -0.09771374, -0.16305055],\n",
              "       [-0.88640526,  2.091999  , -0.05954431,  0.18634349],\n",
              "       [ 1.12815215, -1.49633548, -0.05954431, -0.35715836],\n",
              "       [ 1.12815215,  0.65666521, -0.02137488, -0.04658587],\n",
              "       [-0.88640526,  0.08253169, -0.02137488, -0.39597992],\n",
              "       [-0.88640526, -0.49160182, -0.02137488, -0.3183368 ],\n",
              "       [ 1.12815215, -1.06573534, -0.02137488,  0.06987881],\n",
              "       [-0.88640526,  0.58489852, -0.02137488, -0.12422899],\n",
              "       [-0.88640526, -0.85043527, -0.02137488, -0.00776431],\n",
              "       [ 1.12815215,  0.65666521,  0.01679455, -0.3183368 ],\n",
              "       [ 1.12815215, -1.3528021 ,  0.01679455, -0.04658587],\n",
              "       [-0.88640526, -1.13750203,  0.05496398, -0.35715836],\n",
              "       [-0.88640526,  0.7284319 ,  0.05496398, -0.08540743],\n",
              "       [ 1.12815215,  2.02023231,  0.05496398,  0.34162973],\n",
              "       [ 1.12815215, -0.92220196,  0.05496398,  0.18634349],\n",
              "       [ 1.12815215,  0.7284319 ,  0.05496398,  0.22516505],\n",
              "       [-0.88640526, -1.28103541,  0.05496398, -0.3183368 ],\n",
              "       [-0.88640526,  1.94846562,  0.09313341, -0.00776431],\n",
              "       [ 1.12815215,  1.08726535,  0.09313341, -0.16305055],\n",
              "       [ 1.12815215,  2.091999  ,  0.09313341, -0.27951524],\n",
              "       [ 1.12815215,  1.94846562,  0.09313341, -0.08540743],\n",
              "       [ 1.12815215,  1.87669894,  0.09313341,  0.06987881],\n",
              "       [-0.88640526, -1.42456879,  0.09313341,  0.14752193],\n",
              "       [-0.88640526, -0.06100169,  0.13130284, -0.3183368 ],\n",
              "       [ 1.12815215, -1.42456879,  0.13130284, -0.16305055],\n",
              "       [-0.88640526, -1.49633548,  0.16947227, -0.08540743],\n",
              "       [-0.88640526, -1.42456879,  0.16947227, -0.00776431],\n",
              "       [-0.88640526,  1.73316556,  0.16947227, -0.27951524],\n",
              "       [-0.88640526,  0.7284319 ,  0.16947227,  0.34162973],\n",
              "       [-0.88640526,  0.87196528,  0.24581112, -0.27951524],\n",
              "       [-0.88640526,  0.80019859,  0.24581112,  0.26398661],\n",
              "       [ 1.12815215, -0.85043527,  0.24581112,  0.22516505],\n",
              "       [-0.88640526, -0.06100169,  0.24581112, -0.39597992],\n",
              "       [-0.88640526,  0.08253169,  0.32214998,  0.30280817],\n",
              "       [ 1.12815215,  0.010765  ,  0.32214998,  1.58391968],\n",
              "       [-0.88640526, -1.13750203,  0.36031941, -0.82301709],\n",
              "       [-0.88640526, -0.56336851,  0.36031941,  1.04041783],\n",
              "       [ 1.12815215,  0.29783176,  0.39848884, -0.59008772],\n",
              "       [ 1.12815215,  0.08253169,  0.39848884,  1.73920592],\n",
              "       [ 1.12815215,  1.4460988 ,  0.39848884, -1.52180518],\n",
              "       [ 1.12815215, -0.06100169,  0.39848884,  0.96277471],\n",
              "       [ 1.12815215,  0.58489852,  0.39848884, -1.5994483 ],\n",
              "       [ 1.12815215,  0.010765  ,  0.39848884,  0.96277471],\n",
              "       [-0.88640526, -0.99396865,  0.43665827, -0.62890928],\n",
              "       [-0.88640526, -0.56336851,  0.43665827,  0.80748846],\n",
              "       [ 1.12815215, -1.3528021 ,  0.4748277 , -1.75473454],\n",
              "       [-0.88640526, -0.70690189,  0.4748277 ,  1.46745499],\n",
