outliers_analysis.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "outliers_analysis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN8q6ymKW3nn76jvvteBuNc",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mario-RJunior/olist-e-commerce/blob/master/outliers_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zXgirfpAI64",
        "colab_type": "text"
      },
      "source": [
        "# Análise dos outliers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sch1KbbMS0I6",
        "colab_type": "text"
      },
      "source": [
        "## 1) Introdução"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7z2BaVzXS4Y8",
        "colab_type": "text"
      },
      "source": [
        "Neste notebook faremos uma análise mais específica dos outliers da base de dados da Olist. Esta é uma continuação do trabalho de segmentação de clientes que pode ser visto [aqui](https://github.com/Mario-RJunior/olist-e-commerce/blob/master/segmentation.ipynb)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNogUIjf-PQ-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "ffdb85ea-e146-4652-a6d4-2c1be649e12a"
      },
      "source": [
        "# Importando as bibliotecas\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyuBPh-k95JF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "e9adba7e-2ee2-4ead-f662-a0c02b804a94"
      },
      "source": [
        "! git clone https://github.com/Mario-RJunior/olist-e-commerce"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'olist-e-commerce'...\n",
            "remote: Enumerating objects: 112, done.\u001b[K\n",
            "remote: Counting objects:   0% (1/112)\u001b[K\rremote: Counting objects:   1% (2/112)\u001b[K\rremote: Counting objects:   2% (3/112)\u001b[K\rremote: Counting objects:   3% (4/112)\u001b[K\rremote: Counting objects:   4% (5/112)\u001b[K\rremote: Counting objects:   5% (6/112)\u001b[K\rremote: Counting objects:   6% (7/112)\u001b[K\rremote: Counting objects:   7% (8/112)\u001b[K\rremote: Counting objects:   8% (9/112)\u001b[K\rremote: Counting objects:   9% (11/112)\u001b[K\rremote: Counting objects:  10% (12/112)\u001b[K\rremote: Counting objects:  11% (13/112)\u001b[K\rremote: Counting objects:  12% (14/112)\u001b[K\rremote: Counting objects:  13% (15/112)\u001b[K\rremote: Counting objects:  14% (16/112)\u001b[K\rremote: Counting objects:  15% (17/112)\u001b[K\rremote: Counting objects:  16% (18/112)\u001b[K\rremote: Counting objects:  17% (20/112)\u001b[K\rremote: Counting objects:  18% (21/112)\u001b[K\rremote: Counting objects:  19% (22/112)\u001b[K\rremote: Counting 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(112/112)\u001b[K\rremote: Counting objects: 100% (112/112), done.\u001b[K\n",
            "remote: Compressing objects: 100% (112/112), done.\u001b[K\n",
            "remote: Total 293 (delta 70), reused 0 (delta 0), pack-reused 181\u001b[K\n",
            "Receiving objects: 100% (293/293), 26.43 MiB | 24.45 MiB/s, done.\n",
            "Resolving deltas: 100% (170/170), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5LRqjCBM-bov",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "7d16e04f-6fa5-4422-c082-fa54610a48d2"
      },
      "source": [
        "df = pd.read_csv('olist-e-commerce/outliers.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_unique_id</th>\n",
              "      <th>recency</th>\n",
              "      <th>R</th>\n",
              "      <th>frequency</th>\n",
              "      <th>F</th>\n",
              "      <th>monetary</th>\n",
              "      <th>M</th>\n",
              "      <th>score</th>\n",
              "      <th>RFM_level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0005e1862207bf6ccc02e4228effd9a0</td>\n",
              "      <td>548</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>135.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Ruim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000fbf0473c10fc1ab6f8d2d286ce20c</td>\n",
              "      <td>39</td>\n",
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              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>285.80</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0011805441c0d1b68b48002f1d005526</td>\n",
              "      <td>497</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>269.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Ruim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0011857aff0e5871ce5eb429f21cdaf5</td>\n",
              "      <td>432</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>174.33</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Regular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0028a7d8db7b0247652509358ad8d755</td>\n",
              "      <td>264</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>274.00</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Regular</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 customer_unique_id  recency  R  ...  M  score  RFM_level\n",
              "0  0005e1862207bf6ccc02e4228effd9a0      548  0  ...  0      0       Ruim\n",
              "1  000fbf0473c10fc1ab6f8d2d286ce20c       39  4  ...  1      7      Ótimo\n",
              "2  0011805441c0d1b68b48002f1d005526      497  0  ...  1      1       Ruim\n",
              "3  0011857aff0e5871ce5eb429f21cdaf5      432  1  ...  1      2    Regular\n",
              "4  0028a7d8db7b0247652509358ad8d755      264  2  ...  1      3    Regular\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpIg5ELm-bkL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e82d246e-d415-4a3b-ffcd-3f30d1887681"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9541, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1hMbNUQ-tQe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "78a6f96e-5c13-41a9-8881-14fa51c704b6"
      },
      "source": [
        "df.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",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>recency</th>\n",
              "      <th>R</th>\n",
              "      <th>frequency</th>\n",
              "      <th>F</th>\n",
              "      <th>monetary</th>\n",
              "      <th>M</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9541.000000</td>\n",
              "      <td>9541.000000</td>\n",
              "      <td>9541.000000</td>\n",
              "      <td>9541.000000</td>\n",
              "      <td>9541.000000</td>\n",
              "      <td>9541.000000</td>\n",
              "      <td>9541.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>283.694791</td>\n",
              "      <td>1.992034</td>\n",
              "      <td>1.671313</td>\n",
              "      <td>0.512944</td>\n",
              "      <td>536.772972</td>\n",
