Clustering_Extraindo_Padroes.ipynb

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      "name": "Clustering_Extraindo_Padroes.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbiNQu-chtvj"
      },
      "source": [
        "# Aula 01:\n",
        "\n",
        "Neste módulo foi abordado o conceito de aprendizado de máquina supervisionado e não supervisionado. Foi levado em consideração os labels (ou rótulos) e discutida a diferença entre classificação e clusterização.\n",
        "\n",
        "O risco é uma variável alvo do problema em um caso de questão de dados. \n",
        "\n",
        "Clusterização é um algoritmo não supervisionado."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PHGt5XeChtvq"
      },
      "source": [
        "# Aula 02:\n",
        "\n",
        "Obtendo os clusters. \n",
        "\n",
        "Endereço do dataset: https://raw.githubusercontent.com/alura-cursos/alura-clustering-validation/base-de-dados/CC%20GENERAL.csv"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3e6JHgQhtvs"
      },
      "source": [
        "import pandas as pd\n",
        "import scipy as scp\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dxQyPZXdhtvt"
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/alura-cursos/alura-clustering-validation/base-de-dados/CC%20GENERAL.csv'\n",
        "df = pd.read_csv(url)"
      ],
      "execution_count": null,
      "outputs": []
    },
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          "height": 320
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        "outputId": "a35a6ed9-43b7-4230-f8fe-2c786ed7843f"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BALANCE</th>\n",
              "      <th>BALANCE_FREQUENCY</th>\n",
              "      <th>PURCHASES</th>\n",
              "      <th>ONEOFF_PURCHASES</th>\n",
              "      <th>INSTALLMENTS_PURCHASES</th>\n",
              "      <th>CASH_ADVANCE</th>\n",
              "      <th>PURCHASES_FREQUENCY</th>\n",
              "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
              "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_TRX</th>\n",
              "      <th>PURCHASES_TRX</th>\n",
              "      <th>CREDIT_LIMIT</th>\n",
              "      <th>PAYMENTS</th>\n",
              "      <th>MINIMUM_PAYMENTS</th>\n",
              "      <th>PRC_FULL_PAYMENT</th>\n",
              "      <th>TENURE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8949.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8637.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1564.474828</td>\n",
              "      <td>0.877271</td>\n",
              "      <td>1003.204834</td>\n",
              "      <td>592.437371</td>\n",
              "      <td>411.067645</td>\n",
              "      <td>978.871112</td>\n",
              "      <td>0.490351</td>\n",
              "      <td>0.202458</td>\n",
              "      <td>0.364437</td>\n",
              "      <td>0.135144</td>\n",
              "      <td>3.248827</td>\n",
              "      <td>14.709832</td>\n",
              "      <td>4494.449450</td>\n",
              "      <td>1733.143852</td>\n",
              "      <td>864.206542</td>\n",
              "      <td>0.153715</td>\n",
              "      <td>11.517318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2081.531879</td>\n",
              "      <td>0.236904</td>\n",
              "      <td>2136.634782</td>\n",
              "      <td>1659.887917</td>\n",
              "      <td>904.338115</td>\n",
              "      <td>2097.163877</td>\n",
              "      <td>0.401371</td>\n",
              "      <td>0.298336</td>\n",
              "      <td>0.397448</td>\n",
              "      <td>0.200121</td>\n",
              "      <td>6.824647</td>\n",
              "      <td>24.857649</td>\n",
              "      <td>3638.815725</td>\n",
              "      <td>2895.063757</td>\n",
              "      <td>2372.446607</td>\n",
              "      <td>0.292499</td>\n",
              "      <td>1.338331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.019163</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>128.281915</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>39.635000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1600.000000</td>\n",
              "      <td>383.276166</td>\n",
              "      <td>169.123707</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>873.385231</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>361.280000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>856.901546</td>\n",
              "      <td>312.343947</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2054.140036</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1110.130000</td>\n",
              "      <td>577.405000</td>\n",
              "      <td>468.637500</td>\n",
              "      <td>1113.821139</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>6500.000000</td>\n",
              "      <td>1901.134317</td>\n",
              "      <td>825.485459</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>19043.138560</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>49039.570000</td>\n",
              "      <td>40761.250000</td>\n",
              "      <td>22500.000000</td>\n",
              "      <td>47137.211760</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>358.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>50721.483360</td>\n",
              "      <td>76406.207520</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "            BALANCE  BALANCE_FREQUENCY  ...  PRC_FULL_PAYMENT       TENURE\n",
              "count   8950.000000        8950.000000  ...       8950.000000  8950.000000\n",
              "mean    1564.474828           0.877271  ...          0.153715    11.517318\n",
              "std     2081.531879           0.236904  ...          0.292499     1.338331\n",
              "min        0.000000           0.000000  ...          0.000000     6.000000\n",
              "25%      128.281915           0.888889  ...          0.000000    12.000000\n",
              "50%      873.385231           1.000000  ...          0.000000    12.000000\n",
              "75%     2054.140036           1.000000  ...          0.142857    12.000000\n",
              "max    19043.138560           1.000000  ...          1.000000    12.000000\n",
              "\n",
              "[8 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 4
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      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8950 entries, 0 to 8949\n",
            "Data columns (total 18 columns):\n",
            " #   Column                            Non-Null Count  Dtype  \n",
            "---  ------                            --------------  -----  \n",
            " 0   CUST_ID                           8950 non-null   object \n",
            " 1   BALANCE                           8950 non-null   float64\n",
            " 2   BALANCE_FREQUENCY                 8950 non-null   float64\n",