              "       [-0.88640526,  0.36959845,  0.4748277 , -1.67709142],\n",
              "       [ 1.12815215, -0.49160182,  0.4748277 ,  0.88513158],\n",
              "       [ 1.12815215, -1.42456879,  0.51299713, -1.56062674],\n",
              "       [-0.88640526, -0.27630176,  0.51299713,  0.84631002],\n",
              "       [-0.88640526,  1.30256542,  0.55116656, -1.75473454],\n",
              "       [ 1.12815215, -0.49160182,  0.55116656,  1.6615628 ],\n",
              "       [-0.88640526, -0.77866858,  0.58933599, -0.39597992],\n",
              "       [-0.88640526, -0.49160182,  0.58933599,  1.42863343],\n",
              "       [ 1.12815215, -0.99396865,  0.62750542, -1.48298362],\n",
              "       [ 1.12815215, -0.77866858,  0.62750542,  1.81684904],\n",
              "       [ 1.12815215,  0.65666521,  0.62750542, -0.55126616],\n",
              "       [-0.88640526, -0.49160182,  0.62750542,  0.92395314],\n",
              "       [-0.88640526, -0.34806844,  0.66567484, -1.09476801],\n",
              "       [ 1.12815215, -0.34806844,  0.66567484,  1.54509812],\n",
              "       [ 1.12815215,  0.29783176,  0.66567484, -1.28887582],\n",
              "       [ 1.12815215,  0.010765  ,  0.66567484,  1.46745499],\n",
              "       [-0.88640526,  0.36959845,  0.66567484, -1.17241113],\n",
              "       [-0.88640526, -0.06100169,  0.66567484,  1.00159627],\n",
              "       [-0.88640526,  0.58489852,  0.66567484, -1.32769738],\n",
              "       [-0.88640526, -0.85043527,  0.66567484,  1.50627656],\n",
              "       [ 1.12815215, -0.13276838,  0.66567484, -1.91002079],\n",
              "       [-0.88640526, -0.6351352 ,  0.66567484,  1.07923939],\n",
              "       [ 1.12815215, -0.34806844,  0.66567484, -1.91002079],\n",
              "       [-0.88640526, -0.6351352 ,  0.66567484,  0.88513158],\n",
              "       [-0.88640526,  1.23079873,  0.70384427, -0.59008772],\n",
              "       [-0.88640526, -0.70690189,  0.70384427,  1.27334719],\n",
              "       [ 1.12815215, -1.42456879,  0.78018313, -1.75473454],\n",
              "       [-0.88640526, -0.56336851,  0.78018313,  1.6615628 ],\n",
              "       [ 1.12815215,  0.80019859,  0.93286085, -0.93948177],\n",
              "       [-0.88640526, -0.20453507,  0.93286085,  0.96277471],\n",
              "       [ 1.12815215,  0.22606507,  0.97103028, -1.17241113],\n",
              "       [-0.88640526, -0.41983513,  0.97103028,  1.73920592],\n",
              "       [-0.88640526, -0.20453507,  1.00919971, -0.90066021],\n",
              "       [ 1.12815215, -0.49160182,  1.00919971,  0.49691598],\n",
              "       [ 1.12815215,  0.08253169,  1.00919971, -1.44416206],\n",
              "       [ 1.12815215, -0.77866858,  1.00919971,  0.96277471],\n",
              "       [ 1.12815215, -0.20453507,  1.00919971, -1.56062674],\n",
              "       [ 1.12815215, -0.20453507,  1.00919971,  1.62274124],\n",
              "       [-0.88640526,  0.94373197,  1.04736914, -1.44416206],\n",
              "       [-0.88640526, -0.6351352 ,  1.04736914,  1.38981187],\n",
              "       [ 1.12815215,  1.37433211,  1.04736914, -1.36651894],\n",
              "       [ 1.12815215, -0.85043527,  1.04736914,  0.72984534],\n",
              "       [ 1.12815215,  1.4460988 ,  1.23821628, -1.4053405 ],\n",
              "       [ 1.12815215, -0.27630176,  1.23821628,  1.54509812],\n",
              "       [-0.88640526, -0.13276838,  1.390894  , -0.7065524 ],\n",
              "       [-0.88640526, -0.49160182,  1.390894  ,  1.38981187],\n",