              "      <td>1.425427</td>\n",
              "      <td>3.930406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>174.812672</td>\n",
              "      <td>1.423794</td>\n",
              "      <td>1.453804</td>\n",
              "      <td>0.890060</td>\n",
              "      <td>485.599817</td>\n",
              "      <td>0.738446</td>\n",
              "      <td>1.979766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>131.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>279.900000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>273.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>426.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>634.400000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>729.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>13440.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           recency            R  ...            M        score\n",
              "count  9541.000000  9541.000000  ...  9541.000000  9541.000000\n",
              "mean    283.694791     1.992034  ...     1.425427     3.930406\n",
              "std     174.812672     1.423794  ...     0.738446     1.979766\n",
              "min       1.000000     0.000000  ...     0.000000     0.000000\n",
              "25%     131.000000     1.000000  ...     1.000000     2.000000\n",
              "50%     273.000000     2.000000  ...     1.000000     4.000000\n",
              "75%     426.000000     3.000000  ...     2.000000     5.000000\n",
              "max     729.000000     4.000000  ...     4.000000    12.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nB-8B5vPAABX",
        "colab_type": "text"
      },
      "source": [
        "## 2) Exibindo os dados graficamente"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RGiSUzW0ARfG",
        "colab_type": "text"
      },
      "source": [
        "- Pairplot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q2DSKPjNAQCV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "outputId": "4553a80c-749d-40d8-c5e9-4b635503aa04"
      },
      "source": [
        "# Plotando um pairplot\n",
        "colunas = df[['frequency', 'recency', 'monetary']]\n",
        "sns.pairplot(data=colunas, diag_kind='kde')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rLMGFLqzJh_8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "outputId": "200d94ea-7bbe-42b9-d34c-52e7d5ac67bf"
      },
      "source": [
        "# Plotando um pairplot em escala logarítmica\n",
        "colunas = df[['frequency', 'recency', 'monetary']]\n",
        "sns.pairplot(data=np.log(colunas), diag_kind='kde')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gUqMO-efbhBX",
        "colab_type": "text"
      },
      "source": [
        "- Heatmap"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9a2lN4tIBja",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "1de681fc-47b4-4b0f-d84d-a5c9040e7107"
      },
      "source": [
        "# Verificando a correlação das varáveis\n",
        "fig = plt.figure(figsize=(10,7))\n",
        "correlacao = df.corr()\n",
        "sns.heatmap(data=correlacao, annot=True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jk8JlBh7bnei",
        "colab_type": "text"
      },
      "source": [
        "- Boxplot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOJpzEA6SzfP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "a5058241-405a-4a6e-cbbb-154a18e31623"
      },
      "source": [
        "# Plotando um boxplot para as variáveis de interesse\n",
        "plt.figure(figsize=(20,5))\n",
        "plt.subplot(1,3,1)\n",
        "sns.boxplot(data=df['recency'])\n",
        "plt.title('Recência')\n",
        "plt.ylabel('Número de dias')\n",
        "plt.subplot(1,3,2)\n",
        "sns.boxplot(data=df['frequency'])\n",
        "plt.title('Frequência')\n",
        "plt.ylabel('Número de compras')\n",
        "plt.subplot(1,3,3)\n",
        "sns.boxplot(data=df['monetary'])\n",
        "plt.title('Monetaridade')\n",
        "plt.ylabel('Valor total (R$)')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GmTNn5hyRmZmNJr5PWLVxIsnMzMysgurq6mhqakISTU1N1NXV5R2SmZmNIr5PWLXx0DYzMzOzCmtubqarq8tPmc3MrE++T1g1cSLJzMzMrMLq6upYsGBB3mGYmdko5fuEVZOKDW2T9HpJdxdtT0n6vKQdJbVLejC97pDOl6QFkjol3SNp30rFZmZmZmZmZmZmm65iiaSI+ENE7B0RewP7Ac8C1wBfAm6OiD2Am1MZ4FBgj7TNAi6qVGxmZmZmZmZmZrbpRmqy7YOBP0XEw8BRQEuqbwGOTvtHAa2RuROYKGmXEYrPzMzMzMzMzMwGMFKJpI8AV6b9nSPikbS/Gtg57U8GVhRdszLVmZmZmZmZmZnZKFDxRJKkzYEjgZ/2PhYRAcQmtjdLUoekjrVr1w5TlGZmZmZmZmZmNpCR6JF0KHBXRDyayo8Whqyl1zWpfhUwtei6KaluIxFxcUQ0RkTjpEmTKhi2mZmZmZmZmZkVG4lE0jG8NKwNYCHQnPabgWuL6mem1dv2B54sGgJnZmZmZmZmZmY5q61k45K2BmYAny6q/jrwE0nHAQ8DH0r1i4HDgE6yFd6OrWRsZmZmZmZmZma2aSqaSIqIdUBdr7puslXcep8bwPGVjMfMzMzMzMzMzAZvpFZtMzMzMzMzMzOzKudEkpmZmZmZmZmZlcWJJDMzMzMzMzMzK4sTSWZmZmZmZmZmVhYnkszMzMzMzMzMrCxOJJmZmZmZmZmZWVmcSDIzMzMzMzMzs7I4kWRmZmZmZmZmZmVxIsnMzMzMzCpK0qWS1ki6t6juG5IekHSPpGskTSw69mVJnZL+IOm9RfVNqa5T0peK6neX9KtU/2NJm4/cpzMzG1+cSDIzMzMzs0q7HGjqVdcOvDki3gr8EfgygKQ9gY8Ab0rXXCipRlIN8B3gUGBP4Jh0LsDZwHkR0QA8DhxX2Y9jZjZ+OZFkZmZmZmYVFRFLgcd61d0UET2peCcwJe0fBfwoIl6IiD8DncDb09YZEQ9FxN+BHwFHSRJwEHBVur4FOLqiH8jMbBxzIsnMzMzMzPL2KeCGtD8ZWFF0bGWq66++DniiKClVqDczswpwIsnMzMzMzHIj6StAD3DFCL3fLEkdkjrWrl07Em9pZjamOJFkZmZmZma5kPRJ4H3AxyIiUvUqYGrRaVNSXX/13cBESbW96vsUERdHRGNENE6aNGlYPoeZ2XjiRJKZmZlZhXV3dzN79my6u7vzDsVs1JDUBHwRODIini06tBD4iKRXSNod2AP4NfAbYI+0QtvmZBNyL0wJqFuBD6Trm4FrR+pzmJmNN04kmZmZmVVYS0sLy5cvp7W1Ne9QzHIh6Urgl8DrJa2UdBzwbWBboF3S3ZK+CxARvwd+AtwHtAHHR8T6NAfS54AbgfuBn6RzAU4BviCpk2zOpEtG8OOZmY0rtQOfYmZmZmaD1d3dTVtbGxFBW1sbM2fOpK6uLu+wzEZURBzTR3W/yZ6IOBM4s4/6xcDiPuofIlvVzczMKsw9kszMzMwqqKWlhQ0bNgCwfv1690oyMzOzquZEkpmZmVkFLVmyhJ6ebFXynp4e2tvbc47IzMzMbPCcSDIzMzOroOnTp1Nbm80mUFtby4wZM3KOyMzMzGzwnEgyMzMzq6Dm5mYmTMj+5KqpqWHmzJk5R2RmZmY2eE4kmZmZmVVQXV0dTU1NSKKpqckTbZuZmVlV86ptZmZmZhXW3NxMV1eXeyOZmZlZ1XMiyczMzKzC6urqWLBgQd5hmJmZmQ2Zh7aZmZmZVVhnZyeHH344nZ2deYdiZmZmNiQVTSRJmijpKkkPSLpf0jsl7SipXdKD6XWHdK4kLZDUKekeSftWMjYzMxudJE2VdKuk+yT9XtKJqb7P+4dZNZg/fz7r1q1j/vz5eYdiZmZmNiSV7pF0PtAWEW8A9gLuB74E3BwRewA3pzLAocAeaZsFXFTh2MzMbHTqAU6OiD2B/YHjJe1J//cPs1Gts7OTrq4uALq6utwryczMzKpaxRJJkrYHDgQuAYiIv0fEE8BRQEs6rQU4Ou0fBbRG5k5goqRdKhWfmZmNThHxSETclfafJnsIMZn+7x9mo1rvXkjulWRmZmbVrJI9knYH1gKXSfqtpO9L2hrYOSIeSeesBnZO+5OBFUXXr0x1ZmY2TkmqB/YBfkX/94/e18yS1CGpY+3atSMSp1kphd5I/ZXNzMzMqkklE0m1wL7ARRGxD7COXsMQIiKA2JRG/QPBzGx8kLQNcDXw+Yh4qvhYqftHRFwcEY0R0Thp0qQRiNSstPr6+pJlMzMzs2pSyUTSSmBlRPwqla8iSyw9Whiyll7XpOOrgKlF109JdRvxDwQzs7FP0mZkSaQrIuJnqbq/+4fZqDZ37tySZTMzM7NqUrFEUkSsBlZIen2qOhi4D1gINKe6ZuDatL8QmJlWb9sfeLJoCIOZmY0TkkQ2v979EXFu0aH+7h9mo1pDQ8M/eiHV19fT0NCQb0BmZmZmQ1DpVdtOAK6QdA+wN3AW8HVghqQHgempDLAYeAjoBL4HfLbCsZmZ2ej0LuATwEGS7k7bYfR//zAb9ebOncvWW2/t3khmZmZW9Wor2XhE3A009nHo4D7ODeD4SsZjZmajX0T8AlA/h192/zCrBg0NDVx//fV5h2FmZmY2ZJXukWRmZmZmZmZmZmOEE0lmZmZmZmZmZlYWJ5LMzMzMzMzMzKwsTiSZmZmZmZmZmVlZnEgyMzMzMzMzM7OyOJFkZmZmZmZmZmZlcSLJzMzMzMzMzMzK4kSSmZmZmZmZmZmVxYkkMzMzMzMzMzMrixNJZmZmZmZmZmZWFieSzMzMzMzMzMysLE4kmZmZmZlZxUm6VNIaSfcW1e0oqV3Sg+l1h1QvSQskdUq6R9K+Rdc0p/MflNRcVL+fpOXpmgWSNLKf0MxsfHAiyczMzKzCuru7mT17Nt3d3XmHYpany4GmXnVfAm6OiD2Am1MZ4FBgj7TNAi6CLPEEzAPeAbwdmFdIPqVz/q3out7vZWZmw8CJJDMzM7MKa2lpYfny5bS2tuYdilluImIp8Fiv6qOAlrTfAhxdVN8amTuBiZJ2Ad4LtEfEYxHxONAONKVj20XEnRERQGtRW2ZmNoycSDIzMzOroO7ubtra2ogI2tra3CvJbGM7R8QjaX81sHPanwysKDpvZaorVb+yj3ozMxtmTiSZmZmZVVBLSwsbNmwAYP369e6VZNaP1JMoKv0+kmZJ6pDUsXbt2kq/nZnZmONEkpmZmVkFLVmyhJ6eHgB6enpob2/POSKzUeXRNCyN9Lom1a8CphadNyXVlaqf0kf9y0TExRHRGBGNkyZNGpYPYWY2njiRZGZmZlZB06dPp7a2FoDa2lpmzJiRc0Rmo8pCoLDyWjNwbVH9zLR62/7Ak2kI3I3AIZJ2SJNsHwLcmI49JWn/tFrbzKK2zMxsGDmRZGZmZlZBzc3NTJiQ/clVU1PDzJkzc47ILB+SrgR+Cbxe0kpJxwFfB2ZIehCYnsoAi4GHgE7ge8BnASLiMeCrwG/SdkaqI53z/XTNn4AbRuJzmZmNN7V5B2BmZmY2ltXV1dHU1MSiRYtoamqirq4u75DMchERx/Rz6OA+zg3g+H7auRS4tI/6DuDNQ4nRzMwG5kSSmZmZWYU1NzfT1dXl3khmZmZW9ZxIMjMzM6uwuro6FixYkHcYZmZmZkPmOZLMzMzMzMzMzKwsTiSZmZmZmZmZmVlZnEgyMzMzMzMzM7OyVDSRJKlL0nJJd0vqSHU7SmqX9GB63SHVS9ICSZ2S7pG0byVjMzMzMzMzMzOzTTMSPZLeExF7R0RjKn8JuDki9gBuTmWAQ4E90jYLuGgEYjMzswqR9C5JW6f9j0s6V9JuecdlZmZmZmaDl8fQtqOAlrTfAhxdVN8amTuBiZJ2ySE+MzMbHhcBz0raCzgZ+BPQmm9IZmZmZmY2FJVOJAVwk6Rlkmalup0j4pG0vxrYOe1PBlYUXbsy1ZmZWXXqiYgge1Dw7Yj4DrBtzjGZmZmZmdkQ1Fa4/X+KiFWSXgm0S3qg+GBEhKTYlAZTQmoWwK677jp8kZqZ2XB7WtKXgY8DB0qaAGyWc0xmZmZmZjYEFe2RFBGr0usa4Brg7cCjhSFr6XVNOn0VMLXo8imprnebF0dEY0Q0Tpo0qZLhm5nZ0HwYeAE4LiJWk32vfyPfkMzMzMzMbCgqlkiStLWkbQv7wCHAvcBCoDmd1gxcm/YXAjPT6m37A08WDYEzM7MqExGrI+LciPh/qfyXiPAcSWZmZmZmVaySQ9t2Bq6RVHifH0ZEm6TfAD+RdBzwMPChdP5i4DCgE3gWOLaCsZmZWYWlhwIXAG8ENgdqgGciYvtcAzMzMzMzs0GrWCIpIh4C9uqjvhs4uI/6AI6vVDxmZjbivg18BPgp0AjMBF6Xa0RmOenu7ub0009n3rx51NXV5R2OmZmZ2aBVetU2MzMbxyKiE6iJiPURcRnQlHdMZnloaWlh+fLltLZ6dKeZmZlVNyeSzMysUp6VtDlwt6T/knQSvu/YONTd3U1bWxsRQVtbG93d3XmHZGZmZjZo/oPezMwq5RNk95nPAevIVub8l1wjMstBS0sLGzZsAGD9+vXulWRmZmZVbcBEUlp9bULaf52kIyVtVvnQzMysWkmqAc6KiOcj4qmIOD0ivpCGupmNK0uWLKGnpweAnp4e2tvbc47IzMzMbPDK6ZG0FNhC0mTgJrInzJdXMigzM6tuEbEe2C0NbTMb16ZPn05tbba+SW1tLTNmzMg5IjMzM7PBK2fVNkXEs5KOAy6MiP+SdHelAzMzs6r3EPC/khaSDW0DICLOzS8ks5HX3NxMW1sbADU1NcycOTPniMyGRlIj8H+AVwPPAfcC7RHxeK6BmZnZiCinR5IkvRP4GHB9qqupXEhmZjZG/Am4juxes23RZjau1NXV0dTUhCSampqoq6vLOySzQZF0rKS7gC8DWwJ/ANYA/wQskdQiadc8YzQzs8orp0fSiWQ3i2si4veSXgPcWtmwzMys2kXE6QCStsuK8XTOIZnlprm5ma6uLvdGsmq3FfCuiHiur4OS9gb2AP4yolGZmdmIGjCRFBFLyeZJKpQfAmZXMigzM6t+aejDZaReSJKeBD4VEctyDcwsB3V1dSxYsCDvMMyGJCK+M8BxT39hZjYODJhIkjQJ+CLwJmCLQn1EHFTBuMzMrPpdCnw2Iv4fgKR/IkssvTXXqMzMbNAkvRJYFxHrJG0JfIHsgcH5EfFIvtGZmdlIKGeOpCuAB4DdgdOBLuA3FYzJzMzGhvWFJBJARPwC6BnoIkmXSloj6d6iutMkrZJ0d9oOq1DMZhXR2dnJ4YcfTmdnZ96hmA3Vj4DCRF+nAw3A48APc4vIzMxGVDmJpLqIuAR4MSJuj4hPAe6NZGZmA7ld0n9Lmibp3ZIuBG6TtK+kfUtcdznQ1Ef9eRGxd9oWVyRiswqZP38+69atY/78+XmHYjZokpqB1wLT0v6HgQ5gNbCbpJmS3OvUzGyMK2ey7RfT6yOSDgf+CuxYuZDMzGyM2Cu9zutVvw8Q9PNQIiKWSqqvXFhmI6uzs5Ouri4Aurq66OzspKGhId+gzAbnNmAdcA9Zr6RHgUWAgOPT8Sdzis3MzEZIOYmk+ZK2B04GLgC2A06qaFRmZlb1IuI9w9zk5yTNJHv6fXJEPD7M7ZtVRO9eSPPnz+fyyy/PJxizIYiIhyVdANwIbAD+LSL+ImlXoDsivFqbmdk4MODQtoi4LiKejIh7I+I9EbFfRCwcieDMzKx6SZooabakcyUtKGyDbO4isuEUewOPAOeUeN9Zkjokdaxdu3aQb2c2fAq9kform1WTiCh8H782Iq5L1d3AMYNtU9JJkn4v6V5JV0raQtLukn4lqVPSjyVtns59RSp3puP1Re18OdX/QdJ7B/8pzcyslH4TSZK+mF4vKP4BMMQfAmZmNn4sBuqB5cCyom2TRcSjEbE+IjYA3wPeXuLciyOiMSIaJ02aNJi3MxtW9fX1Jctm1SYinomIZ4uqNiMb2rbJJE0GZgONEfFmoAb4CHA22dx4hcm8j0uXHAc8nurPS+chac903ZvI5tm7UFLNYGIyM7PSSt5isYoAACAASURBVPVIuj+9drDxD4BB/xAwM7NxZYuI+EJEXBYRLYVtMA1J2qWo+M/Avf2dazbazJ07t2TZrFpImirpYknXSfpXSVtLOgf4I/DKITRdC2wpqRbYiqzn6UHAVel4C3B02j8qlUnHD5akVP+jiHghIv4MdFLioYOZmQ1ev3MkRcSi9DqoP/rNzGzc+4GkfwOuA14oVEbEY6UuknQlMA3YSdJKssm6p0nam2yS7i7g0xWK2WzYNTQ0UF9fT1dXF/X19Z5o26pZK3A7cDVZr58O4G7grRGxejANRsQqSd8E/gI8B9xE9tD6iYjoSaetBCan/cnAinRtj6QnySb+ngzcWdR08TUbkTQLmAWw6667DiZsM7Nxrd9EkqRFZH+w9ykijqxIRGZmNlb8HfgG8BVeup8E8JpSF0VEX/NsXDK8oZmNrLlz53LiiSe6N5JVux0j4rS0f6OkDwIfS8OOB0XSDmS9iXYHngB+SpakqpiIuBi4GKCxsbHf3ztmZta3Uqu2fTO9vh94FfA/qXwM2VKf494FF1xAZ2dn3mHYKFD47+DEE0/MORIbDRoaGjjhhBPyDmM0OBloiIi/5R2IWd4aGhq4/vrr8w7DbMhS4kep2A1sn4aWDdjjtB/TgT9HxNrU/s+AdwETJdWmXklTgFXp/FXAVGBlGgq3fYqjUF9QfI2ZmQ2jUkPbbgeQdE5ENBYdWiSpo+KRVYHOzk7uvvd+1m+1Y96hWM4m/D17mLXsIedYx7uaZwfzN/SY1Qk8O+BZZmZWLbYnG3amorq70uuAPU778Rdgf0lbkQ1tO5hsyNytwAeAHwHNwLXp/IWp/Mt0/JaICEkLgR9KOhd4NbAH8OtBxGNmZgMo1SOpYGtJr4mIhwAk7Q5sXdmwqsf6rXbkuTcclncYZjZKbPnA4rxDGE3WAXdLupWN50ianV9IZvno6Ojgi1/8It/4xjfYb7/98g7HbFAior4Cbf5K0lVkCake4Ldkw86uB34kaX6qKwxxvoRsDr5O4DGyldqIiN9L+glwX2rn+IhYP9zxmplZeYmkk4DbJD1E9vRhN9LkdGZmZiX8PG1m495pp53Ghg0bmDdvHtddd13e4ZgNiqT6iOgqcVzA5IhYuSntRsQ8soUVij1EH6uuRcTzwAf7aedM4MxNeW8zM9t0AyaSIqJN0h7AG1LVAxHxQqlrzMzMIqJF0ubA61LVHyLixTxjMstDR0cHzzzzDADPPPMMy5Ytc68kq1bfkDSBbJjZMmAtsAXQALyHbFjaPLIV08zMbIwqp0cSKXH0uwrHYmZmY4ikaUAL0EXWo3WqpOaIWJpnXGYj7bTTTtuo7F5JVq0i4oOS9gQ+BnwK2IVsLrz7gcXAmanHkJmZjWETKv0Gkmok/VbSdam8u6RfSeqU9OP0tBpJr0jlznS8vtKxmZlZRZ0DHBIR746IA4H3AuflHJPZiCv0RuqvbFZNIuK+iPhKREyLiNdHxD4R8dGI+B8nkcwGr7u7m9mzZ9Pd3Z13KGYDqngiCTiR7ClFwdnAeRHRADwOHJfqjwMeT/XnpfPMzKx6bRYRfygUIuKPwGY5xmOWi2222aZk2czMrKWlheXLl9Pa2pp3KGYDGjCRpMzHJZ2ayrtKetnEd/1cOwU4HPh+oS3gIOCqdEoLcHTaPyqVSccPTuebmVl16pD0fUnT0vY9siWdzcaV3kPbTj/99HwCMTOzUam7u5u2tjYigra2NvdKslGvnB5JFwLvBI5J5aeB75TZ/reALwIbUrkOeCIielJ5JTA57U8GVgCk40+m883MrDr9O9kyzLPTdl+qMxtXGhsb/9ELaZtttvFE22ZmtpGWlhY2bMh+Mq9fv969kmzUKyeR9I6IOB54HiAiHgc2H+giSe8D1kTEsqGF+LJ2Z0nqkNSxdu3a4WzazMyGVy1wfkS8PyLeDywAanKOySwXp512GhMmTHBvJKtqkvYtteUdn1m1WrJkCT09WV+Lnp4e2tvbc47IrLRyVm17UVINEACSJvFSD6NS3gUcKekwsmVBtwPOByZKqk29jqYAq9L5q4CpwEpJtcD2wMv69EXExcDFAI2NjVFGHGZmlo+bgelAYWbhLYGbgANyi8gsJ42Njdxyyy15h2E2VOeUOBZkU1iY2SaaPn06ixYtIiKQxIwZM/IOyaykcnokLQCuAV4p6UzgF8BZA10UEV+OiCkRUQ98BLglIj4G3Ap8IJ3WDFyb9hemMun4LRHhRJGZWfXaIiL+sTxV2t8qx3jMzGwIIuI9JTYnkcwG6cgjj6Tw0zciOOKII3KOyKy0ARNJEXEF2TxHXwMeAY6OiJ8O4T1PAb4gqZNsDqRLUv0lQF2q/wLwpSG8h5mZ5W9d8VAHSfsBz+UYj1luOjs7Ofzww+ns7Mw7FLNhIenNkj4kaWZhyzsms2q1cOFCCutMSWLRokU5R2RWWr9D2yTtWFRcA1xZfCwiHiv3TSLiNuC2tP8Q8LJV3yLieeCD5bZpZmaj3ueBn0r6KyDgVcCH8w3JLB/z589n3bp1zJ8/n8svvzzvcMyGRNI8YBqwJ7AYOJRs1IJnCDYbhCVLlmzUI6m9vZ2TTjop56jM+leqR9IysmWalwFrgT8CD6b9YZ1A28zMxp6I+A3wBrKV2j4DvHG4F2AwqwadnZ10dXUB0NXV5V5JNhZ8ADgYWB0RxwJ7kc1vamaDMH36dGprsz4etbW1niPJRr1+E0kRsXtEvAZYAhwRETtFRB3wPrLJUs3MzEqKiBcj4t60vZh3PGZ5mD9/fsmyWRV6LiI2AD2StiMbvTA155jMqlZzczMTJmQ/zWtqapg50yNFbXQrZ7Lt/SNicaEQETfgFXfMzMzMylLojdRf2awKdUiaCHyPbKTCXcAv8w3JrHrV1dXR1NSEJJqamqirq8s7JLOSykkk/VXSXEn1afsK8NdKB2ZmZmY2FtTX15csm1WbiPhsRDwREd8FZgDNaYibmQ3SgQceiCQOPPDAvEMxG1A5iaRjgEnANcDP0v4xlQzKzMyqnzIfl3RqKu8q6WWLLZiNdXPnzi1ZNqs2km4u7EdEV0TcU1xnZpvu29/+Nhs2bOCCCy7IOxSzAfW7altBWp3txBGIxczMxpYLgQ3AQcAZwNPA1cDb8gzKbKQ1NDRQX19PV1cX9fX1NDQ05B2S2aBI2gLYCthJ0g5kK3ICbAdMzi0wsyrX16IMvlfYaFZOjyQzM7PBeEdEHA88DxARjwOb5xuSWT7mzp3L1ltv7d5IVu0+TTYn0hvI5kValrZrgW/nGJdZVfOiDFZtBuyRZGZmNkgvSqoBAkDSJLIeSmbjTkNDA9dff