            " 3   PURCHASES                         8950 non-null   float64\n",
            " 4   ONEOFF_PURCHASES                  8950 non-null   float64\n",
            " 5   INSTALLMENTS_PURCHASES            8950 non-null   float64\n",
            " 6   CASH_ADVANCE                      8950 non-null   float64\n",
            " 7   PURCHASES_FREQUENCY               8950 non-null   float64\n",
            " 8   ONEOFF_PURCHASES_FREQUENCY        8950 non-null   float64\n",
            " 9   PURCHASES_INSTALLMENTS_FREQUENCY  8950 non-null   float64\n",
            " 10  CASH_ADVANCE_FREQUENCY            8950 non-null   float64\n",
            " 11  CASH_ADVANCE_TRX                  8950 non-null   int64  \n",
            " 12  PURCHASES_TRX                     8950 non-null   int64  \n",
            " 13  CREDIT_LIMIT                      8949 non-null   float64\n",
            " 14  PAYMENTS                          8950 non-null   float64\n",
            " 15  MINIMUM_PAYMENTS                  8637 non-null   float64\n",
            " 16  PRC_FULL_PAYMENT                  8950 non-null   float64\n",
            " 17  TENURE                            8950 non-null   int64  \n",
            "dtypes: float64(14), int64(3), object(1)\n",
            "memory usage: 1.2+ MB\n"
          ],
          "name": "stdout"
        }
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    {
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      "metadata": {
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          "height": 226
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        "id": "XrfcUmydhtvw",
        "outputId": "9d8bfb34-05d0-461d-95b9-fc26c0109ddc"
      },
      "source": [
        "#Retirando colunas não desejadas\n",
        "df.drop(columns=['CUST_ID','TENURE'],inplace=True)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>BALANCE</th>\n",
              "      <th>BALANCE_FREQUENCY</th>\n",
              "      <th>PURCHASES</th>\n",
              "      <th>ONEOFF_PURCHASES</th>\n",
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              "      <th>CREDIT_LIMIT</th>\n",
              "      <th>PAYMENTS</th>\n",
              "      <th>MINIMUM_PAYMENTS</th>\n",
              "      <th>PRC_FULL_PAYMENT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40.900749</td>\n",
              "      <td>0.818182</td>\n",
              "      <td>95.40</td>\n",
              "      <td>0.00</td>\n",
              "      <td>95.4</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>201.802084</td>\n",
              "      <td>139.509787</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3202.467416</td>\n",
              "      <td>0.909091</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6442.945483</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>4103.032597</td>\n",
              "      <td>1072.340217</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2495.148862</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>773.17</td>\n",
              "      <td>773.17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>622.066742</td>\n",
              "      <td>627.284787</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1666.670542</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>1499.00</td>\n",
              "      <td>1499.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>205.788017</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>817.714335</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>16.00</td>\n",
              "      <td>16.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>678.334763</td>\n",
              "      <td>244.791237</td>\n",
              "      <td>0.000000</td>\n",
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            "text/plain": [
              "       BALANCE  BALANCE_FREQUENCY  ...  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT\n",
              "0    40.900749           0.818182  ...        139.509787          0.000000\n",
              "1  3202.467416           0.909091  ...       1072.340217          0.222222\n",
              "2  2495.148862           1.000000  ...        627.284787          0.000000\n",
              "3  1666.670542           0.636364  ...               NaN          0.000000\n",
              "4   817.714335           1.000000  ...        244.791237          0.000000\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
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        "id": "_Ato4F0bhtvw",
        "outputId": "046d9f5f-9db8-426f-a0c3-568aaa482c75"
      },
      "source": [
        "#Retirando dados faltantes\n",
        "missing = df.isna().sum()\n",
        "print(missing)\n",
        "#Faltam 313 dados no Minimum_Payments"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BALANCE                               0\n",
            "BALANCE_FREQUENCY                     0\n",
            "PURCHASES                             0\n",
            "ONEOFF_PURCHASES                      0\n",
            "INSTALLMENTS_PURCHASES                0\n",
            "CASH_ADVANCE                          0\n",
            "PURCHASES_FREQUENCY                   0\n",
            "ONEOFF_PURCHASES_FREQUENCY            0\n",
            "PURCHASES_INSTALLMENTS_FREQUENCY      0\n",
            "CASH_ADVANCE_FREQUENCY                0\n",
            "CASH_ADVANCE_TRX                      0\n",
            "PURCHASES_TRX                         0\n",
            "CREDIT_LIMIT                          1\n",
            "PAYMENTS                              0\n",
            "MINIMUM_PAYMENTS                    313\n",
            "PRC_FULL_PAYMENT                      0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "am9RZjSThtvx"
      },
      "source": [
        "df.fillna(df.median(),inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ur_6uuhJhtvy",
        "outputId": "5c1115b2-8974-40eb-b8ce-01ddc75a1282"
      },
      "source": [
        "missing = df.isna().sum()\n",
        "print(missing)\n",
        "#Todos os dados foram preenchidos com a mediana"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BALANCE                             0\n",
            "BALANCE_FREQUENCY                   0\n",
            "PURCHASES                           0\n",
            "ONEOFF_PURCHASES                    0\n",
            "INSTALLMENTS_PURCHASES              0\n",
            "CASH_ADVANCE                        0\n",
            "PURCHASES_FREQUENCY                 0\n",
            "ONEOFF_PURCHASES_FREQUENCY          0\n",
            "PURCHASES_INSTALLMENTS_FREQUENCY    0\n",
            "CASH_ADVANCE_FREQUENCY              0\n",
            "CASH_ADVANCE_TRX                    0\n",
            "PURCHASES_TRX                       0\n",
            "CREDIT_LIMIT                        0\n",
            "PAYMENTS                            0\n",
            "MINIMUM_PAYMENTS                    0\n",
            "PRC_FULL_PAYMENT                    0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJDdTNRwhtvy"
