01. Arvores_Decisao/2020_02_AP_04_ArvDecisão_AED.ipynb

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  "metadata": {
    "colab": {
      "name": "2020.02_AP_04_ArvDecisão_AED_Eduardo_Thiago_Wendel.ipynb",
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      "collapsed_sections": [
        "-_Rom_E8Yrt2",
        "4dZV_3fGZNpr",
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        "ao9brJj5T136",
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9we-KiwSP51"
      },
      "source": [
        "**Instituto de Informática - UFG**\n",
        "\n",
        "Disciplina: Inteligência Artificial\n",
        "\n",
        "Prof. Cedric Luiz de Carvalho\n",
        "\n",
        "\n",
        "Grupo (A)\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Aluna(o): Eduardo Barros Pimenta - Gerente\n",
        "\n",
        "---\n",
        "\n",
        "Aluna(o): Thiago Emanuell Vieira Moura - Engenheiro de Conhecimento\n",
        "\n",
        "---\n",
        "\n",
        "Aluna(o): Wendel Marques de Jesus Souza - Analista de Dados\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e0IEfqwfSX5N"
      },
      "source": [
        "# Objetivos deste artefato"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SfGRAAi2Sbnd"
      },
      "source": [
        "O objetivo geral é a obtenção de *insights* sobre o conjunto de dados.  Para tanto, serão utilizadas técnicas de visualização de dados e alguns cálculos estátisticos básicos. Essa etapa é chamada de análise exploratória de dados (AED). Por meio dela, é possível também encontrar problemas, padrões e relacionamentos que poderão apoiar a etapa de pré-processamento, que antecede a implementação. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_Rom_E8Yrt2"
      },
      "source": [
        "# Descrição geral da base de dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4dZV_3fGZNpr"
      },
      "source": [
        "### O conjunto de dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gr7zeqakY-Qp"
      },
      "source": [
        "É um conjunto de dados sintético gerado usando o simulador chamado PaySim. PaySim usa dados agregados do conjunto de dados privado para gerar um conjunto de dados sintético que se assemelha à operação normal de transações e injeta comportamento malicioso para avaliar posteriormente o desempenho dos métodos de detecção de fraude.\n",
        "\n",
        "Os logs originais foram fornecidos por uma empresa multinacional, fornecedora do serviço financeiro móvel que atualmente opera em mais de 14 países em todo o mundo. O conjunto de dados sintético é reduzido em 1/4 do conjunto de dados original\n",
        "\n",
        "Tradução livre da descrição disponível em [conjunto de dados financeiros sintéticos para detecção de fraude\n",
        "](https://www.kaggle.com/ntnu-testimon/paysim1)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vaTR91taZQcH"
      },
      "source": [
        "### Headers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MCYDQOyUZXQx"
      },
      "source": [
        "Este é um exemplo de 1 linha com explicação de cabeçalhos:\n",
        "\n",
        "\n",
        "**1,\tCASH_OUT,\t181.00,\tC840083671,\t181.0,\t0.00,\tC38997010,\t21182.0,\t0.0,\t1,\t0**\n",
        "\n",
        "Cada campo corresponde a respectivamente:\n",
        "\n",
        "*  **step** - mapeia uma unidade de tempo no mundo real. Neste caso, 1 etapa corresponde a 1 hora de tempo. Total de etapas 744 (simulação de 30 dias).\n",
        "\n",
        "* **tipo** - CASH-IN, CASH-OUT, DEBIT, PAYMENT ou TRANSFER.\n",
        "\n",
        "* **montante** - montante da transação em moeda local.\n",
        "\n",
        "* **nameOrig** - cliente que iniciou a transação\n",
        "\n",
        "* **oldbalanceOrg** - saldo inicial antes da transação\n",
        "\n",
        "* **newbalanceOrig** - novo saldo após a transação\n",
        "\n",
        "* **nameDest** - cliente que é o destinatário da transação\n",
        "\n",
        "* **oldbalanceDest** - destinatário do saldo inicial antes da transação. Observe que não há informações para clientes que começam com M (Comerciantes).\n",
        "newbalanceDest - novo destinatário do saldo após a transação. Observe que não há informações para clientes que começam com M (Comerciantes).\n",
        "\n",
        "* **isFraud** - São as transações feitas pelos agentes fraudulentos dentro da simulação. Neste conjunto de dados específico, o comportamento fraudulento dos agentes visa lucrar ao assumir o controle das contas dos clientes e tentar esvaziar os fundos transferindo para outra conta e retirando do sistema.\n",