33eYZgNSUScD5wv6YSI8Pgbs2HiRRms2rhHkpmZVcoCsvn1XinpTOAXwFn5hmSWj7PPPptp06Zxzjnn5B2K2XD4b0mzJV2Vts9J2izvoMyqlRdlsGozYCJJ0hRJ10haK2mNpKslTRmJ4MzMrHpFxBXAF4GvAY8AR0fET/ONyiwfN9xwAwCLFi3KORKzYXEhsF96LexflGtEZlXsc5/73EblE044IadIzMpTTo+ky4CFwC7Aq4FFqc7MzOxlJO1Y2IA1wJXAD4FHU53ZuHL22WdvVHavJBsD3hYRzRFxS9qOxQspmA3a0qVLS5bNRptyEkmTIuKyiOhJ2+XApArHZWZm1WsZ0JFe1wJ/BB5M+8tyjMssF4XeSAXulWRjwHpJry0UJL0GWJ9jPGZVbcmSJRuV29vbc4rErDzlJJK6JX1cUk3aPg50VzowMzOrThGxe0S8BlgCHBERO0VEHfA+4KZ8ozMzs2Hwn8Ctkm6TdDtwC/AfOcdkVrWmT59ObW22DlZtbS0zZszIOSKz0spJJH0K+BCwmmyOiw8Ax1YyKDMzGxP2j4jFhUJE3AAckGM8ZmY2PH4B7AHMBk4AXg/8b64RmVWx5uZmJkzIfprX1NQwc+bMnCMyK61kIikt23xWRBwZEZMi4pURcXRE/GWE4jMzs+r1V0lzJdWn7SvAX/MOymykHXrooRuVjzjiiJwiMRs2v4yIFyLinrS9APwy76DMqlVdXR1NTU1Ioqmpibq6urxDMiupZCIpItYDu0nafITiMTOzseMYsjn1rgF+lvaPyTUisxyccsopG5VPPvnknCIxGxpJr5K0H7ClpH0k7Zu2acBWOYdnVtWam5t5y1ve4t5IVhVqyzjnIeB/JS0E1hUqI+LcikVlZmZVLyIeA07MOw6z0eDQQw/lhhtucG8kq3bvBT4JTAHOAZTqnwLmDLZRSROB7wNvBoJsao0/AD8G6oEu4EMR8bgkAecDhwHPAp+MiLtSO83A3NTs/IhoGWxMZiOtrq6OBQsW5B2GWVnKmSPpT8B16dxtizYzMzMzK8Mpp5zCbbfd5t5IVtUioiUi3kOWvDkoIt6TtqMi4mdDaPp8oC0i3gDsBdwPfAm4OSL2AG5OZYBDyeZn2gOYBVwEIGlHYB7wDuDtwDxJOwwhJrMR1d3dzezZs+nu9rpWNvoN2CMpIk4HkLRVRDxb+ZDMzMzMzGy0ioirh6stSdsDB5L1dCIi/g78XdJRwLR0WgtwG3AKcBTQGhEB3ClpoqRd0rntqTcsktqBJuDK4YrVrJJaWlpYvnw5ra2tnHTSSXmHY1bSgD2SJL1T0n3AA6m8l6QLKx6ZmZmZ2RgxZ84cpk2bxqmnnpp3KGajze7AWuAySb+V9H1JWwM7R8Qj6ZzVwM5pfzKwouj6lamuv3qzUa+7u5u2tjYigra2NvdKslGvnKFt3yIbD90NEBG/I3tqYGZm1i9JUyRdI2mtpDWSrpY0Je+4zPJwxx13ALB06dKcIzEbGkkTJB0wjE3WAvsCF0XEPmRzsn6p+ITU+yiG6w0lzZLUIalj7dq1w9Ws2aC1tLSwYcMGANavX09ra2vOEZmVVk4iiYhY0atqfQViMTOzseUyYCGwC/BqYFGqMxtX5szZeA5i90qyahYRG4DvDGOTK4GVEfGrVL6KLLH0aBqyRnpdk46vAqYWXT8l1fVX/zIRcXFENEZE46RJk4btg5gN1pIlS+jp6QGgp6eH9vb2nCMyK62cRNKK9NQhJG0m6T/IJsAzMzMrZVJEXBYRPWm7HPBf7DbuFHojFbhXko0BN0v6l7SC2pBExGqy3xuvT1UHA/eRPYhoTnXNwLVpfyEwU5n9gSfTELgbgUMk7ZAm2T4k1ZmNetOnT6e2Npu+uLa2lhkzZuQckVlp5SSSPgMcTzbGeBWwdyqbmZmV0i3p45Jq0vZx0jBpMzOrap8Gfko2KfZTkp6W9NQQ2jsBuELSPWS/Nc4Cvg7MkPQgMD2VARYDDwGdwPeAzwKkSba/CvwmbWcUJt42G+2am5uZMCH7aV5TU8PMmTNzjsistHJWbfsb8LERiMXMzMaWTwEXAOeRzW1xB3BsrhGZmdmQRcS2w9ze3UBjH4cO7uPcoJ+H2hFxKXDpcMZmNhLq6upoampi0aJFNDU1UVdXl3dIZiUNmEiStDvZU4L64vMj4sgBrtsCWAq8Il13VUTMS+39CKgDlgGfiIi/S3oF0ArsR/bE+sMR0TWIz2RmZjmTVAOcNdC9wmw8OOCAAzYa3nbggV6zxKqfpCN5aQGe2yLiujzjMat2zc3NdHV1uTeSVYVyhrb9HOgie6p8TtE2kBeAgyJiL7Iuqk1pHPPZwHkR0QA8DhyXzj8OeDzVn5fOMzOzKhQR64HdJG2edyxmeTvrrLM2Kp9xxhk5RWI2PCR9HTiRbC6j+4ATJX0t36jMqltdXR0LFixwbySrCgP2SAKej4gFm9pw6nb6TCpulrYADgI+mupbgNOAi4Cj0j5kqzV8W5JSO2ZmVn0eAv5X0kKy5ZwBiIhz8wvJLB+FXknujWRjxGHA3mkFNyS1AL8FvpxrVGZmNiLKSSSdL2kecBNZLyMAIuKugS5MQxuWAQ1ky4T+CXgiInrSKSvJJvEmva5IbfdIepJs+NvfyvsoZmY2yvwpbROAYZ1Pw6za9O6VZDYGTAQKk1lvn2cgZmY2sspJJL0F+ARZT6INqa7Qs6ikNLRhb0kTgWuANwwyzn+QNAuYBbDrrrsOtTkzM6uQiDgdQNJWEfFs3vGYmdmw+RrwW0m3AiKbK+lL+YZkZmYjpZw5kj4IvCYi3h0R70nbgEmkYhHxBHAr8E5goqRCAmsKsCrtrwKmAqTj29PHMtERcXFENEZE46RJkzYlDDMzG0GS3inpPuCBVN5L0oVlXHeppDWS7i2q21FSu6QH0+sOFQzdbNjNmTOHadOmceqpp+YditmQRcSVwP7Az4CrgXdGxI/zjcrMzEZKOYmke8m6rm4SSZNSTyQkbQnMAO4nSyh9IJ3WDFyb9hemMun4LZ4fycysqn0LeC/poUBE/I6XVvgp5XKgqVfdl4CbI2IP4Gb85NuqTGHVtqVLl+YcidngSdq3sAG7kE1TsRJ4daozM7NxoJyhbROBByT9ho3nSBpoSeddgJY0T9IE4CcRcV16Ov0jSfPJJuW7JJ1/CfADSZ1k460/smkfxczMRpuIWCGpuGp9GdcslVTfq/ooYFrabwFuA04ZcoBmI2DOnDkblU899VSv3GbVqtTKzWVNfWFmZtWvnETSvME0HBH3APv0Uf8Q8PY+6p8nG0ZnZmZjwwpJBwAhaTOypaLvH2RbO0fEI2l/NbDzcARoNhIKvZEK3CvJqlVEvCfvGMzMLH8DJpIi4vaRCMTMzMaczwDnk63KuYps9c/jh9poRISkfoc+e1EGM7PKk/RmYE9gi0JdRLTmF5GZmY2UPhNJxSvsSHqarKsqwObAZsC6iNhuZEI0M7NqFBF/Az42TM09KmmXiHhE0i7AmhLvezFwMUBjY6Pn2jMzG2aS5pENN94TWAwcCvwCcCLJzGwc6G+y7U9KmgsQEdtGxHYpcbQl8C/AgKvumJnZ+CZpd0nnSvqZpIWFbZDNFS/IULxQg9mod8ABB2xUPvDAcuacNxvVPgAcDKyOiGOBvchWXDYzs3Ggzx5JEXGhpI9J+kRE/KCoPoCfp6cQ437FnFWrVlHz7JNs+cDivEMxs1Gi5tluVq3qyTuM0eLnZAspLAI2lHuRpCvJnnTvJGkl2Vx9Xwd+Iuk44GHgQ8MerVmFnHXWWUybNu0fZU+0bWPAcxGxQVKPpO3IeolOzTsoMzMbGf3OkRQRVwBIen9R9QSgEXi+wnGZmVn1ez4iFmzqRRFxTD+HDh5iPGa5OeCAA7jjjjvcG8nGig5JE4HvAcuAZ4Bf5huSmZmNlHJWbTuiaL8H6CJbhnncmzx5MqtfqOW5NxyWdyhmNkps+cBiJk/2gmLJ+akH603AC4XKiLgrv5DM8nHWWWflHYLZkEn6DvDDiPhsqvqupDZgu7Ris5mZjQPlrNp27EgEYmZmY85bgE8AB/HS0LZIZTMzqz5/BL6ZFj34CXBlRPw255jMxoTu7m5OP/105s2bR11dXd7hmJXUbyJJ0qklrouI+GoF4jEzs7Hjg8BrIuLveQdilrc5c+b8Y2ib50iyahUR55P1Nt0N+AhwqaQtgSvJkkp/zDVAsyrW0tLC8uXLaW1t5aSTTso7HLOS+lu1DWBdHxvAccApFY7LzMyq373AxLyDMBsN7rjjDgCWLl2acyRmQxcRD0fE2RGxD3AMcDRwf85hmVWt7u5ubrjhBiKCxYsX093dnXdIZiX1m0iKiHMKG3AxsCVwLPAj4DUjFJ+ZmVWvicADkm6UtLCw5R2U2UibM2fORuVTTy3V6dts9JNUK+kISVcANwB/AN4/wGVm1o+WlhZefPFFAF588UVaW1tzjsistJJzJEnaEfgC8DGgBdg3Ih4ficDMzKzqzcs7ALPRoNAbqcC9kqxaSZpB1gPpMODXZA+YZ0XEupIXmllJN91000blG2+80cPbbFQrNUfSN8ieLFwMvCUinhmxqMzMrOpFxO15x2BmZsPqy8APgZP9cNls+NTW1pYsm402pf4LPZlsuea5wFckFepFNtn2dhWOzczMqoykrSLi2bT/NNkqbQCbA5sB63z/sP/f3v3H+H3X9wF/vuw04EKYSWuZ4CQz6CJNqSpUdEpQmJApGgtsnSttq2ATSRAo/4DlSkgjraLxoytimtbOjUo1y0SECjVDa7eiEcYiijUhfjShpZQkpZxSR7FJE4cEEpYE5OS1P+5z4c7x2V/ju/t8v+fHQzp9v+/353Pn51ey7q33696f9xuYTd3t1E1YBz/84Q9P24Zpc7o9krZ097buvqi7X7bs6yKTAABWcUNV3Zwky8ePLO6z9y+TfGzUdDCCa665ZkX7DW94w0hJAJhGu3fvPm0bps3pTm0DgLPS3R9L8ndV9Y6T+ru7/2eSfzpOMhjPRz7ykRXtD3/4wyMlAWAaXXfddSva119//UhJYDIevgRgTXX3p5Kkqpaf4LMlyXySZ0YJBSO75ppr8uUvf9lqJABe4ORT2m677ba88Y1vHCkNnJlCEgDr5VeWvT+R5EiSveNEgXGdvCoJAJYcOXLktG2YNgpJAKyL7n7n2BlgWtxwww05cuRI5ubmcujQobHjADBFdu/evaJ4ZI8kpp1CEgBrqqr+/Wkud3f/1oaFgSmxNEFYWFgYNwgAU+fmm2/Ou9/97hVtmGY22wZgrf2/U3wlybuSvH+sUDCWG264YUV7+WQBWFRVW6vqL6vqfw3tV1XV16pqoar+W1VdOPS/aGgvDNd3L/sZvzH0f7uqHO4AsE4UkgBYU939n5e+khxMsi3JO5PcnuTVo4aDEZy814VVSXBK+5Pct6z9H5P8bnfPJXk8i3+MyPD6+ND/u8N9qaork7wtyS8kuTbJx6pq6wZlh3PywQ9+cEX7Qx/60DhBYEIKSQCsuaq6uKr+Q5JvZvEx6td29/u7+5GRowEwZarq0iT/LMmhoV1JfjnJfx9uuS3Jrw7v9w7tDNffNNy/N8nt3f2j7v67JAtJrtqYTwDn5ujRoyvaDz744EhJYDIKSQCsqar6T0nuSvJkkl/s7g929+MjxwJgev2XJP8uyXND++eSfL+7Twzto0l2De93JXkwSYbrPxjuf77/FN8DwBpSSAJgrb0vySuT3Jzku1X1xPD1ZFU9MXI22HAnn74zNzc3ThCYQlX1z5M80t1f38B/88aquruq7j5+/PhG/bMAm4ZCEgBrqru3dPe27r6ou1+27Oui7n7Z2Plgo33iE59Y0T506NA4QWA6vT7Jv6iqI1ncS++XkxxIsr2qlk6YvjTJseH9sSSXJclw/R8k+d7y/lN8zwrdfbC757t7fseOHWv7aQDOA+tWSKqqy6rqi1V1b1XdU1X7h/6Lq+rOqvrO8Pryob+q6veGkxa+WVWvXa9sAAAbaWlVktVIsFJ3/0Z3X9rdu7O4Wfafdfe/TfLFJP9quO36JH86vP/M0M5w/c+6u4f+tw2nur0qyRVJ/nyDPgacky1btpy2DdPmgjPf8lM7keR93f0XVXVRkq9X1Z1Jbkjyhe7+aFXdlOSmLB4H/ZYs/sK/IsnVSf5geAUAmGknr0oCzuj9SW4fDm74yyQfH/o/nuQPq2ohyWNZLD6lu++pqk8nuTeL85D3dPezGx8bzt7VV1+dr3zlKyvaMM3WrZDU3Q8leWh4/2RV3ZfFDe/2Jtkz3HZbksNZHCj2Jvnk8BeFr1bV9qq6ZPg5AADAJtbdh7M4N0h3359TnLrW3c8k+derfP9vJ/nt9UsI6+PIkSMr2g888MA4QWBC67ki6XlVtTvJLyX5WpKdy4pDf59k5/B+tZMWFJIAgJm2Z8+e598fPnx4tBwATJ+HHlo55f3ud787UhKYzLo/fFlVL03yx0l+vbtXnNYzrD7qs/x5TlkAAAAAGMG6FpKq6meyWET6VHf/ydD9cFVdMly/JMkjQ/9EJy04ZQEAmCXLVyOdqg0AMEvW89S2yuJmePd19+8su7T8pIWTT2C4bji97XVJfmB/JAAAADYzp7Yxa9Zzj6TXJ3lHkr+uqm8Mfb+Z5KNJPl1V70ryQJJfG67dkeStSRaSPJXkneuYDQAAAEa3c+fOFfskveIVrxgxDZzZep7a9qUktcrlN53i/k7ynvXKAwAAANPmkUceWdF++OGHR0