      },
      "source": [
        "from sklearn.preprocessing import Normalizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Mu2hZ9Rhtv0"
      },
      "source": [
        "values = Normalizer().fit_transform(df.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_M3T_olchtv0"
      },
      "source": [
        "from sklearn.cluster import KMeans"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-sV9937htv1"
      },
      "source": [
        "kmeans = KMeans(n_clusters=5,n_init=10,max_iter=300)\n",
        "y_pred = kmeans.fit_predict(values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OGhzd_yShtv1"
      },
      "source": [
        "# Aula 03:\n",
        "\n",
        "Validando e acompanhando o desempenho dos clusters.\n",
        "\n",
        "## Métricas de Validação:\n",
        "\n",
        "- Externas: precisamos ter os labels\n",
        "- Internas: independentes dos labels\n",
        "\n",
        "## Critérios de Validação:\n",
        "\n",
        "1. Compactação: Quão próximos estãos os pontos em um mesmo cluster.\n",
        "2. Separação: Quão bem separados estão os pontos em clusters diferentes.\n",
        "\n",
        "## Medida bastante utilizada: Método de Silhoutte\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbThfsavhtv1",
        "outputId": "5b68658a-5ffd-472f-fcd5-aad491bd48b6"
      },
      "source": [
        "from sklearn import metrics\n",
        "labels = kmeans.labels_\n",
        "silhouette = metrics.silhouette_score(values, labels, metric='euclidean')\n",
        "print(silhouette)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.36457606314711744\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UdH4LKkDhtv1"
      },
      "source": [
        "Não consegui rodar o método silhoutte_score no meu PC. Rodei o notebook no colab para ganhar velocidade nos estudos. O resultado deste método foi 0.364439277430815."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYNWrAbthtv2"
      },
      "source": [
        "# Aula 04:\n",
        "\n",
        "Compreendendo os parâmetros de Davies Bouldin. O índice foca no centróide ao invés dos pontos. É muito com o método de Silhoutte.\n",
        "$$\n",
        "R _{ij} = \\frac{(S_i+S_j)}{D_{ij}}\n",
        "$$\n",
        "\n",
        "$$\n",
        "\n",
        "DBI = \\frac{1}{N} * \\sum{}_{i=1}^{N} (max_{i!=j}   R _{ij})\n",
        "\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLVS00fdhtv3",
        "outputId": "5bbb7bb7-c3e6-4a30-e284-5eea727ae34d"
      },
      "source": [
        "dbs = metrics.davies_bouldin_score(values,labels)\n",
        "print(dbs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0749530537541763\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "py04UTrahtv3"
      },
      "source": [
        "# Aula 05: \n",
        "\n",
        "Índice de Calinski-Harabasz leva a dispersão dos pontos dentro dos clusters. Assim como o Davies Bouldin, ele compara a semelhança e a separação.\n",
        "\n",
        "https://www.geeksforgeeks.org/calinski-harabasz-index-cluster-validity-indices-set-3/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iyojsbCJhtv3",
        "outputId": "f397f606-4095-4993-ed3a-6ab0d055bd56"
      },
      "source": [
        "ch = metrics.calinski_harabasz_score(values,labels)\n",
        "print(ch)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3431.58002074861\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rboIzvhZhtv4"
      },
      "source": [
        "# Aula 06:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82vyIvJehtv4"
      },
      "source": [
        "def clustering_algorithm(n_clusters,dataset):\n",
        "    kmeans = KMeans(n_clusters=n_clusters,n_init=10,max_iter=300)\n",
        "    labels = kmeans.fit_predict(dataset)\n",
        "    s = metrics.silhouette_score(dataset,labels,metric='manhattan')\n",
        "    # No curso se utiliza a metrica euclidean. Decidi pela manhattan para o código rodar mais rápido\n",
        "    d = metrics.davies_bouldin_score(dataset,labels)\n",
        "    c = metrics.calinski_harabasz_score(dataset,labels)\n",
        "    # print(\"Silhoutte:\",s,\"Davies-Boldin\",d,\"Calinski-Harabasz\",c)\n",
        "    return s,d,c"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHtHFhcqhtv4",
        "outputId": "db424329-8f58-4b41-bb09-7193e9134f3e"
      },
      "source": [
        "clustering_algorithm(3,values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.3287444078014163, 1.3093507662143316, 3526.4668344515403)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otmpen-rhtv4",
        "outputId": "06f16359-67bd-4e10-83df-3be08d14521f"
      },
      "source": [
        "clustering_algorithm(5,values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.3645333662323694, 1.0756224069224563, 3431.798945679164)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FoXymvnShtv5",
        "outputId": "de376572-303b-45c2-d067-7caa08c5bb2a"
      },
      "source": [
        "clustering_algorithm(10,values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.29606011798147674, 1.154911857762088, 3017.5053287394276)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lVDea6OFhtv5",
        "outputId": "df66baee-ec22-4526-9955-555cdec47c60"
      },
      "source": [
        "random_data = np.random.rand(8950,16)\n",
        "clustering_algorithm(5,random_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.04699727637831865, 3.5330286685671424, 300.74798770231456)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EI5-Oj5khtv5"
      },
      "source": [
        "# Aula 07:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "lHnnZyKyhtv6",
        "outputId": "275764e5-eb93-4932-90a3-b003cf8c59d3"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(df['PURCHASES'],df['PAYMENTS'],c=labels,s=5,cmap='rainbow')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f017de6e5d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8jMoY5gj75Y"
      },
      "source": [
        "df['cluster'] = labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AOSvHKfHju5M"
      },
      "source": [
        "# Aula 08:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "55_YkGx2jlSH",
        "outputId": "9d1d158f-e025-41ef-dadf-0ec52954ab32"
      },
      "source": [
        "df.groupby(\"cluster\").describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">BALANCE</th>\n",
              "      <th colspan=\"8\" halign=\"left\">BALANCE_FREQUENCY</th>\n",
              "      <th colspan=\"8\" halign=\"left\">PURCHASES</th>\n",
              "      <th colspan=\"8\" halign=\"left\">ONEOFF_PURCHASES</th>\n",
              "      <th colspan=\"8\" halign=\"left\">INSTALLMENTS_PURCHASES</th>\n",
              "      <th>...</th>\n",
              "      <th colspan=\"8\" halign=\"left\">PURCHASES_TRX</th>\n",
              "      <th colspan=\"8\" halign=\"left\">CREDIT_LIMIT</th>\n",
              "      <th colspan=\"8\" halign=\"left\">PAYMENTS</th>\n",