              "       [ 1.12815215,  0.51313183,  1.42906343, -1.36651894],\n",
              "       [-0.88640526, -0.70690189,  1.42906343,  1.46745499],\n",
              "       [-0.88640526,  0.15429838,  1.46723286, -0.43480148],\n",
              "       [ 1.12815215, -0.6351352 ,  1.46723286,  1.81684904],\n",
              "       [-0.88640526,  1.08726535,  1.54357172, -1.01712489],\n",
              "       [ 1.12815215, -0.77866858,  1.54357172,  0.69102378],\n",
              "       [-0.88640526,  0.15429838,  1.61991057, -1.28887582],\n",
              "       [-0.88640526, -0.20453507,  1.61991057,  1.35099031],\n",
              "       [-0.88640526, -0.34806844,  1.61991057, -1.05594645],\n",
              "       [-0.88640526, -0.49160182,  1.61991057,  0.72984534],\n",
              "       [ 1.12815215, -0.41983513,  2.00160487, -1.63826986],\n",
              "       [-0.88640526, -0.06100169,  2.00160487,  1.58391968],\n",
              "       [-0.88640526,  0.58489852,  2.26879087, -1.32769738],\n",
              "       [-0.88640526, -0.27630176,  2.26879087,  1.11806095],\n",
              "       [-0.88640526,  0.44136514,  2.49780745, -0.86183865],\n",
              "       [ 1.12815215, -0.49160182,  2.49780745,  0.92395314],\n",
              "       [ 1.12815215, -0.49160182,  2.91767117, -1.25005425],\n",
              "       [ 1.12815215, -0.6351352 ,  2.91767117,  1.27334719]])"
            ]
          },
          "metadata": {},
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2_esc.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kw5zhX_XkXRC",
        "outputId": "0fec88b6-b83e-4ba0-e1d2-4516bf5c1aa6"
      },
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Componentes principais"
      ],
      "metadata": {
        "id": "ILgLxNZTkdt6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA"
      ],
      "metadata": {
        "id": "G4kW8Ra9kgfa"
      },
      "execution_count": 248,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA(n_components=2)"
      ],
      "metadata": {
        "id": "MSmrgOUgk57B"
      },
      "execution_count": 249,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2_pca =pca.fit_transform(df2_esc)"
      ],
      "metadata": {
        "id": "dRT_EdSAlA-z"
      },
      "execution_count": 250,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2_pca"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WadCCh9NlKNB",
        "outputId": "0615112c-bffc-4b4f-985f-f686aaaee562"
      },
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-4.06382715e-01, -5.20713635e-01],\n",
              "       [-1.42767287e+00, -3.67310199e-01],\n",
              "       [ 5.07605706e-02, -1.89406774e+00],\n",
              "       [-1.69451310e+00, -1.63190805e+00],\n",
              "       [-3.13108383e-01, -1.81048272e+00],\n",
              "       [-1.71744627e+00, -1.59926418e+00],\n",
              "       [ 7.90821124e-01, -1.94727112e+00],\n",
              "       [-2.14832159e+00, -1.50537369e+00],\n",
              "       [ 2.77428623e+00, -8.82987672e-01],\n",
              "       [-1.21629477e+00, -1.61640464e+00],\n",
              "       [ 2.62905084e+00, -8.61237043e-01],\n",
              "       [-1.68947038e+00, -1.54542784e+00],\n",
              "       [ 1.68582253e+00, -2.02394479e+00],\n",
              "       [-1.64607339e+00, -1.52251259e+00],\n",
              "       [ 1.17443628e+00, -6.12790961e-01],\n",
              "       [-1.32613070e+00, -2.36719149e-01],\n",
              "       [ 1.67728253e-02, -1.74344572e+00],\n",
              "       [-1.07842454e+00, -2.44715641e-01],\n",
              "       [ 1.48758780e+00, -5.72676028e-01],\n",