oCk7FmDgAAAEayuKZi9TZMG4UkAIB1dPjw4dO2ATi/vfKVr1zR3rVr10hJYDIKSQAAADCSRx99dEX7+PHjIyWByaznZtsAAMQqJABW9+IXvzjPPPPM8+1t27aNmAbOzIokAAAAGMn3v//9Fe3HH398pCQwGSuSAADW2Z49e55/b3USADDLrEgCAAAAYCIKSQAA62j5aqRTtQEAZolCEgAAAAATUUgCAAAAYCIKSQAAAABMRCEJAAAAgIkoJAEArKPDhw+ftg0AMEsUkgAAAACYyAVjBwAA2OysQgIANguFJACAdbZnz57n3ysqAQCzTCEJgJlRVUeSPJnk2SQnunt+3EQAAHB+UUg6R1ufeizb/uaOsWMwsi3PPJEkee7FLxs5CWPb+tRjSXaOHWOze2N3Pzp2CJjU8tVIS22rkgCAWaWQdA7m5ubGjsCUWFh4Mkky92oFBHb63QAAAGxaCknnYN++fWNHYErs378/SXLgwIGRk8Cm10n+T1V1kv/a3QdPvqGqbkxyY5JcfvnlGxwPAAA2ty1jBwCAs/CPu/u1Sd6S5D1V9YaTb+jug909393zO3bs2PiEAACwiSkkATAzuvvY8PpIkv+R5KpxEwEAwPlFIQmAmVBVL6mqi5beJ3lzkm+NmwrO7OSNtW20DT9RVZdV1Rer6t6quqeq9g/9F1fVnVX1neH15UN/VdXvVdVCVX2zql677GddP9z/naq6fqzPBLDZKSQBMCt2JvlSVf1Vkj9P8tnu/t8jZwLg3JxI8r7uvjLJ67L42PKVSW5K8oXuviLJF4Z2svho8xXD141J/iBZLDwl+UCSq7O4WvUDS8UnANaWfOJnCgAACFxJREFUzbYBmAndfX+S14ydA34aViHBqXX3Q0keGt4/WVX3JdmVZG+SPcNttyU5nOT9Q/8nu7uTfLWqtlfVJcO9d3b3Y0lSVXcmuTbJH23YhwE4T6zbiqSqurWqHqmqby3rO+slqgAAwOZXVbuT/FKSryXZORSZkuTvs7gqNVksMj247NuODn2r9QOwxtbz0bZPZPGvAMud1RJVAIDNYM+ePc9/AS9UVS9N8sdJfr27n1h+bVh91Gv4b91YVXdX1d3Hjx9fqx8LcN5Yt0JSd//fJI+d1L03i0tTM7z+6rL+T/airyZZWqIKAABsYlX1M1ksIn2qu/9k6H54aT4wvD4y9B9Lctmyb7906Fut/wW6+2B3z3f3/I4dO9bugwCcJzZ6s+2zXaIKADDTTl6FZFUS/ERVVZKPJ7mvu39n2aXPJFk6ee36JH+6rP+6YWuM1yX5wTC/+HySN1fVy4ftM9489AGwxkbbbLu7u6rOeolqVd2Yxcffcvnll695LgAAYMO8Psk7kvx1VX1j6PvNJB9N8umqeleSB5L82nDtjiRvTbKQ5Kkk70yS7n6sqn4ryV3DfR9e2ngbgLW10YWkh6vqku5+aMIlqi/Q3QeTHEyS+fn5NXtWGgAA2Fjd/aUktcrlN53i/k7ynlV+1q1Jbl27dACcykY/2na2S1QBAAAAmBLrtiKpqv4oyZ4kP19VR5N8IGe5RBUAAACA6bFuhaTufvsql85qiSoAwCw7fPjwig22Dx8+PFoWAIBztdGPtgEAAAAwo0Y7tQ0A4HxhFRIAsFlYkQQAAADARBSSAAAAAJiIQhIAAAAAE1FIAgAAAGAiNtsGANbNLbfckoWFhbFjjO7YsWNJkl27do2cZHxzc3PZt2/f2DGAKWGcOLX9+/ePHWE0xonpp5AEALDOnn766bEjAACsCYUkAGDd+IvioqW/LB84cGDkJADTxTiR7Nmz5wV9xgummT2SAAAAYCTbtm1b0X7JS14yUhKYjEISAAAAjORzn/vcivZnP/vZkZLAZBSSAAAAYApYjcQssEcSAAAAjOg1r3lNEnsjMRusSAIAAABgIgpJAAAAAExEIQkAAACAidgjCQAAYAS33HJLFhYWxo7BFFj6f7B///6RkzAN5ubmsm/fvrFjrEohCQDWiQkCS0wQWG7aJwhsnIWFhXzjW/fl2Z+9eOwojGzLjztJ8vX7Hx45CWPb+tRjY0c4I4UkAFgnJggsMUFgySxMENhYz/7sxXn6H7117BjAlNj2N3eMHeGMFJIAYB2ZIADLzcIEAQBORyEJAABgBMeOHcvWp36gwAg8b+tT38uxYyfGjnFaTm0DAAAAYCIKSQAAACPYtWtXkho7BlNgyzNPZMszT4wdg6lQw++G6eXRNgAAgBHMzc2NHYEpsbDwZJJk7tU7R07C+HZO/e8GhSQAAIAR7Nu3b+wITIn9+/cnSQ4cODByEjizqXq0raqurapvV9VCVd00dh4AAGB2mE8ArL+pKSRV1dYkv5/kLUmuTPL2qrpy3FQAAMAsMJ8A2BjT9GjbVUkWuvv+JKmq25PsTXLvqKk4o1tuuSULCwtjxxjV0udfWpJ6Ppubm7NMGwbHjh3L1ie/l5f+xR+OHWU8zz2bdI+dgmlSlWzZOnaK8Tx7YuqPdZ5h5hMzyFxikfnET5hPTL9pKiTtSvLgsvbRJFePlAXOyrZt28aOAEyh7du35+mnnx47xqh+9KMf5bnnnhs7BlNky5YtedGLLhw7xoguzPbt28cOsVlNNJ+oqhuT3Jgkl19++cYkgzMwn2CWTFMhaSJ+8U8f1WKAUzt06NDYEQA4SXcfTHIwSebn5y2ZHJm5BMyeqdkjKcmxJJcta1869K3Q3Qe7e76753fs2LFh4QAAgKk20XwCgHMzTYWku5JcUVWvqqoLk7wtyWdGzgQAAMwG8wmADTA1j7Z194mqem+SzyfZmuTW7r5n5FgAAMAMMJ8A2BhTU0hKku6+I8kdY+cAAABmj/kEwPqbpkfbAAAAAJhiCkkAzIyquraqvl1VC1V109h5AADgfKOQBMBMqKqtSX4/yVuSXJnk7VV15bipAADg/KKQBMCsuCrJQnff390/TnJ7kr0jZwIAgPOKQhIAs2JXkgeXtY8OfQAAwAZRSAJgU6mqG6vq7qq6+/jx42PHAQCATUUhCYBZcSzJZcvalw59K3T3we6e7+75HTt2bFg4AAA4H1R3j53hp1ZVx5M8MHYOGPx8kkfHDgGDf9jdm6qKUlUXJPnbJG/KYgHpriT/prvvOc33GCeYJsYJpsmmGyd+GsYJpoxxgmmy6jhxwUYnWUsGP6ZJVd3d3fNj54DNqrtPVNV7k3w+ydYkt56uiDR8j3GCqWGcgOljnGCaGCeYFTNdSALg/NLddyS5Y+wcAABwvrJHEgAAAAATUUiCtXNw7AAATDXjBACnY5xgJsz0ZtsAAAAAbBwrkgAAAACYiEISrIGquraqvl1VC1V109h5AJguxgkAVmOMYNZ4tA3OUVVtTfK3Sf5JkqNJ7kry9u6+d9RgAEwF4wQAqzFGMIusSIJzd1WShe6+v7t/nOT2JHtHzgTA9DBOALAaYwQzRyEJzt2uJA8uax8d+gAgMU4AsDpjBDNHIQkAAACAiSgkwbk7luSyZe1Lhz4ASIwTAKzOGMHMUUiCc3dXkiuq6lVVdWGStyX5zMiZAJgexgkAVmOMYOZcMHYAmHXdfaKq3pvk80m2Jrm1u+8ZORYAU8I4AcBqjBHMourusTMAAAAAMAM82gYAAADARBSSAAAAAJiIQhIAAAAAE1FIAgAAAGAiCkkAAAAATEQhCQAAAICJKCQBAAAAMBGFJAAAAAAm8v8B3B7oBzCW/owAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_DTXXCctQYpv",
        "colab_type": "text"
      },
      "source": [
        "Analisando o boxplot vemos que a recência não apresenta outliers, logo nossa análise ficará mais focada na frequência e na monetaridade. Tendo em vista que a melhor situação possível ocorre quando temos grandes frequência e monetaridade, podemos procurar aqueles clientes \"ideais\", ou seja, aqueles que compram muitas vezes e gastam muito."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VrHTCbxhRZxV",
        "colab_type": "text"
      },
      "source": [
        "## 3) Procurando os \"melhores\" clientes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UhXmF0lhSrC6",
        "colab_type": "text"
      },
      "source": [
        "Dos resultados acima, buscaremos encontrar aqueles clientes considerados ideais."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjW7du6tTDwV",
        "colab_type": "text"
      },
      "source": [
        "- Considerando a frequência"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dEbJnwP4TM63",
        "colab_type": "text"
      },
      "source": [
        "Considerando o boxplot, podemos ver que os outliers são os pontos acima do valor máximo e agora podemos calcular este valor máximo para selecionar estes clientes fazendo uma simples query no nosso dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hel5Na8A77N0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Cálculo do valor máximo\n",
        "coluna = df['frequency']\n",
        "Q1, Q3 = coluna.quantile(q=[.25, .75])\n",
        "V_max = Q3 + 1.5 * (Q3 - Q1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZWtSTonS7Zm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1351acd7-b47f-4471-e464-5f4c6f2632f3"
      },
      "source": [
        "# Criação do dataframe para frequência\n",
        "df_freq = df.query(f'frequency > {V_max}')\n",
        "df_freq.head()"
      ],
      "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 th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_unique_id</th>\n",
              "      <th>recency</th>\n",
              "      <th>R</th>\n",
              "      <th>frequency</th>\n",
              "      <th>F</th>\n",
              "      <th>monetary</th>\n",
              "      <th>M</th>\n",
              "      <th>score</th>\n",
              "      <th>RFM_level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000fbf0473c10fc1ab6f8d2d286ce20c</td>\n",
              "      <td>39</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>285.80</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0064a1b5f5cddd047c987b988a90c8c1</td>\n",
              "      <td>186</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>215.60</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0096c18412349537abc45e350581cbbc</td>\n",
              "      <td>538</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>679.60</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>Bom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0097fca0db567f5ca79509b7b4fc1c2d</td>\n",