              "      <th colspan=\"8\" halign=\"left\">MINIMUM_PAYMENTS</th>\n",
              "      <th colspan=\"8\" halign=\"left\">PRC_FULL_PAYMENT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>...</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3284.0</td>\n",
              "      <td>445.102105</td>\n",
              "      <td>847.653849</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>23.396562</td>\n",
              "      <td>83.076264</td>\n",
              "      <td>412.596088</td>\n",
              "      <td>9630.367575</td>\n",
              "      <td>3284.0</td>\n",
              "      <td>0.774331</td>\n",
              "      <td>0.301681</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3284.0</td>\n",
              "      <td>628.639333</td>\n",
              "      <td>758.786483</td>\n",
              "      <td>0.00</td>\n",
              "      <td>133.0000</td>\n",
              "      <td>362.435</td>\n",
              "      <td>833.4625</td>\n",
              "      <td>6407.67</td>\n",
              "      <td>3284.0</td>\n",
              "      <td>313.849071</td>\n",
              "      <td>566.206218</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>26.325</td>\n",
              "      <td>406.835</td>\n",
              "      <td>6300.40</td>\n",
              "      <td>3284.0</td>\n",
              "      <td>315.124741</td>\n",
              "      <td>440.372610</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>166.505</td>\n",
              "      <td>427.7475</td>\n",
              "      <td>4116.00</td>\n",
              "      <td>...</td>\n",
              "      <td>3284.0</td>\n",
              "      <td>11.940317</td>\n",
              "      <td>14.074151</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>232.0</td>\n",
              "      <td>3284.0</td>\n",
              "      <td>5131.606811</td>\n",
              "      <td>3698.640618</td>\n",
              "      <td>300.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>3284.0</td>\n",
              "      <td>813.968250</td>\n",
              "      <td>962.970839</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>221.668006</td>\n",
              "      <td>499.038060</td>\n",
              "      <td>1045.484917</td>\n",
              "      <td>8946.495566</td>\n",
              "      <td>3284.0</td>\n",
              "      <td>228.802505</td>\n",
              "      <td>236.680894</td>\n",
              "      <td>0.019163</td>\n",
              "      <td>120.161214</td>\n",
              "      <td>171.648931</td>\n",
              "      <td>249.152357</td>\n",
              "      <td>4483.503862</td>\n",
              "      <td>3284.0</td>\n",
              "      <td>0.246505</td>\n",
              "      <td>0.346644</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2642.0</td>\n",
              "      <td>3044.938037</td>\n",
              "      <td>2482.240438</td>\n",
              "      <td>6.824905</td>\n",
              "      <td>1263.747824</td>\n",
              "      <td>2173.994774</td>\n",
              "      <td>4241.510992</td>\n",
              "      <td>18495.558550</td>\n",
              "      <td>2642.0</td>\n",
              "      <td>0.964891</td>\n",
              "      <td>0.119311</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2642.0</td>\n",
              "      <td>389.000167</td>\n",
              "      <td>781.743976</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>64.820</td>\n",
              "      <td>438.6525</td>\n",
              "      <td>7823.74</td>\n",
              "      <td>2642.0</td>\n",
              "      <td>236.095821</td>\n",
              "      <td>559.831028</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>204.000</td>\n",
              "      <td>7564.81</td>\n",
              "      <td>2642.0</td>\n",
              "      <td>152.993834</td>\n",
              "      <td>378.470140</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>124.9875</td>\n",
              "      <td>4092.67</td>\n",
              "      <td>...</td>\n",
              "      <td>2642.0</td>\n",
              "      <td>7.249432</td>\n",
              "      <td>15.154244</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>208.0</td>\n",
              "      <td>2642.0</td>\n",
              "      <td>4489.610144</td>\n",
              "      <td>3608.012157</td>\n",
              "      <td>200.0</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>6237.5</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>2642.0</td>\n",
              "      <td>968.134662</td>\n",
              "      <td>920.485245</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>378.536081</td>\n",
              "      <td>676.492068</td>\n",
              "      <td>1288.582010</td>\n",
              "      <td>11123.409180</td>\n",
              "      <td>2642.0</td>\n",
              "      <td>1027.525658</td>\n",
              "      <td>915.374330</td>\n",
              "      <td>67.786744</td>\n",
              "      <td>406.579965</td>\n",
              "      <td>717.686451</td>\n",
              "      <td>1331.858565</td>\n",
              "      <td>8345.641905</td>\n",
              "      <td>2642.0</td>\n",
              "      <td>0.001772</td>\n",
              "      <td>0.023236</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>399.0</td>\n",
              "      <td>1963.743393</td>\n",
              "      <td>1836.283217</td>\n",
              "      <td>70.794108</td>\n",
              "      <td>1007.928213</td>\n",
              "      <td>1316.179352</td>\n",
              "      <td>2127.610649</td>\n",
              "      <td>11670.179850</td>\n",
              "      <td>399.0</td>\n",
              "      <td>0.989170</td>\n",
              "      <td>0.047040</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>399.0</td>\n",
              "      <td>850.298471</td>\n",
              "      <td>1235.297199</td>\n",
              "      <td>0.00</td>\n",
              "      <td>147.9050</td>\n",
              "      <td>494.040</td>\n",
              "      <td>1049.9850</td>\n",
              "      <td>11147.33</td>\n",
              "      <td>399.0</td>\n",
              "      <td>163.977719</td>\n",
              "      <td>404.892880</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>127.960</td>\n",
              "      <td>3352.28</td>\n",
              "      <td>399.0</td>\n",
              "      <td>687.188045</td>\n",
              "      <td>1137.986129</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>349.920</td>\n",
              "      <td>872.1000</td>\n",
              "      <td>10009.93</td>\n",
              "      <td>...</td>\n",
              "      <td>399.0</td>\n",
              "      <td>17.644110</td>\n",
              "      <td>24.244539</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>199.0</td>\n",
              "      <td>399.0</td>\n",