        "\n",
        "* **isFlaggedFraud** - O modelo de negócios visa controlar as transferências em massa de uma conta para outra e sinaliza tentativas ilegais. Uma tentativa ilegal neste conjunto de dados é uma tentativa de transferir mais de 200.000 em uma única transação.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ao9brJj5T136"
      },
      "source": [
        "# Passos preliminares"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78jz3gvIn6Tp"
      },
      "source": [
        "###Bibliotecas utilizadas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebUQXRyyTwDU"
      },
      "source": [
        "#biblitoecas utilizadas na etada AED\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings \n",
        "warnings.filterwarnings('ignore') #evita que warnings sejam mostrados\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z30w6iF1n-Jy"
      },
      "source": [
        "### Leitura dos dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WGBT3NPUBIh"
      },
      "source": [
        "#Leitura dos dados\n",
        "df = pd.read_csv('/content/drive/MyDrive/2020.2/Inteligência Artificial (IA)/Trabalhos/00. Árvores de Decisão/BaseDeDados/PS_20174392719_1491204439457_log.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ixNi1L46TtBT"
      },
      "source": [
        "# Análise exploratória dos dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvDJ7aaTUBu5"
      },
      "source": [
        "### Informações básicas sobre o DataFrame\n",
        "Dimensionalidade e descrição estatística"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "CDeeAfgXU2cc",
        "outputId": "a56e90ea-fd7d-449d-aa44-04a565eb027c"
      },
      "source": [
        "#Verificação de algumas linhas \n",
        "df.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>step</th>\n",
              "      <th>type</th>\n",
              "      <th>amount</th>\n",
              "      <th>nameOrig</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>nameDest</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>9839.64</td>\n",
              "      <td>C1231006815</td>\n",
              "      <td>170136.0</td>\n",
              "      <td>160296.36</td>\n",
              "      <td>M1979787155</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>C1666544295</td>\n",
              "      <td>21249.0</td>\n",
              "      <td>19384.72</td>\n",
              "      <td>M2044282225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>181.00</td>\n",
              "      <td>C1305486145</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>C553264065</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>181.00</td>\n",
              "      <td>C840083671</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>C38997010</td>\n",
              "      <td>21182.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>11668.14</td>\n",
              "      <td>C2048537720</td>\n",
              "      <td>41554.0</td>\n",
              "      <td>29885.86</td>\n",
              "      <td>M1230701703</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   step      type    amount  ... newbalanceDest  isFraud  isFlaggedFraud\n",
              "0     1   PAYMENT   9839.64  ...            0.0        0               0\n",
              "1     1   PAYMENT   1864.28  ...            0.0        0               0\n",
              "2     1  TRANSFER    181.00  ...            0.0        1               0\n",
              "3     1  CASH_OUT    181.00  ...            0.0        1               0\n",
              "4     1   PAYMENT  11668.14  ...            0.0        0               0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HY3AHP8SUTzR",
        "outputId": "3a239bbc-4145-4e67-db16-856f6677627a"
      },
      "source": [
        "#Verificação da dimensionalidade do DataFrame\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6362620, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3vRBEbwPUdqw",
        "outputId": "2e0b5559-4929-4d6a-f445-4366c55bb622"
      },
      "source": [
        "#Informações dos atributos\n",
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6362620 entries, 0 to 6362619\n",
            "Data columns (total 11 columns):\n",
            " #   Column          Dtype  \n",
            "---  ------          -----  \n",
            " 0   step            int64  \n",
            " 1   type            object \n",
            " 2   amount          float64\n",
            " 3   nameOrig        object \n",
            " 4   oldbalanceOrg   float64\n",
            " 5   newbalanceOrig  float64\n",
            " 6   nameDest        object \n",
            " 7   oldbalanceDest  float64\n",
            " 8   newbalanceDest  float64\n",
            " 9   isFraud         int64  \n",
            " 10  isFlaggedFraud  int64  \n",
            "dtypes: float64(5), int64(3), object(3)\n",
            "memory usage: 534.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "BZf6o92ohsN-",
        "outputId": "51bd41bd-fdbb-4388-9b2a-dbe2307db13a"
      },
      "source": [
        "#Descrição estatística de cada recurso\n",
        "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",
              "        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>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.433972e+02</td>\n",
              "      <td>1.798619e+05</td>\n",
              "      <td>8.338831e+05</td>\n",
              "      <td>8.551137e+05</td>\n",
              "      <td>1.100702e+06</td>\n",
              "      <td>1.224996e+06</td>\n",
              "      <td>1.290820e-03</td>\n",
              "      <td>2.514687e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.423320e+02</td>\n",
              "      <td>6.038582e+05</td>\n",
              "      <td>2.888243e+06</td>\n",
              "      <td>2.924049e+06</td>\n",
              "      <td>3.399180e+06</td>\n",
              "      <td>3.674129e+06</td>\n",
              "      <td>3.590480e-02</td>\n",
              "      <td>1.585775e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.560000e+02</td>\n",
              "      <td>1.338957e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.390000e+02</td>\n",
              "      <td>7.487194e+04</td>\n",
              "      <td>1.420800e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.327057e+05</td>\n",
              "      <td>2.146614e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.350000e+02</td>\n",
              "      <td>2.087215e+05</td>\n",
              "      <td>1.073152e+05</td>\n",
              "      <td>1.442584e+05</td>\n",
              "      <td>9.430367e+05</td>\n",
              "      <td>1.111909e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.430000e+02</td>\n",
              "      <td>9.244552e+07</td>\n",
              "      <td>5.958504e+07</td>\n",
              "      <td>4.958504e+07</td>\n",
              "      <td>3.560159e+08</td>\n",
              "      <td>3.561793e+08</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               step        amount  ...       isFraud  isFlaggedFraud\n",
              "count  6.362620e+06  6.362620e+06  ...  6.362620e+06    6.362620e+06\n",
              "mean   2.433972e+02  1.798619e+05  ...  1.290820e-03    2.514687e-06\n",
              "std    1.423320e+02  6.038582e+05  ...  3.590480e-02    1.585775e-03\n",
              "min    1.000000e+00  0.000000e+00  ...  0.000000e+00    0.000000e+00\n",
              "25%    1.560000e+02  1.338957e+04  ...  0.000000e+00    0.000000e+00\n",
              "50%    2.390000e+02  7.487194e+04  ...  0.000000e+00    0.000000e+00\n",
              "75%    3.350000e+02  2.087215e+05  ...  0.000000e+00    0.000000e+00\n",
              "max    7.430000e+02  9.244552e+07  ...  1.000000e+00    1.000000e+00\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-fmI9qwdUgw-"
      },
      "source": [
        "Há tipos *int, float e object.*\n",
        "\n",
        "Possui 6.362.620 linhas (transações) e 11 colunas (atributos)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ueNal_8vVLWI"
      },
      "source": [
        "### Valores faltantes e linhas duplicadas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KGGxjtjIVPSr",
        "outputId": "da753376-6b4c-4479-82a0-ab2294abb064"
      },
      "source": [
        "#Verificação de valores missing\n",
        "for coluna in df.columns:\n",
        "    print(\"Qtd. de valores faltantes em {}: {}\".format(coluna, df[coluna].isna().sum()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Qtd. de valores faltantes em step: 0\n",
            "Qtd. de valores faltantes em type: 0\n",
            "Qtd. de valores faltantes em amount: 0\n",
            "Qtd. de valores faltantes em nameOrig: 0\n",
            "Qtd. de valores faltantes em oldbalanceOrg: 0\n",
            "Qtd. de valores faltantes em newbalanceOrig: 0\n",
            "Qtd. de valores faltantes em nameDest: 0\n",
            "Qtd. de valores faltantes em oldbalanceDest: 0\n",