              "      <td>137</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>172.14</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>00ae50eb5e1d2514f694dee1dcbbd5ae</td>\n",
              "      <td>116</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>213.50</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  customer_unique_id  recency  R  ...  M  score  RFM_level\n",
              "1   000fbf0473c10fc1ab6f8d2d286ce20c       39  4  ...  1      7      Ótimo\n",
              "18  0064a1b5f5cddd047c987b988a90c8c1      186  3  ...  1      6      Ótimo\n",
              "26  0096c18412349537abc45e350581cbbc      538  0  ...  2      4        Bom\n",
              "27  0097fca0db567f5ca79509b7b4fc1c2d      137  3  ...  1      7      Ótimo\n",
              "30  00ae50eb5e1d2514f694dee1dcbbd5ae      116  3  ...  1      7      Ótimo\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "miHvGiDTfv4Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6da47562-0587-4fbf-9c75-19d269fe74a2"
      },
      "source": [
        "df_freq.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(848, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lglexXwCLbKS",
        "colab_type": "text"
      },
      "source": [
        "- Considerando a monetaridade"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OJQGDFamLkpy",
        "colab_type": "text"
      },
      "source": [
        "Analogamente ao caso anterior, podemos selecionar os outliers levando em consideração a coluna de monetaridade segundo a seguinte query."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_CrBO78LkHX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Cálculo do valor máximo\n",
        "coluna = df['monetary']\n",
        "Q1, Q3 = coluna.quantile(q=[.25, .75])\n",
        "V_max = Q3 + 1.5 * (Q3 - Q1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kngRicaAK-fp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "93528dc4-278c-4f45-c40e-689d25388c97"
      },
      "source": [
        "# Criação do dataframe para monetaridade\n",
        "df_mon = df.query(f'monetary > {V_max}')\n",
        "df_mon.head()"
      ],
      "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 th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_unique_id</th>\n",
              "      <th>recency</th>\n",
              "      <th>R</th>\n",
              "      <th>frequency</th>\n",
              "      <th>F</th>\n",
              "      <th>monetary</th>\n",
              "      <th>M</th>\n",
              "      <th>score</th>\n",
              "      <th>RFM_level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>005226be8d5b972d0f72b4e8a79f322b</td>\n",
              "      <td>155</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1561.0</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>00adeda9b742746c0c66e10d00ea1b74</td>\n",
              "      <td>300</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2078.5</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>00c07da5ba0e07b4f248a3a373b07476</td>\n",
              "      <td>38</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2258.0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>011875f0176909c5cf0b14a9138bb691</td>\n",
              "      <td>534</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3999.9</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>Bom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0129d48dce554d95f641d01eaa88904a</td>\n",
              "      <td>172</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1390.0</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  customer_unique_id  recency  R  ...  M  score  RFM_level\n",
              "15  005226be8d5b972d0f72b4e8a79f322b      155  3  ...  3      6      Ótimo\n",
              "29  00adeda9b742746c0c66e10d00ea1b74      300  2  ...  3      6      Ótimo\n",
              "33  00c07da5ba0e07b4f248a3a373b07476       38  4  ...  3      7      Ótimo\n",
              "41  011875f0176909c5cf0b14a9138bb691      534  0  ...  4      4        Bom\n",
              "46  0129d48dce554d95f641d01eaa88904a      172  3  ...  3      6      Ótimo\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWZBMLEDMhQA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9b216bac-74cd-47c0-89bb-b40902cdfdef"
      },
      "source": [
        "df_mon.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(702, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2e9o7UQuP6FY",
        "colab_type": "text"
      },
      "source": [
        "Podemos agora pegar os clientes que estão nos dois dataframes, ou seja, aqueles clientes com alto número de compras com altos valores gastos. Para isso, criaremos dois conjuntos (um para cada variável) contendo os índices dos clientes e pegaremos a interseção desses grupos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gxYBQ23XdUk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Criação dos conjuntos com os índices dos clientes\n",
        "outliers_freq = set(df_freq.index)\n",
        "outliers_mon = set(df_mon.index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMJutHXZQJ9T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "447e6bb9-3372-4290-882d-0c3987b717ba"
      },
      "source": [
        "clientes = outliers_freq.intersection(outliers_mon)\n",
        "print(f'Temos então {len(clientes)} clientes nestas condições.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Temos então 55 clientes nestas condições.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lzE7LbnK7lES",
        "colab_type": "text"
      },
      "source": [
        "Ou seja, ao todo temos 55 clientes que compraram muitas vezes e gastaram também muito dinheiro, comportamento adequado para aqueles clientes considerados ideais. Este tipo de cliente precisa de um estudo mais profundo para entendermos se tratam-se de outliers, de alguma tentativa de fraude ou se realmente são clientes assíduos pelas compras online. Para isso podemos criar um novo dataframe com estes clientes e estudá-lo fazendo uma filtragem segundo os seguintes passos:\n",
        "\n",
        "1) Encontrar a média das frequências;\n",
        "\n",
        "2) Encontrar a média da monetaridade;\n",
        "\n",
        "3) Classificar os clientes de acordo com o critério:\n",
        "\n",
        "- Acima da média em ambas as medidas: ***cliente ideal***\n",
        "- Acima da média em uma medida e abaixo da média na outra um possível outlier ou ainda um possível caso de fraude. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kNv60HJEZhD6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a6bad29f-98e3-4a5a-930c-24f08269fb47"
      },