              "       [-1.66373169e+00, -1.43259774e+00],\n",
              "       [ 4.88090311e-01, -3.92921145e-01],\n",
              "       [-1.01895051e+00, -1.66247511e-01],\n",
              "       [ 1.35891492e+00, -1.82866936e+00],\n",
              "       [-7.22972722e-01, -1.81687017e-01],\n",
              "       [ 1.51315931e+00, -1.76451196e+00],\n",
              "       [-1.06241157e+00, -4.31150614e-02],\n",
              "       [ 5.88832908e-01, -1.62541614e+00],\n",
              "       [-2.06188227e-01, -1.71906970e-01],\n",
              "       [ 3.68426440e-01, -1.56300645e+00],\n",
              "       [-1.96420414e+00, -1.21211990e+00],\n",
              "       [ 2.54759194e+00, -5.27913522e-01],\n",
              "       [-1.68983067e+00, -1.22412321e+00],\n",
              "       [ 2.20131721e+00, -3.88195647e-01],\n",
              "       [-1.87329695e+00,  2.24855270e-01],\n",
              "       [ 1.26515693e+00, -1.58125854e+00],\n",
              "       [-1.90386557e+00, -1.10444099e+00],\n",
              "       [ 8.39344590e-01, -1.48793867e+00],\n",
              "       [-1.24644437e+00, -1.17425940e+00],\n",
              "       [ 3.02432444e-01, -1.31960089e+00],\n",
              "       [-1.79415868e+00, -1.00420352e+00],\n",
              "       [ 1.49387594e+00, -1.47013252e+00],\n",
              "       [-1.57824777e+00,  3.26252676e-01],\n",
              "       [ 1.09972892e+00, -4.75148304e-02],\n",
              "       [-8.78229151e-01, -1.08373653e+00],\n",
              "       [ 8.90421740e-01, -1.34990772e+00],\n",
              "       [-1.33047664e+00, -1.01561907e+00],\n",
              "       [ 2.19540681e-01, -1.21995587e+00],\n",
              "       [-7.02592333e-01, -1.08085693e+00],\n",
              "       [-4.70519161e-01, -1.11577393e+00],\n",
              "       [-3.71782513e-01, -1.13065684e+00],\n",
              "       [ 2.49709965e-01, -1.16611641e+00],\n",
              "       [-2.81507713e-01,  2.47898317e-01],\n",
              "       [-6.92486630e-01, -9.94947343e-01],\n",
              "       [ 1.00183656e+00,  8.36296961e-02],\n",
              "       [ 4.85517270e-01, -1.17239636e+00],\n",
              "       [ 9.16095461e-01,  9.67976267e-02],\n",
              "       [ 4.01316916e-01, -1.13059450e+00],\n",
              "       [ 1.86862991e+00, -1.76710825e-02],\n",
              "       [-8.10654470e-01, -8.89574289e-01],\n",
              "       [ 1.07827242e+00,  1.59810666e-01],\n",
              "       [ 1.65086088e+00,  7.33740960e-02],\n",
              "       [-8.40256337e-01,  4.48881521e-01],\n",
              "       [ 1.13717903e+00, -1.15401647e+00],\n",
              "       [ 3.08719686e-01, -1.02922982e+00],\n",
              "       [ 1.43815483e+00,  1.63848653e-01],\n",
              "       [-9.96758186e-01,  5.30768704e-01],\n",
              "       [ 5.44171232e-03, -9.54225943e-01],\n",
              "       [ 1.29298442e+00, -1.14827597e+00],\n",
              "       [-9.47389862e-01,  5.23327249e-01],\n",
              "       [-4.57607937e-01, -8.84390383e-01],\n",
              "       [ 1.67683173e+00,  1.56994962e-01],\n",
              "       [ 4.16021294e-01, -9.86837080e-01],\n",
              "       [ 8.70906219e-01, -1.02631905e+00],\n",
              "       [ 6.84235085e-01, -9.98271327e-01],\n",
              "       [ 1.34595784e+00,  3.52842574e-01],\n",
              "       [-4.69867989e-01,  6.26458338e-01],\n",
              "       [ 2.27835275e-02, -7.81833032e-01],\n",
              "       [ 3.81292376e-01,  4.98237051e-01],\n",