            "Qtd. de valores faltantes em newbalanceDest: 0\n",
            "Qtd. de valores faltantes em isFraud: 0\n",
            "Qtd. de valores faltantes em isFlaggedFraud: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3EhUrYRVSAx"
      },
      "source": [
        "#Eliminação de valores missing (caso seja necessário)\n",
        "df.dropna(inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "qTpYhxR9WPP7",
        "outputId": "8aed4164-cda0-453a-813f-7fd0608e3e65"
      },
      "source": [
        "#Eliminação de duplicates\n",
        "df[df.duplicated()]"
      ],
      "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>step</th>\n",
              "      <th>type</th>\n",
              "      <th>amount</th>\n",
              "      <th>nameOrig</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>nameDest</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [step, type, amount, nameOrig, oldbalanceOrg, newbalanceOrig, nameDest, oldbalanceDest, newbalanceDest, isFraud, isFlaggedFraud]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ctVuLcT4O3p3"
      },
      "source": [
        "#Remoção de duplicates, se necessário\n",
        "df.drop_duplicates(keep=False, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ECcPmmv6PADG",
        "outputId": "c2696a97-8a3a-4fe8-a91d-d4d7ecbb87c3"
      },
      "source": [
        "df.shape()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6362620, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WO5Ou20fVc8c"
      },
      "source": [
        "Como base nos resultados, é possível afirmar que a base de dados não possui valores faltantes ou linhas duplicadas. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPkpKTNoXlNJ"
      },
      "source": [
        "### Análise dos atributos\n",
        "\n",
        "O processo de análise foi guiado por 4 principais questões. São elas:\n",
        "* O conjunto de dados é balanceado?\n",
        "* Qual é a taxa de fraude por hora do dia?\n",
        "* Em quais operações ocorrem fraudes?\n",
        "* Qual é a correlação entre cada atributo?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KhINV2lZXrvw"
      },
      "source": [
        "#### isFraud e isFlaggedFraud\n",
        "**O conjunto de dados é balanceado?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEWLmTCFi9b2"
      },
      "source": [
        "Após a análise do contexto do problema e da origem do conjunto de dados, vamos considerar que:\n",
        "* isFraud indica as transações reais de fraude;\n",
        "* isFlaggedFraud indica transações que foram sinalizadas pelo fato de utrapassarem certo limite estabelecido (tentativa de transferir mais de 200.000 em uma única transação)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dGCmuNIXj-F",
        "outputId": "d0cbfc85-8907-40fd-e072-bc52662d27d6"
      },
      "source": [
        "#Compração da quantidades de fraudes (ou possível tentativa de fraude) com a quantidade total de transações\n",
        "print('isFraud:')\n",
        "print(df.isFraud.value_counts())\n",
        "\n",
        "print('\\n\\nisFlaggedFraud:')\n",
        "print(df.isFlaggedFraud.value_counts())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "isFraud:\n",
            "0    6354407\n",
            "1       8213\n",
            "Name: isFraud, dtype: int64\n",
            "\n",
            "\n",
            "isFlaggedFraud:\n",
            "0    6362604\n",
            "1         16\n",
            "Name: isFlaggedFraud, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C11pLHWTQRV0"
      },
      "source": [
        "\n",
        "Vamos verificar essas questões nos gráficos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "MRmquBJizk5P",
        "outputId": "6b9bba8a-7ff8-40f5-d031-f7d3a3704841"
      },
      "source": [
        "#Análise visual da váriavel 'isFraud'\n",
        "sns.countplot(df['isFraud'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f93ff104c90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "WMFyM-TGbJQu",
        "outputId": "c0859f6a-3e84-43ae-bdaa-2cb9b4174ab0"
      },
      "source": [
        "#Análise visual da váriavel 'isFlaggedFraud'\n",
        "sns.countplot(df['isFlaggedFraud'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f93f33bdb10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MGXhNs+cBNyW5gVF0zxvzeecDZw8/0Hwy8JNh+6WMfsPVZuAzjI5j/6iq/g/4Q+CKJBuAe4AfDc95H6NDPjcn2TTcBvg4cGySLcB7gQ3jflFVdTfwCWAjo/9Arltw9weAtwxf86oF2w96PS1fXk1Sy0qSY6vq3iS/DFwLPKeqbl+wPYx+afl/V9VfT3Za6dB4DF7LzeXD2TBHAe+rqtuH7W9Kcvaw/QZGZ9VID2juwUtSpzwGL0mdMvCS1CkDL0mdMvCS1CkDL0md+n+3mpPy0jDtiwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LEcycF27elHB",