      "source": [
        "# Criação do dataframe clientes_top\n",
        "clientes_top = df.iloc[list(clientes),:].sort_index(ascending=True)\n",
        "clientes_top.head()"
      ],
      "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 th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_unique_id</th>\n",
              "      <th>recency</th>\n",
              "      <th>R</th>\n",
              "      <th>frequency</th>\n",
              "      <th>F</th>\n",
              "      <th>monetary</th>\n",
              "      <th>M</th>\n",
              "      <th>score</th>\n",
              "      <th>RFM_level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>0341bbd5c969923a0f801b9e2d10a7b8</td>\n",
              "      <td>96</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>1828.44</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>386</th>\n",
              "      <td>0a0a92112bd4c708ca5fde585afaa872</td>\n",
              "      <td>339</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>13440.00</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>573</th>\n",
              "      <td>0f5ac8d5c31de21d2f25e24be15bbffb</td>\n",
              "      <td>280</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>1252.35</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>675</th>\n",
              "      <td>12138e8ba93973c9b561253683f5581b</td>\n",
              "      <td>90</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>1330.00</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024</th>\n",
              "      <td>1b76903617af13189607a36b0469f6f3</td>\n",
              "      <td>104</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>3099.75</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    customer_unique_id  recency  R  ...  M  score  RFM_level\n",
              "116   0341bbd5c969923a0f801b9e2d10a7b8       96  4  ...  3     10      Ótimo\n",
              "386   0a0a92112bd4c708ca5fde585afaa872      339  1  ...  4      8      Ótimo\n",
              "573   0f5ac8d5c31de21d2f25e24be15bbffb      280  2  ...  3      9      Ótimo\n",
              "675   12138e8ba93973c9b561253683f5581b       90  4  ...  3     10      Ótimo\n",
              "1024  1b76903617af13189607a36b0469f6f3      104  4  ...  4     11      Ótimo\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQ5qUmggg_-B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1dfa220a-4aa8-4ff1-dd2a-8cbc9c005db1"
      },
      "source": [
        "clientes_top.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(55, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EqtJMoG49tOj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "d8fb8c28-fdcb-4ba6-fce3-22a846ac5788"
      },
      "source": [
        "# Valor médio da frequência\n",
        "media_f = clientes_top['frequency'].mean()\n",
        "\n",
        "# Valor médio da monetaridade\n",
        "media_m = clientes_top['monetary'].mean()\n",
        "\n",
        "print(f'Valor médio da frequência: {media_f} \\nValor médio da monetaridade: {media_m}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Valor médio da frequência: 7.236363636363636 \n",
            "Valor médio da monetaridade: 2129.6076363636357\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H1h9HTQw-hlv",
        "colab_type": "text"
      },
      "source": [
        "Feito isso, faremos uma seleção dos clientes segundo os critérios estabelecidos acima."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1XIAiune_ihL",
        "colab_type": "text"
      },
      "source": [
        "- Clientes ideais"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSu8cLiW-B2W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "61eb7dea-120f-473b-dd86-38f6da0e3a06"
      },
      "source": [
        "clientes_ideais = clientes_top.query(f'frequency > {media_f} & monetary > {media_m}')\n",
        "clientes_ideais.head()"
      ],
      "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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_unique_id</th>\n",
              "      <th>recency</th>\n",
              "      <th>R</th>\n",
              "      <th>frequency</th>\n",
              "      <th>F</th>\n",
              "      <th>monetary</th>\n",
              "      <th>M</th>\n",
              "      <th>score</th>\n",
              "      <th>RFM_level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>386</th>\n",
              "      <td>0a0a92112bd4c708ca5fde585afaa872</td>\n",
              "      <td>339</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>13440.0</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5009</th>\n",
              "      <td>86df00dc5fd68f4dd5d5945ca19f3ed6</td>\n",
              "      <td>325</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>2200.0</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7473</th>\n",
              "      <td>c8460e4251689ba205045f3ea17884a1</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "      <td>24</td>\n",
              "      <td>4</td>\n",
              "      <td>4080.0</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8948</th>\n",
              "      <td>ef8d54b3797ea4db1d63f0ced6a906e9</td>\n",
              "      <td>137</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>2849.9</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    customer_unique_id  recency  R  ...  M  score  RFM_level\n",
              "386   0a0a92112bd4c708ca5fde585afaa872      339  1  ...  4      8      Ótimo\n",
              "5009  86df00dc5fd68f4dd5d5945ca19f3ed6      325  2  ...  3      9      Ótimo\n",
              "7473  c8460e4251689ba205045f3ea17884a1       26  4  ...  4     12      Ótimo\n",
              "8948  ef8d54b3797ea4db1d63f0ced6a906e9      137  3  ...  4     11      Ótimo\n",
              "\n",
              "[4 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pd_QiINe_buI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "31f89875-4af4-47f0-874d-c479749925ce"
      },
      "source": [
        "clientes_ideais.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x65WLLrmHaZp",
        "colab_type": "text"
      },
      "source": [
        "- Outliers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TxiUWM9DHeXx",
        "colab_type": "text"
      },
      "source": [
        "Podemos aqui fazer uma nova divisão."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bzbnc3EnHqPo",
        "colab_type": "text"
      },
      "source": [
        "1) Frequência acima da média e monetaridade abaixo da média "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9xtSuXXHdH4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "052a3373-3679-42b8-bc26-46a950d924b5"
      },
      "source": [