              "       [-1.03665230e+00, -6.22127824e-01],\n",
              "       [ 5.13597177e-01, -8.55673851e-01],\n",
              "       [ 1.14055197e+00,  3.83752756e-01],\n",
              "       [ 9.58845944e-02,  5.41167687e-01],\n",
              "       [ 1.90090826e+00,  2.69270021e-01],\n",
              "       [ 3.12157595e-01, -8.25335848e-01],\n",
              "       [-1.26872547e+00, -5.87210823e-01],\n",
              "       [ 8.29573578e-01,  4.30691769e-01],\n",
              "       [ 3.82433784e-01, -7.48585816e-01],\n",
              "       [-1.16671899e+00, -5.15038230e-01],\n",
              "       [-7.07867782e-01, -5.55092382e-01],\n",
              "       [ 4.55367669e-01, -7.30251123e-01],\n",
              "       [ 1.10375960e+00, -7.98926738e-01],\n",
              "       [-5.19300383e-01,  7.79947493e-01],\n",
              "       [ 7.48178745e-01,  6.17967591e-01],\n",
              "       [ 1.21223952e-01, -6.21459016e-01],\n",
              "       [-3.27057250e-01, -5.53913735e-01],\n",
              "       [-5.16662945e-01,  8.08582982e-01],\n",
              "       [ 2.80131086e-01, -6.45501482e-01],\n",
              "       [-7.87237308e-01, -4.84651914e-01],\n",
              "       [ 9.34617726e-01,  6.19129095e-01],\n",
              "       [-6.34366480e-01,  8.55537580e-01],\n",
              "       [-7.45169167e-01, -4.32528995e-01],\n",
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              "       [ 1.22953909e+00,  4.07829870e-01],\n",
              "       [-7.55384802e-01,  2.04050460e+00],\n",
              "       [ 7.73957706e-01,  5.34939526e-01],\n",
              "       [-1.28626064e+00,  8.44610416e-01],\n",
              "       [ 2.68375609e-01,  6.11070624e-01],\n",
              "       [-1.05705706e+00,  8.10267154e-01],\n",
              "       [ 1.08870927e+00,  2.11339899e+00],\n",
              "       [-1.34984935e+00,  1.14586069e+00],\n",
              "       [ 1.09288835e+00,  9.82840852e-01],\n",
              "       [-1.17957174e+00,  1.32456784e+00],\n",
              "       [ 6.72751128e-01,  1.22106095e+00],\n",
              "       [-7.23719161e-01,  2.76501038e+00],\n",
              "       [ 7.67096226e-01,  2.86193009e+00],\n",
              "       [-1.06501524e+00,  3.13725616e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 251
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import DBSCAN"
      ],
      "metadata": {
        "id": "caZ7rlSUlPiq"
      },
      "execution_count": 252,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dbscan = DBSCAN(eps = 0.32, min_samples= 3)"
      ],
      "metadata": {
        "id": "jryuD7OglW9B"
      },
      "execution_count": 253,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dbscan.fit(df2_pca)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8d0oP1yrlsMk",
        "outputId": "c18b24da-890b-453c-f19e-a57f75b112eb"
      },
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DBSCAN(eps=0.32, min_samples=3)"
            ]
          },
          "metadata": {},
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classificacao6 = dbscan.labels_"
      ],
      "metadata": {
        "id": "9rDZqZd_l5o5"
      },
      "execution_count": 255,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classificacao6"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ezkRdvMwmA8L",
        "outputId": "c5a98ac6-02ac-4c73-ad47-60cf41359bd7"
      },
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  0, -1,  1, -1,  1, -1, -1, -1, -1, -1,  1,  3,  1,  2,  0, -1,\n",
              "        0,  2,  1,  0,  0,  3,  0,  3,  0,  0,  0,  0,  1, -1,  1, -1,  0,\n",