        "outputId": "c57b2312-1b95-4434-aff9-c698a82b5b3f"
      },
      "source": [
        "#Cálculo da representatividade das transações fraudulentas\n",
        "porcentagem_fraudes = (df.isFraud.value_counts()[1]*100/df.isFraud.value_counts()[0])\n",
        "porcentagem_fraudes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.129248881917699"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gt73fwt-YXMn"
      },
      "source": [
        "O número total de transações é 6.362.620, das quais 8.213 são fraudes e 6.354.407 não são transações fraudulentas. Isso significa, como cálculado anteriormente, que aproximadamente 0,13% são transações fraudulentas.  \n",
        "\n",
        "Além disso, o indicador *isFlaggedFraud* sinalizou apenas 16 transações do total de 6.362.620. \n",
        "\n",
        "Esse resutlados mostram que este é um conjunto de dados desequilibrado.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TcZ7n-_HwIQZ"
      },
      "source": [
        "#### Step vs. Taxa de fraude\n",
        "**Qual a taxa de fraude por hora do dia?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tk24OmcvwQ5Z"
      },
      "source": [
        "**Step** mapeia uma unidade de tempo no mundo real. Assim, 1 *step* corresponde a 1 hora de tempo. Total de etapas 744 (simulação de 30 dias). Nesse sentido, podemos tentar encontrar um padrão no que diz respeito à tempestividade das tentativas de fraude."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZEjkgM9xutL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "6426727d-2747-4c65-e5d0-d55634bd860a"
      },
      "source": [
        "#transforma step (1 a 744) em horas do dia (0 a 24)\n",
        "tmp = pd.DataFrame()\n",
        "tmp = df.copy()\n",
        "tmp['hora_do_dia'] = tmp.step%24\n",
        "\n",
        "#calcula a quantidade de transacoes por hora\n",
        "contagem_transacoes_por_hora = tmp.groupby('hora_do_dia')['isFraud'].count()\n",
        "#calcula a qtd de fraudes por hora\n",
        "soma_fraudes_por_hora = tmp.groupby('hora_do_dia')['isFraud'].sum()\n",
        "\n",
        "fraudes_por_hora = pd.concat([soma_fraudes_por_hora, contagem_transacoes_por_hora],axis=1)\n",
        "fraudes_por_hora.columns = ['fraudes','transacoes']\n",
        "fraudes_por_hora['taxa_de_fraude'] = fraudes_por_hora.fraudes/fraudes_por_hora.transacoes\n",
        "\n",
        "# Create bars\n",
        "bars = fraudes_por_hora.index\n",
        "x_pos = np.arange(len(bars))\n",
        "height = fraudes_por_hora.taxa_de_fraude\n",
        "\n",
        "# Create names on the x-axis\n",
        "plt.bar(x_pos, height, color=(0.2, 0.4, 0.6, 1))\n",
        "plt.xticks(x_pos, bars)\n",
        "# tmp.drop(columns=['hora_do_dia'],inplace=True)\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "oHKwD2gg5wS7"
      },
      "source": [
        "O gráfico anterior mostra que as maiores taxas de fraude estão entre 3 horas da manhã e 5 horas da manhã. \n",
        "\n",
        "**Este resultado significa que há uma quantidade maior de fraudes nesse período do dia? Vamos verificar!**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffKO4oy1WS-Y",
        "outputId": "ad6bb9fd-fe44-4ef6-c908-7c2260953946"
      },
      "source": [
        "#quantidade de transações totais entre 3am e 5am\n",
        "contagem_transacoes_por_hora[3:6]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hora_do_dia\n",
              "3    2007\n",
              "4    1241\n",
              "5    1641\n",
              "Name: isFraud, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_wG_kd2WayS",
        "outputId": "5811e56c-d78c-445f-f545-a064eb2a8960"
      },
      "source": [
        "#quantidade de transações entre 13h e 15h \n",
        "contagem_transacoes_por_hora[13:15]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hora_do_dia\n",
              "13    468474\n",
              "14    439653\n",
              "Name: isFraud, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "U-iFYKMDZsi3",
        "outputId": "48e84104-16ad-45aa-c280-5e32081ffb32"
      },
      "source": [
        "#Plotagem para verificar se existem uma tendência entre transações vs. hora do dia \n",
        "figsize=(10,6)\n",
        "plt.subplots(figsize=figsize)\n",
        "plt.plot(range(len(fraudes_por_hora)), \n",
        "         fraudes_por_hora.transacoes,\n",
        "         'r.'\n",
        "         )\n",