              "      <td>2210.275689</td>\n",
              "      <td>1966.936417</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>11500.0</td>\n",
              "      <td>399.0</td>\n",
              "      <td>1339.214906</td>\n",
              "      <td>1444.107306</td>\n",
              "      <td>27.832777</td>\n",
              "      <td>430.021678</td>\n",
              "      <td>864.471651</td>\n",
              "      <td>1641.156891</td>\n",
              "      <td>8735.607835</td>\n",
              "      <td>399.0</td>\n",
              "      <td>6158.994965</td>\n",
              "      <td>8721.792216</td>\n",
              "      <td>201.615417</td>\n",
              "      <td>1602.529012</td>\n",
              "      <td>3078.975540</td>\n",
              "      <td>7305.064790</td>\n",
              "      <td>76406.207520</td>\n",
              "      <td>399.0</td>\n",
              "      <td>0.020108</td>\n",
              "      <td>0.092247</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1081.0</td>\n",
              "      <td>1800.909055</td>\n",
              "      <td>1979.686042</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>509.872908</td>\n",
              "      <td>1100.941072</td>\n",
              "      <td>2321.846501</td>\n",
              "      <td>14581.459140</td>\n",
              "      <td>1081.0</td>\n",
              "      <td>0.862756</td>\n",
              "      <td>0.230969</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.818182</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1081.0</td>\n",
              "      <td>466.019473</td>\n",
              "      <td>1177.718477</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>30.600</td>\n",
              "      <td>490.2100</td>\n",
              "      <td>22101.78</td>\n",
              "      <td>1081.0</td>\n",
              "      <td>279.478723</td>\n",
              "      <td>951.673102</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>225.500</td>\n",
              "      <td>22101.78</td>\n",
              "      <td>1081.0</td>\n",
              "      <td>186.593219</td>\n",
              "      <td>504.683724</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>134.5600</td>\n",
              "      <td>8409.93</td>\n",
              "      <td>...</td>\n",
              "      <td>1081.0</td>\n",
              "      <td>7.217391</td>\n",
              "      <td>18.197016</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>1081.0</td>\n",
              "      <td>3987.068651</td>\n",
              "      <td>3508.304680</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>21000.0</td>\n",
              "      <td>1081.0</td>\n",
              "      <td>4667.483007</td>\n",
              "      <td>4998.606699</td>\n",
              "      <td>193.747321</td>\n",
              "      <td>1664.863771</td>\n",
              "      <td>3009.926784</td>\n",
              "      <td>5949.072074</td>\n",
              "      <td>40627.595240</td>\n",
              "      <td>1081.0</td>\n",
              "      <td>739.509333</td>\n",
              "      <td>1126.489747</td>\n",
              "      <td>1.334075</td>\n",
              "      <td>221.752549</td>\n",
              "      <td>445.215577</td>\n",
              "      <td>886.322345</td>\n",
              "      <td>21235.065300</td>\n",
              "      <td>1081.0</td>\n",
              "      <td>0.115960</td>\n",
              "      <td>0.186377</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1544.0</td>\n",
              "      <td>1143.323704</td>\n",
              "      <td>1662.446912</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>212.974614</td>\n",
              "      <td>556.818404</td>\n",
              "      <td>1372.545948</td>\n",
              "      <td>19043.138560</td>\n",
              "      <td>1544.0</td>\n",
              "      <td>0.927532</td>\n",
              "      <td>0.167251</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1544.0</td>\n",
              "      <td>3266.487766</td>\n",
              "      <td>4069.772447</td>\n",
              "      <td>100.98</td>\n",
              "      <td>1146.1425</td>\n",
              "      <td>2127.640</td>\n",
              "      <td>3868.0925</td>\n",
              "      <td>49039.57</td>\n",
              "      <td>1544.0</td>\n",
              "      <td>2124.563051</td>\n",
              "      <td>3352.199030</td>\n",
              "      <td>0.0</td>\n",
              "      <td>399.4875</td>\n",
              "      <td>1242.450</td>\n",
              "      <td>2552.805</td>\n",
              "      <td>40761.25</td>\n",
              "      <td>1544.0</td>\n",
              "      <td>1142.539352</td>\n",
              "      <td>1687.911601</td>\n",
              "      <td>0.0</td>\n",
              "      <td>167.935</td>\n",
              "      <td>674.840</td>\n",
              "      <td>1444.2375</td>\n",
              "      <td>22500.00</td>\n",
              "      <td>...</td>\n",
              "      <td>1544.0</td>\n",
              "      <td>37.853627</td>\n",
              "      <td>41.189845</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>358.0</td>\n",
              "      <td>1544.0</td>\n",
              "      <td>4092.072637</td>\n",
              "      <td>3637.444078</td>\n",
              "      <td>200.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>1544.0</td>\n",
              "      <td>3044.597207</td>\n",
              "      <td>4070.189694</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>947.851804</td>\n",
              "      <td>1890.180168</td>\n",
              "      <td>3725.052084</td>\n",
              "      <td>50721.483360</td>\n",
              "      <td>1544.0</td>\n",
              "      <td>543.365781</td>\n",
              "      <td>1110.269713</td>\n",
              "      <td>14.012997</td>\n",
              "      <td>168.883713</td>\n",
              "      <td>219.087870</td>\n",
              "      <td>525.802344</td>\n",
              "      <td>18621.013310</td>\n",
              "      <td>1544.0</td>\n",
              "      <td>0.277309</td>\n",
              "      <td>0.366536</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 128 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        BALANCE                            ... PRC_FULL_PAYMENT               \n",
              "          count         mean          std  ...              50%       75%  max\n",
              "cluster                                    ...                                \n",
              "0        3284.0   445.102105   847.653849  ...         0.000000  0.444444  1.0\n",
              "1        2642.0  3044.938037  2482.240438  ...         0.000000  0.000000  0.6\n",
              "2         399.0  1963.743393  1836.283217  ...         0.000000  0.000000  1.0\n",
              "3        1081.0  1800.909055  1979.686042  ...         0.083333  0.142857  1.0\n",