              "        3,  1,  0, -1,  0,  1,  3,  0,  2,  4,  0, -1,  0,  4,  4,  4,  0,\n",
              "       -1,  4,  2,  0,  2,  0,  2,  4,  2,  2,  0,  0,  0,  2,  0,  0,  0,\n",
              "        0,  4,  2,  0,  0,  0,  2,  0,  0,  2,  0,  0,  2,  2,  2,  0,  0,\n",
              "        2,  0,  0,  0,  0,  2,  0,  2,  0,  0,  0,  0,  0,  2,  0,  0,  0,\n",
              "        2,  0,  2,  0,  2,  2,  2,  2,  2,  0,  0,  0,  0,  0,  2,  0,  0,\n",
              "        0,  0,  0,  0,  0,  0,  0,  2,  0, -1,  0,  2,  0,  0,  0,  5,  0,\n",
              "        2,  0,  5,  0, -1,  0,  0,  0,  5, -1,  2,  0,  2,  0,  2,  0,  2,\n",
              "        0,  2,  0,  2,  0,  2,  0,  2,  0,  5,  0,  2,  0,  2,  0,  2, -1,\n",
              "        2,  0,  2,  0,  2,  0, -1,  0, -1,  0,  2,  0, -1,  0,  2, -1,  2,\n",
              "        0,  2,  0,  2,  0, -1,  0,  2,  0,  5, -1, -1, -1])"
            ]
          },
          "metadata": {},
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "É possível identificar uma quantidade considerável de ruídos ( -1 ) na criação do modelo utilizando PCA. Ajustando o eps que define o raio de seleção do cluster ( default = 0.5 ) para valores maiores cria uma predominância do cluster 0. Encontramos um maior equilíbrio com eps = 0.32, mesmo com tantos ruídos."
      ],
      "metadata": {
        "id": "I56ji8Tcockz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "nmVK0Tz5mPGY"
      },
      "execution_count": 257,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graf5 = px.scatter( x = df2_pca[:,0], y = df2_pca[:,1], color = classificacao6)\n",
        "graf5.update_layout( width=800, height = 500, title_text=\"Agrupamento DBSCAN\")\n",
        "graf5.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "WJ4JFPVqmP7Q",
        "outputId": "32f86099-b7ec-4824-f815-9c4463c2278e"
      },
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        "agrupamento5 = pd.DataFrame(classificacao6, columns=[\"Grupo\"])"
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              "     Grupo\n",
              "0        0\n",
              "1        0\n",
              "2       -1\n",
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              "4       -1\n",
              "..     ...\n",
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "Juntando o atributo Grupo ao dataframe original. Será criado um novo dataframe para manter a integridade dos dados"
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      "metadata": {
        "id": "EozfDHmkpokV"
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        "df8 = pd.concat([df,agrupamento5],axis=1)\n",
        "df8"
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      "execution_count": 261,
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              "     CustomerID  genero  idade  rendimento  pontuacao  Grupo\n",
              "0             1    Male     19          15         39      0\n",
              "1             2    Male     21          15         81      0\n",
              "2             3  Female     20          16          6     -1\n",
              "3             4  Female     23          16         77      1\n",
              "4             5  Female     31          17         40     -1\n",
              "..          ...     ...    ...         ...        ...    ...\n",
              "195         196  Female     35         120         79      0\n",
              "196         197  Female     45         126         28      5\n",
              "197         198    Male     32         126         74     -1\n",
              "198         199    Male     32         137         18     -1\n",