        "plt.xlabel('Hora do dia')\n",
        "plt.ylabel('Transações')\n",
        "plt.title('Transações X hora do dia')\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "NzYnjcq1YFxu",
        "outputId": "9a41d5ad-c16f-47de-c505-b9bb59864ca0"
      },
      "source": [
        "#Plotagem para verificar se existem uma tendência entre hora do dia vs. qtd. de fraudes\n",
        "figsize=(10,6)\n",
        "plt.subplots(figsize=figsize)\n",
        "plt.plot(range(len(fraudes_por_hora)), \n",
        "         fraudes_por_hora.fraudes,\n",
        "         'r.'\n",
        "         )\n",
        "plt.xlabel('Hora do dia')\n",
        "plt.ylabel('Fraudes')\n",
        "plt.title('Fraudes X Hora do dia')\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KNL6uEo_XMYk",
        "outputId": "f475aca1-357d-4518-8765-3ceed834e31a"
      },
      "source": [
        "#Média da quantidade de fraudes realizadas \n",
        "print(round(fraudes_por_hora['fraudes'].mean(), 0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "342.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JXnoSBx-W9xU"
      },
      "source": [
        "Como podemos observar, a quantidade de fraudes é, em média, 342. Este resultado, em consonância com os gráficos anteriores, mostra que as maiores taxas de fraude estão entre 3 horas da manhã e 5 horas da manhã porque a quantidade de transações nesse período é significativamente menor."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pep-Ba6Ad5SV"
      },
      "source": [
        "#### Tipos de transações\n",
        "**Em quais operações ocorrem fraudes?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rBQkTmtcd9Ur"
      },
      "source": [
        "Como ilustrado abaixo, existem 5 tipos de transações."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hsr7rgoreD_j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "ec8698c6-f857-498c-fd5e-1950fbf4bfc1"
      },
      "source": [
        "# Verificação das características das categorias 'type'\n",
        "df.type.value_counts()\n",
        "sns.countplot(df.type)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f20c1bb4950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "VhvRiy-GiOc7"
      },
      "source": [
        "Em quais tipos de transação acontecem fraudes? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-7ZHpCadeCAD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "2009286b-0b02-41ea-ab06-96bb2b34bb79"
      },
      "source": [
        "#Groupby type\n",
        "df.groupby('type')['isFraud','isFlaggedFraud'].sum()"
      ],
      "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>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CASH_IN</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CASH_OUT</th>\n",
              "      <td>4116</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEBIT</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAYMENT</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TRANSFER</th>\n",
              "      <td>4097</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          isFraud  isFlaggedFraud\n",
              "type                             \n",
              "CASH_IN         0               0\n",
              "CASH_OUT     4116               0\n",
              "DEBIT           0               0\n",
              "PAYMENT         0               0\n",
              "TRANSFER     4097              16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "04OPgQwXc97H"
      },
      "source": [
        "Vamos verificar visual o resultado da tabela anterior."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-utffZMAiGHp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "25368bd2-dfb4-4a79-b761-c132664618a7"
      },
      "source": [
        "sns.barplot(x=df.type.unique(),y=df.groupby(\"type\")[\"isFraud\"].sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f20c18c2690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "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": "A8wy-3jBiJDI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "f059f578-d0c4-463b-b9a4-0d00e95a5796"
      },
      "source": [
        "sns.barplot(x=df.type.unique(),y=df.groupby(\"type\")[\"isFlaggedFraud\"].sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f20c178ac50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "K-UEbN86f4jK"
      },
      "source": [
        "Dos cincos possíveis tipos de transações, fraudes ocorrem apenas em 2 tipos de transações: ***transfer*** e ***cash_out*** (em *isFraud*). \n",
        "\n",