              "4        1544.0  1143.323704  1662.446912  ...         0.083333  0.545455  1.0\n",
              "\n",
              "[5 rows x 128 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "koYxYF_QkfOo",
        "outputId": "6286bea1-af02-4256-b464-7e7148e95118"
      },
      "source": [
        "centroides = kmeans.cluster_centers_\n",
        "centroides"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.92595308e-02, 2.30686023e-04, 1.21382295e-01, 4.79315402e-02,\n",
              "        7.35508613e-02, 2.47153466e-02, 1.72659283e-04, 3.36343950e-05,\n",
              "        1.40157387e-04, 7.96901589e-06, 1.25450247e-04, 2.86056694e-03,\n",
              "        9.43498125e-01, 1.53749473e-01, 5.68773030e-02, 7.60565847e-05],\n",
              "       [5.06008804e-01, 2.73199069e-04, 6.26436989e-02, 3.68644377e-02,\n",
              "        2.58010236e-02, 2.49962891e-01, 6.84607127e-05, 2.62761411e-05,\n",
              "        4.56720931e-05, 5.70264270e-05, 1.14528182e-03, 1.35307476e-03,\n",
              "        7.18700948e-01, 1.66343271e-01, 1.80691979e-01, 5.33603955e-07],\n",
              "       [3.24769127e-01, 2.64261579e-04, 1.56170300e-01, 3.18471811e-02,\n",
              "        1.24720355e-01, 5.86525519e-02, 1.64152214e-04, 2.30217302e-05,\n",
              "        1.47862104e-04, 1.95436118e-05, 4.28386224e-04, 3.82953041e-03,\n",
              "        3.69551388e-01, 2.52333724e-01, 6.88501438e-01, 4.42920215e-06],\n",
              "       [2.36713453e-01, 2.16961764e-04, 5.90293196e-02, 3.35589818e-02,\n",
              "        2.54755090e-02, 4.04460081e-01, 5.16953900e-05, 2.01969756e-05,\n",
              "        3.43732340e-05, 6.61260673e-05, 1.58762268e-03, 1.09779947e-03,\n",
              "        5.37642538e-01, 5.92146837e-01, 1.04865812e-01, 2.74145272e-05],\n",
              "       [1.60282475e-01, 2.44976409e-04, 4.37904148e-01, 2.56849464e-01,\n",
              "        1.81225639e-01, 2.59402903e-02, 2.04235686e-04, 8.89741230e-05,\n",
              "        1.50490244e-04, 9.43845624e-06, 1.73362399e-04, 6.16864361e-03,\n",
              "        6.02872332e-01, 4.03908363e-01, 8.57564447e-02, 7.17211541e-05]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0lyELTEknsz",
        "outputId": "8e14c4cb-0ae1-4429-9184-d8c9c180c606"
      },
      "source": [
        "max = len(centroides[0])\n",
        "for i in range(max):\n",
        "  print(df.columns.values[i],\"\\n{:.4f}\".format(centroides[:,i].var()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BALANCE \n",
            "0.0223\n",
            "BALANCE_FREQUENCY \n",
            "0.0000\n",
            "PURCHASES \n",
            "0.0196\n",
            "ONEOFF_PURCHASES \n",
            "0.0077\n",
            "INSTALLMENTS_PURCHASES \n",
            "0.0036\n",
            "CASH_ADVANCE \n",
            "0.0228\n",
            "PURCHASES_FREQUENCY \n",
            "0.0000\n",
            "ONEOFF_PURCHASES_FREQUENCY \n",
            "0.0000\n",
            "PURCHASES_INSTALLMENTS_FREQUENCY \n",
            "0.0000\n",
            "CASH_ADVANCE_FREQUENCY \n",
            "0.0000\n",
            "CASH_ADVANCE_TRX \n",
            "0.0000\n",
            "PURCHASES_TRX \n",
            "0.0000\n",
            "CREDIT_LIMIT \n",
            "0.0366\n",
            "PAYMENTS \n",
            "0.0273\n",
            "MINIMUM_PAYMENTS \n",
            "0.0558\n",
            "PRC_FULL_PAYMENT \n",
            "0.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "gcplJiSpmxQh",
        "outputId": "f3b687d6-dd09-4c21-a465-f1455af649ae"
      },
      "source": [
        "escolhidos = ['BALANCE','PURCHASES','CASH_ADVANCE','CREDIT_LIMIT','PAYMENTS']\n",
        "df.groupby(\"cluster\")[escolhidos].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">BALANCE</th>\n",
              "      <th colspan=\"8\" halign=\"left\">PURCHASES</th>\n",
              "      <th colspan=\"8\" halign=\"left\">CASH_ADVANCE</th>\n",
              "      <th colspan=\"8\" halign=\"left\">CREDIT_LIMIT</th>\n",
              "      <th colspan=\"8\" halign=\"left\">PAYMENTS</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3284.0</td>\n",
              "      <td>445.102105</td>\n",
              "      <td>847.653849</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>23.396562</td>\n",
              "      <td>83.076264</td>\n",
              "      <td>412.596088</td>\n",
              "      <td>9630.367575</td>\n",
              "      <td>3284.0</td>\n",
              "      <td>628.639333</td>\n",
              "      <td>758.786483</td>\n",
              "      <td>0.00</td>\n",
              "      <td>133.0000</td>\n",
              "      <td>362.435</td>\n",
              "      <td>833.4625</td>\n",
              "      <td>6407.67</td>\n",
              "      <td>3284.0</td>\n",
              "      <td>143.824757</td>\n",
              "      <td>522.530008</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6523.780195</td>\n",
              "      <td>3284.0</td>\n",
              "      <td>5131.606811</td>\n",
              "      <td>3698.640618</td>\n",
              "      <td>300.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>3284.0</td>\n",
              "      <td>813.968250</td>\n",
              "      <td>962.970839</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>221.668006</td>\n",
              "      <td>499.038060</td>\n",
              "      <td>1045.484917</td>\n",
              "      <td>8946.495566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2642.0</td>\n",
              "      <td>3044.938037</td>\n",
              "      <td>2482.240438</td>\n",
              "      <td>6.824905</td>\n",
              "      <td>1263.747824</td>\n",
              "      <td>2173.994774</td>\n",
              "      <td>4241.510992</td>\n",
              "      <td>18495.558550</td>\n",
              "      <td>2642.0</td>\n",
              "      <td>389.000167</td>\n",
              "      <td>781.743976</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>64.820</td>\n",
              "      <td>438.6525</td>\n",
              "      <td>7823.74</td>\n",
              "      <td>2642.0</td>\n",
              "      <td>1621.851847</td>\n",
              "      <td>1921.816695</td>\n",
              "      <td>0.0</td>\n",
              "      <td>152.697306</td>\n",
              "      <td>1005.425293</td>\n",
              "      <td>2442.123879</td>\n",
              "      <td>13674.244020</td>\n",
              "      <td>2642.0</td>\n",
              "      <td>4489.610144</td>\n",
              "      <td>3608.012157</td>\n",
              "      <td>200.0</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>6237.5</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>2642.0</td>\n",
              "      <td>968.134662</td>\n",
              "      <td>920.485245</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>378.536081</td>\n",