              "199         200    Male     30         137         83     -1\n",
              "\n",
              "[200 rows x 6 columns]"
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "      <td>-1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>120</td>\n",
              "      <td>79</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>Female</td>\n",
              "      <td>45</td>\n",
              "      <td>126</td>\n",
              "      <td>28</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>126</td>\n",
              "      <td>74</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>137</td>\n",
              "      <td>18</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25b86e4f-1a74-4abf-905f-6cbd8f742cc3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "\n",
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              "          const element = document.querySelector('#df-25b86e4f-1a74-4abf-905f-6cbd8f742cc3');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DBSCAN: Análise com todos os atributos "
      ],
      "metadata": {
        "id": "vkcaqsynqeME"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Configurando o DBSCAN\n",
        "dbscan = DBSCAN(eps = 0.73, min_samples= 4)"
      ],
      "metadata": {
        "id": "p7DX9Hqe3Vyc"
      },
      "execution_count": 287,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Para análise com todos os atributos escalonados utilizaremos o dataframe df2_esc\n",
        "dbscan.fit(df2_esc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftUfxhcl3bEh",
        "outputId": "88bccab7-1227-4cf5-aff3-ebf248f76c48"
      },
      "execution_count": 284,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DBSCAN(eps=0.75, min_samples=4)"
            ]
          },
          "metadata": {},
          "execution_count": 284
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classificacao7 = dbscan.labels_"
      ],
      "metadata": {
        "id": "xhe6-YIl3rT6"
      },
      "execution_count": 285,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classificacao7"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8HOnt0u4mer",
        "outputId": "bcb111b2-ec92-4838-873d-c40856e8bcad"
      },
      "execution_count": 286,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1,  0, -1,  1,  1,  1, -1,  1, -1,  1, -1, -1,  1,  1, -1,  0,  1,\n",
              "        0,  2, -1, -1,  0,  1,  0,  1,  0,  1,  0,  1,  1, -1,  1, -1, -1,\n",
              "        1,  1,  1,  1,  1,  1,  1,  0,  2,  1,  1,  1,  1,  1,  1,  1,  1,\n",
              "        3,  1,  2,  1,  2,  1,  2,  1,  2,  2,  3,  1,  1,  2,  3,  1,  1,\n",
              "        3,  1,  2,  1,  1,  1,  2,  3,  1,  2,  1,  1,  2,  2,  2,  1,  1,\n",
              "        2,  1,  1,  1,  1,  1,  3,  2,  1,  1,  3,  1,  1,  2,  3,  1,  1,\n",
              "        2,  3,  2,  1,  1,  2,  2,  2,  2,  1,  1,  3,  1,  1,  1,  1,  1,\n",
              "        1,  3,  1,  1,  4,  1,  1,  2,  4,  2,  4,  2,  4,  1,  1,  5,  1,\n",
              "        6,  4,  5,  1,  6,  4,  1,  1,  5,  4,  2,  1,  6,  4,  2,  4,  6,\n",
              "        1,  6,  1,  2,  1,  2,  1,  1,  1,  5,  1,  2,  1,  2,  1,  6,  4,\n",
              "        2,  4,  2,  4,  6,  1,  2,  4,  2,  4,  6,  1,  2,  1,  6,  4,  6,\n",
              "        4,  6,  1,  6,  1, -1,  1, -1,  1, -1, -1, -1, -1])"
            ]
          },
          "metadata": {},
          "execution_count": 286
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para a análise DBSCAN com todos os atributos, o valor mais adquado para o eps foi 0.73, onde foi possivel identificar uma baixa demostração de ruídos ( valores -1 )."