        "freq_acima_media = clientes_top.query(f'frequency > {media_f} & monetary < {media_m}')\n",
        "freq_acima_media.head()"
      ],
      "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 th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_unique_id</th>\n",
              "      <th>recency</th>\n",
              "      <th>R</th>\n",
              "      <th>frequency</th>\n",
              "      <th>F</th>\n",
              "      <th>monetary</th>\n",
              "      <th>M</th>\n",
              "      <th>score</th>\n",
              "      <th>RFM_level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>573</th>\n",
              "      <td>0f5ac8d5c31de21d2f25e24be15bbffb</td>\n",
              "      <td>280</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>1252.35</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1122</th>\n",
              "      <td>1da09dd64e235e7c2f29a4faff33535c</td>\n",
              "      <td>235</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>1980.28</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1193</th>\n",
              "      <td>1f98d2384ff7a372e6a2d3bb75cbcd54</td>\n",
              "      <td>95</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>1170.00</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1225</th>\n",
              "      <td>20a5257c01689ac69410a14cb51bb447</td>\n",
              "      <td>356</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>1379.00</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2268</th>\n",
              "      <td>3d47f4368ccc8e1bb4c4a12dbda7111b</td>\n",
              "      <td>336</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>1879.90</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    customer_unique_id  recency  R  ...  M  score  RFM_level\n",
              "573   0f5ac8d5c31de21d2f25e24be15bbffb      280  2  ...  3      9      Ótimo\n",
              "1122  1da09dd64e235e7c2f29a4faff33535c      235  2  ...  3      9      Ótimo\n",
              "1193  1f98d2384ff7a372e6a2d3bb75cbcd54       95  4  ...  3     11      Ótimo\n",
              "1225  20a5257c01689ac69410a14cb51bb447      356  1  ...  3      8      Ótimo\n",
              "2268  3d47f4368ccc8e1bb4c4a12dbda7111b      336  1  ...  3      8      Ótimo\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLKkElYUH8V6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "af4061ed-805e-4d51-8600-aa76b9028f1b"
      },
      "source": [
        "freq_acima_media.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mXK-dhb3H_nz",
        "colab_type": "text"
      },
      "source": [
        "2) Frequência abaixo da média e monetaridade acima da média"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g70UGvVkH9db",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "131b6887-9c62-4149-f084-eb8d796aa3f7"
      },
      "source": [
        "monet_acima_media = clientes_top.query(f'frequency < {media_f} & monetary > {media_m}')\n",
        "monet_acima_media.head()"
      ],
      "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",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_unique_id</th>\n",
              "      <th>recency</th>\n",
              "      <th>R</th>\n",
              "      <th>frequency</th>\n",
              "      <th>F</th>\n",
              "      <th>monetary</th>\n",
              "      <th>M</th>\n",
              "      <th>score</th>\n",
              "      <th>RFM_level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1024</th>\n",
              "      <td>1b76903617af13189607a36b0469f6f3</td>\n",
              "      <td>104</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>3099.75</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2375</th>\n",
              "      <td>4007669dec559734d6f53e029e360987</td>\n",
              "      <td>283</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>5934.60</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4387</th>\n",
              "      <td>763c8b1c9c68a0229c42c9fc6f662b93</td>\n",
              "      <td>50</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>7160.00</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5558</th>\n",
              "      <td>94c1c602b16a9fdfbc154ef63cfa50f9</td>\n",
              "      <td>515</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2492.50</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6482</th>\n",
              "      <td>adfa1cab2b2c8706db21bb13c0a1beb1</td>\n",
              "      <td>94</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2919.40</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>Ótimo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    customer_unique_id  recency  R  ...  M  score  RFM_level\n",
              "1024  1b76903617af13189607a36b0469f6f3      104  4  ...  4     11      Ótimo\n",
              "2375  4007669dec559734d6f53e029e360987      283  2  ...  4      9      Ótimo\n",
              "4387  763c8b1c9c68a0229c42c9fc6f662b93       50  4  ...  4     10      Ótimo\n",
              "5558  94c1c602b16a9fdfbc154ef63cfa50f9      515  0  ...  3      6      Ótimo\n",
              "6482  adfa1cab2b2c8706db21bb13c0a1beb1       94  4  ...  4     11      Ótimo\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "391W2_4pIJ_-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b0559e3c-2efc-478d-8f0b-6b5348cf2d02"
      },
      "source": [
        "monet_acima_media.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iSofe4TApaKF",
        "colab_type": "text"
      },
      "source": [
        "## 4) Conclusão"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-zqUUNwpeiZ",
        "colab_type": "text"
      },
      "source": [
        "Após a análise apresentada concluimos que há alguns clientes que podem ser considerados os ideais pela empresa (dispostos no dataframe clientes_ideais). Teoricamente este grupo contém os clientes mais dispostos a gastar e comprar virtualmente, entretanto ainda é valido o estudo de cada um separadamente para confirmar que de fato não se trata de uma anomalia ou tentativa de fraude.\n",
        "\n",
        "Por outro lado temos ainda os clientes que tem comportamentos opostos para frequência e monetaridade o que pode representar uma probabilidade maior de ser um outlier ou uma fraude. Estes grupos (dispostos nos dataframes freq_acima_media e monet_acima_media) necessitam de um estudo ainda mais profundo para confirmar ou não suas condições. O fato é que se há alguma discrepância dos clientes ou uma tentativa de fraudar o sistema estes 55 clientes devem ser o ponto de partida para esta investigação pois são os dados que mais se destacam neste sentido."
      ]
    }
  ]
}