              "      <td>676.492068</td>\n",
              "      <td>1288.582010</td>\n",
              "      <td>11123.409180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>399.0</td>\n",
              "      <td>1963.743393</td>\n",
              "      <td>1836.283217</td>\n",
              "      <td>70.794108</td>\n",
              "      <td>1007.928213</td>\n",
              "      <td>1316.179352</td>\n",
              "      <td>2127.610649</td>\n",
              "      <td>11670.179850</td>\n",
              "      <td>399.0</td>\n",
              "      <td>850.298471</td>\n",
              "      <td>1235.297199</td>\n",
              "      <td>0.00</td>\n",
              "      <td>147.9050</td>\n",
              "      <td>494.040</td>\n",
              "      <td>1049.9850</td>\n",
              "      <td>11147.33</td>\n",
              "      <td>399.0</td>\n",
              "      <td>417.369198</td>\n",
              "      <td>972.848155</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>376.659826</td>\n",
              "      <td>10616.270140</td>\n",
              "      <td>399.0</td>\n",
              "      <td>2210.275689</td>\n",
              "      <td>1966.936417</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>11500.0</td>\n",
              "      <td>399.0</td>\n",
              "      <td>1339.214906</td>\n",
              "      <td>1444.107306</td>\n",
              "      <td>27.832777</td>\n",
              "      <td>430.021678</td>\n",
              "      <td>864.471651</td>\n",
              "      <td>1641.156891</td>\n",
              "      <td>8735.607835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1081.0</td>\n",
              "      <td>1800.909055</td>\n",
              "      <td>1979.686042</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>509.872908</td>\n",
              "      <td>1100.941072</td>\n",
              "      <td>2321.846501</td>\n",
              "      <td>14581.459140</td>\n",
              "      <td>1081.0</td>\n",
              "      <td>466.019473</td>\n",
              "      <td>1177.718477</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>30.600</td>\n",
              "      <td>490.2100</td>\n",
              "      <td>22101.78</td>\n",
              "      <td>1081.0</td>\n",
              "      <td>3287.022132</td>\n",
              "      <td>4029.611497</td>\n",
              "      <td>0.0</td>\n",
              "      <td>909.480894</td>\n",
              "      <td>1961.772810</td>\n",
              "      <td>4232.135689</td>\n",
              "      <td>47137.211760</td>\n",
              "      <td>1081.0</td>\n",
              "      <td>3987.068651</td>\n",
              "      <td>3508.304680</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>21000.0</td>\n",
              "      <td>1081.0</td>\n",
              "      <td>4667.483007</td>\n",
              "      <td>4998.606699</td>\n",
              "      <td>193.747321</td>\n",
              "      <td>1664.863771</td>\n",
              "      <td>3009.926784</td>\n",
              "      <td>5949.072074</td>\n",
              "      <td>40627.595240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1544.0</td>\n",
              "      <td>1143.323704</td>\n",
              "      <td>1662.446912</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>212.974614</td>\n",
              "      <td>556.818404</td>\n",
              "      <td>1372.545948</td>\n",
              "      <td>19043.138560</td>\n",
              "      <td>1544.0</td>\n",
              "      <td>3266.487766</td>\n",
              "      <td>4069.772447</td>\n",
              "      <td>100.98</td>\n",
              "      <td>1146.1425</td>\n",
              "      <td>2127.640</td>\n",
              "      <td>3868.0925</td>\n",
              "      <td>49039.57</td>\n",
              "      <td>1544.0</td>\n",
              "      <td>183.835583</td>\n",
              "      <td>597.825963</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7540.307350</td>\n",
              "      <td>1544.0</td>\n",
              "      <td>4092.072637</td>\n",
              "      <td>3637.444078</td>\n",
              "      <td>200.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>1544.0</td>\n",
              "      <td>3044.597207</td>\n",
              "      <td>4070.189694</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>947.851804</td>\n",
              "      <td>1890.180168</td>\n",
              "      <td>3725.052084</td>\n",
              "      <td>50721.483360</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        BALANCE               ...     PAYMENTS              \n",
              "          count         mean  ...          75%           max\n",
              "cluster                       ...                           \n",
              "0        3284.0   445.102105  ...  1045.484917   8946.495566\n",
              "1        2642.0  3044.938037  ...  1288.582010  11123.409180\n",
              "2         399.0  1963.743393  ...  1641.156891   8735.607835\n",
              "3        1081.0  1800.909055  ...  5949.072074  40627.595240\n",
              "4        1544.0  1143.323704  ...  3725.052084  50721.483360\n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "JmJt5D5gnHxk",
        "outputId": "513942bb-276b-4d24-f8d4-31dd8e485c35"
      },
      "source": [
        "description = df.groupby(\"cluster\")[\"BALANCE\", \"PURCHASES\", \"CASH_ADVANCE\", \"CREDIT_LIMIT\", \"PAYMENTS\"]\n",
        "n_clients = description.size()\n",
        "description = description.mean()\n",
        "description['n_clients'] = n_clients\n",
        "description"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BALANCE</th>\n",
              "      <th>PURCHASES</th>\n",
              "      <th>CASH_ADVANCE</th>\n",
              "      <th>CREDIT_LIMIT</th>\n",
              "      <th>PAYMENTS</th>\n",
              "      <th>n_clients</th>\n",
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              "    <tr>\n",
              "      <th>cluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <td>143.824757</td>\n",
              "      <td>5131.606811</td>\n",
              "      <td>813.968250</td>\n",
              "      <td>3284</td>\n",
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              "      <td>1621.851847</td>\n",
              "      <td>4489.610144</td>\n",
              "      <td>968.134662</td>\n",
              "      <td>2642</td>\n",
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              "      <th>2</th>\n",
              "      <td>1963.743393</td>\n",
              "      <td>850.298471</td>\n",
              "      <td>417.369198</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1800.909055</td>\n",
              "      <td>466.019473</td>\n",
              "      <td>3287.022132</td>\n",
              "      <td>3987.068651</td>\n",
              "      <td>4667.483007</td>\n",