      ],
      "metadata": {
        "id": "uvybdXuN6iGg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agrupamento"
      ],
      "metadata": {
        "id": "g3CkLz3-6Qri"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "agrupamento6 = pd.DataFrame(classificacao7, columns=[\"Grupo\"])"
      ],
      "metadata": {
        "id": "trN6EqwB7DsF"
      },
      "execution_count": 288,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "agrupamento6"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "I2bezo2_7MXM",
        "outputId": "61e4881c-838d-4f9b-9ddc-2ce6f8d0833a"
      },
      "execution_count": 289,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Grupo\n",
              "0       -1\n",
              "1        0\n",
              "2       -1\n",
              "3        1\n",
              "4        1\n",
              "..     ...\n",
              "195      1\n",
              "196     -1\n",
              "197     -1\n",
              "198     -1\n",
              "199     -1\n",
              "\n",
              "[200 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e85b202-ab9a-4278-b182-8d24ab4826da\">\n",
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              "      <th>4</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>-1</td>\n",
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              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>-1</td>\n",
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              "  </tbody>\n",
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              "<p>200 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e85b202-ab9a-4278-b182-8d24ab4826da')\"\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Juntando o atributo Grupo ao dataframe original. Será criado um novo dataframe para manter a integridade dos dados"
      ],
      "metadata": {
        "id": "p7JFhpV-7UxI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df9 = pd.concat([df,agrupamento6],axis=1)\n",
        "df9"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "UrLs2LQ37VlA",
        "outputId": "f40aee9f-5930-4a0d-c32e-8f613ae1f15c"
      },
      "execution_count": 290,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     CustomerID  genero  idade  rendimento  pontuacao  Grupo\n",
              "0             1    Male     19          15         39     -1\n",
              "1             2    Male     21          15         81      0\n",
              "2             3  Female     20          16          6     -1\n",
              "3             4  Female     23          16         77      1\n",
              "4             5  Female     31          17         40      1\n",
              "..          ...     ...    ...         ...        ...    ...\n",
              "195         196  Female     35         120         79      1\n",
              "196         197  Female     45         126         28     -1\n",
              "197         198    Male     32         126         74     -1\n",
              "198         199    Male     32         137         18     -1\n",
              "199         200    Male     30         137         83     -1\n",
              "\n",
              "[200 rows x 6 columns]"
            ],
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>genero</th>\n",
              "      <th>idade</th>\n",
              "      <th>rendimento</th>\n",
              "      <th>pontuacao</th>\n",
              "      <th>Grupo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>120</td>\n",
              "      <td>79</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>Female</td>\n",
              "      <td>45</td>\n",
              "      <td>126</td>\n",
              "      <td>28</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>126</td>\n",
              "      <td>74</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>137</td>\n",
              "      <td>18</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-069d10a1-7254-4af1-b3df-97f497c45a96')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-069d10a1-7254-4af1-b3df-97f497c45a96 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-069d10a1-7254-4af1-b3df-97f497c45a96');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 290
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Acessando os dados através do índice\n",
        "df9.iloc[120,:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfUy0Oas7jYF",
        "outputId": "d15ac500-7ea8-4ac4-b1a8-213d84c9ca39"
      },
      "execution_count": 291,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID     121\n",
              "genero        Male\n",
              "idade           27\n",
              "rendimento      67\n",
              "pontuacao       56\n",
              "Grupo            3\n",
              "Name: 120, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 291
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando um dataframe com os ruídos ( valores -1 )\n",
        "ruido = df9.loc [ df9.Grupo == -1]\n",
        "ruido"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "xJtL3-Ww75n4",
        "outputId": "fe551b82-a016-4a11-fcff-231b6d45c8a5"
      },
      "execution_count": 292,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     CustomerID  genero  idade  rendimento  pontuacao  Grupo\n",
              "0             1    Male     19          15         39     -1\n",
              "2             3  Female     20          16          6     -1\n",
              "6             7  Female     35          18          6     -1\n",
              "8             9    Male     64          19          3     -1\n",
              "10           11    Male     67          19         14     -1\n",
              "11           12  Female     35          19         99     -1\n",
              "14           15    Male     37          20         13     -1\n",
              "19           20  Female     35          23         98     -1\n",
              "20           21    Male     35          24         35     -1\n",
              "30           31    Male     60          30          4     -1\n",
              "32           33    Male     53          33          4     -1\n",
              "33           34    Male     18          33         92     -1\n",
              "192         193    Male     33         113          8     -1\n",
              "194         195  Female     47         120         16     -1\n",
              "196         197  Female     45         126         28     -1\n",
              "197         198    Male     32         126         74     -1\n",
              "198         199    Male     32         137         18     -1\n",
              "199         200    Male     30         137         83     -1"
            ],
            "text/html": [
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              "      <td>Female</td>\n",
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              "      <td>Male</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bfe93528-db12-4cf9-8139-b8c552bce768');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 292
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando a quantidade de elementos de ruído\n",
        "ruido.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fVx87Iom8QUL",
        "outputId": "127dadab-5cee-41ec-be6a-9846ef5f5e00"
      },
      "execution_count": 293,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 293
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "18 elementos e 6 colunas"
      ],
      "metadata": {
        "id": "OfAmF7Vs8YOo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "puJITpBd8jJw"
      }
    }
  ]
}