        "O indicador *isFlaggedFraud* sinalizou fraudes em apenas um tipo de operação, a saber, ***transfer***."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lQJUyVyGkvAe"
      },
      "source": [
        "### Análise de correlação\n",
        "**Qual é a correlação entre cada atributo?** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEprlM8CkyyW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0315f1a2-9b49-42f6-e67b-60486efa880d"
      },
      "source": [
        "#Explorando as correlações com isFraud\n",
        "df.corr()['isFraud'].sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "isFraud           1.000000\n",
              "amount            0.076688\n",
              "isFlaggedFraud    0.044109\n",
              "step              0.031578\n",
              "oldbalanceOrg     0.010154\n",
              "newbalanceDest    0.000535\n",
              "oldbalanceDest   -0.005885\n",
              "newbalanceOrig   -0.008148\n",
              "Name: isFraud, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vX_upBaRk0_T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24739dff-5710-4995-f02c-86b917927238"
      },
      "source": [
        "#Explorando as correlações com isFlaggedFraud\n",
        "df.corr()['isFlaggedFraud'].sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "isFlaggedFraud    1.000000\n",
              "isFraud           0.044109\n",
              "amount            0.012295\n",
              "oldbalanceOrg     0.003835\n",
              "newbalanceOrig    0.003776\n",
              "step              0.003277\n",
              "oldbalanceDest   -0.000513\n",
              "newbalanceDest   -0.000529\n",
              "Name: isFlaggedFraud, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yMkSgtEhYmDn"
      },
      "source": [
        "# df.corr(method='spearman')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDmwHw1Gk3Ug",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "outputId": "f82f7399-4322-4ff8-a4cc-24dff50293d8"
      },
      "source": [
        "#Mapa de calor das features\n",
        "f, ax = plt.subplots(figsize=(12,6))\n",
        "sns.heatmap(df.corr(), annot=True, fmt='.2f')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f93f30e1a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uD66S1Wd88U7"
      },
      "source": [
        "Quanto maior o valor da correlação entre cada variável, o gráfico fica  mais claro."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bxesXzrb8_DX"
      },
      "source": [
        "# Problemas encontrados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xj4rgJjbLe6L"
      },
      "source": [
        "#### isFlaggedFraud não é acionada"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWO9kwEE8-nt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3443245-b605-4417-e386-637d7bdbfc26"
      },
      "source": [
        "#Verificação de: \"O modelo de negócios visa controlar as transferências em massa de uma conta para outra e sinaliza tentativas ilegais. \n",
        "                  #Uma tentativa ilegal neste conjunto de dados é uma tentativa de transferir mais de 200.000 em uma única transação.\"\n",
        "                  \n",
        "df_filtered = df[(df.type == 'TRANSFER') & (df.amount >= 200000)]\n",
        "len(df_filtered)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "409110"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5E_fBnpRLiqS"
      },
      "source": [
        "Por definição, quando há uma tentativa de transferir (operação *transfer*) um valor igual ou superior a 200 mil em apenas uma transação, *isFlaggedFraud* é acionada. Entretanto, como mostar o resultado anterior, isso não acontece em um total de 409.110 transações.\n",
        "\n",
        "\n",
        "Caso seja esse um dos atributos selecionados para treinar o modelo, uma possível solução seria alterar *isFlaggedFraud* de acordo com o limite estabelecido. Porém, pode não ser uma prática correta. O ideal seria verificar as razões pelas quais a origem dos dados- nesse caso, o simulador, não acionou a *flag*."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwqZ44hbTcbz"
      },
      "source": [
        "#### Conjunto de dados desbalanceado"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7iWNnpexTqar"
      },
      "source": [
        "Como visto na etapa *Análise dos atributos*, do número total de transações (6.362.620) apenas 0,13% são transações frauldulentas.\n",
        "\n",
        "Uma solução será apresentada na etapa de pré-processamento."
      ]
    }
  ]
}