              "      <td>1081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1143.323704</td>\n",
              "      <td>3266.487766</td>\n",
              "      <td>183.835583</td>\n",
              "      <td>4092.072637</td>\n",
              "      <td>3044.597207</td>\n",
              "      <td>1544</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             BALANCE    PURCHASES  ...     PAYMENTS  n_clients\n",
              "cluster                            ...                        \n",
              "0         445.102105   628.639333  ...   813.968250       3284\n",
              "1        3044.938037   389.000167  ...   968.134662       2642\n",
              "2        1963.743393   850.298471  ...  1339.214906        399\n",
              "3        1800.909055   466.019473  ...  4667.483007       1081\n",
              "4        1143.323704  3266.487766  ...  3044.597207       1544\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tg5xyloipU6i"
      },
      "source": [
        "#Aula 09:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "3KyQIK7xpM67",
        "outputId": "24d18c87-7d2a-4c54-e006-b10c465193a1"
      },
      "source": [
        "description"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BALANCE</th>\n",
              "      <th>PURCHASES</th>\n",
              "      <th>CASH_ADVANCE</th>\n",
              "      <th>CREDIT_LIMIT</th>\n",
              "      <th>PAYMENTS</th>\n",
              "      <th>n_clients</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>0</th>\n",
              "      <td>445.102105</td>\n",
              "      <td>628.639333</td>\n",
              "      <td>143.824757</td>\n",
              "      <td>5131.606811</td>\n",
              "      <td>813.968250</td>\n",
              "      <td>3284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3044.938037</td>\n",
              "      <td>389.000167</td>\n",
              "      <td>1621.851847</td>\n",
              "      <td>4489.610144</td>\n",
              "      <td>968.134662</td>\n",
              "      <td>2642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1963.743393</td>\n",
              "      <td>850.298471</td>\n",
              "      <td>417.369198</td>\n",
              "      <td>2210.275689</td>\n",
              "      <td>1339.214906</td>\n",
              "      <td>399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1800.909055</td>\n",
              "      <td>466.019473</td>\n",
              "      <td>3287.022132</td>\n",
              "      <td>3987.068651</td>\n",
              "      <td>4667.483007</td>\n",
              "      <td>1081</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1143.323704</td>\n",
              "      <td>3266.487766</td>\n",
              "      <td>183.835583</td>\n",
              "      <td>4092.072637</td>\n",
              "      <td>3044.597207</td>\n",
              "      <td>1544</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             BALANCE    PURCHASES  ...     PAYMENTS  n_clients\n",
              "cluster                            ...                        \n",
              "0         445.102105   628.639333  ...   813.968250       3284\n",
              "1        3044.938037   389.000167  ...   968.134662       2642\n",
              "2        1963.743393   850.298471  ...  1339.214906        399\n",
              "3        1800.909055   466.019473  ...  4667.483007       1081\n",
              "4        1143.323704  3266.487766  ...  3044.597207       1544\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "nx3c0H4Grp3E",
        "outputId": "81ede4c7-70b4-4b5c-d59c-627004739d2d"
      },
      "source": [
        "df.groupby(\"cluster\")['PRC_FULL_PAYMENT'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3284.0</td>\n",
              "      <td>0.246505</td>\n",
              "      <td>0.346644</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2642.0</td>\n",
              "      <td>0.001772</td>\n",
              "      <td>0.023236</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>399.0</td>\n",
              "      <td>0.020108</td>\n",
              "      <td>0.092247</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1081.0</td>\n",
              "      <td>0.115960</td>\n",
              "      <td>0.186377</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1544.0</td>\n",
              "      <td>0.277309</td>\n",
              "      <td>0.366536</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          count      mean       std  min  25%       50%       75%  max\n",
              "cluster                                                               \n",
              "0        3284.0  0.246505  0.346644  0.0  0.0  0.000000  0.444444  1.0\n",
              "1        2642.0  0.001772  0.023236  0.0  0.0  0.000000  0.000000  0.6\n",
              "2         399.0  0.020108  0.092247  0.0  0.0  0.000000  0.000000  1.0\n",
              "3        1081.0  0.115960  0.186377  0.0  0.0  0.083333  0.142857  1.0\n",
              "4        1544.0  0.277309  0.366536  0.0  0.0  0.083333  0.545455  1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bR2um1TPpcq0"
      },
      "source": [
        "## ANÁLISES\n",
        "\n",
        "**CLUSTER 0:** MENOR VALOR DE GASTOS. MENOR VALOR EM SAQUES. MAIOR LIMITE DE CRÉDITOS. MENOR VALOR DE PAGAMENTOS.\n",
        "\n",
        "**CLUSTER 1:** MAIOR VALOR DE GASTOS. MENOR VALOR EM COMPRAS. VALOR CONSIDERÁVEL EM SAQUES. \n",
        "\n",
        "**CLUSTER 2:** MENOR LIMITE DE CRÉDITO.\n",
        "\n",
        "**CLUSTER 3:** MAIOR VALOR EM SAQUES COM GRANDES DIFERENÇAS. MAIOR VALOR DE PAGAMENTO.\n",
        "\n",
        "**CLUSTER 4:** MAIOR VALOR EM COMPRAS COM GRANDE DIFERENÇAS. PAGAMENTOS CONSIDERÁVEIS\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eigvoZRnphkN"
      },
      "source": [
        "### ANÁLISE FINAL\n",
        "\n",
        "**CLUSTER 0:** CLIENTES QUE GASTAM POUCO. PAGADORES OK. MAIOR NÚMERO DE CLIENTES.\n",
        "\n",
        "**CLUSTER 1:** CLIENTES QUE MAIS GASTAM. O FOCO É EM SAQUE. NÃO COSTUMAM PAGAR.\n",
        "\n",
        "**CLUSTER 2:** CLIENTE COM MENOR LIMITE. MENOR NÚMERO DE CLIENTES.\n",
        "\n",
        "**CLUSTER 3:** CLIENTES QUE GASTAM MUITO COM SAQUES.\n",
        "\n",
        "**CLUSTER 4:** CLIENTES QUE GASTAM MUITO COM COMPRAS. BONS PAGADORES."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWF9rh_rtNa3"
      },
      "source": [
        "## Como utilizar o Cluster?\n",
        "\n",
        "Podemos aplicar estratégia de negócios para os grupos de cluster. Aumentar o limite para os clientes que pagam bem, inserir uma estratégia para aumentar o número de compras os que gastam pouco. Bonificar os clientes que gastam muito com compras e são bons pagadores. São diversas estratégias que podem ser aplicadas pelo negócio.\n",
        "\n",
        "Podemos usar também um teste AB e conferir se os grupos de teste e controle responderam ao estímulo (programa de pontos, por exemplo)."
      ]
    }
  ]
}