AUTO_ENCODER_CARTAO_DE_CREDITO.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yurikomuta/autoenconder-detection-fraud/blob/main/AUTO_ENCODER_CARTAO_DE_CREDITO.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Auto-encoder Cartão de Crédito\n"
      ],
      "metadata": {
        "id": "iBv_4tMlhJxF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "pT6BaJNgmZ6c"
      },
      "outputs": [],
      "source": [
        "# Bibliotecas utilizadas\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "from keras import layers\n",
        "from keras.layers import Input, Dense\n",
        "from keras.models import Model, Sequential \n",
        "from keras import regularizers\n",
        "\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "EhRN1qWwmhlD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c59d977-7e7f-4314-9b08-4da932e585e9"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cm-b1HrvmZ6k"
      },
      "source": [
        "## Carregar Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "7S5AMH6hmZ6m"
      },
      "outputs": [],
      "source": [
        "df_credit_card = pd.read_csv('/content/drive/MyDrive/Autoencoder - INF317E/creditcard.csv', header=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "5jJLLzYcmZ6o",
        "outputId": "8ca347eb-adf6-4c5a-f1bd-44c48c2decd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284807 entries, 0 to 284806\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   Time    284807 non-null  float64\n",
            " 1   V1      284807 non-null  float64\n",
            " 2   V2      284807 non-null  float64\n",
            " 3   V3      284807 non-null  float64\n",
            " 4   V4      284807 non-null  float64\n",
            " 5   V5      284807 non-null  float64\n",
            " 6   V6      284807 non-null  float64\n",
            " 7   V7      284807 non-null  float64\n",
            " 8   V8      284807 non-null  float64\n",
            " 9   V9      284807 non-null  float64\n",
            " 10  V10     284807 non-null  float64\n",
            " 11  V11     284807 non-null  float64\n",
            " 12  V12     284807 non-null  float64\n",
            " 13  V13     284807 non-null  float64\n",
            " 14  V14     284807 non-null  float64\n",
            " 15  V15     284807 non-null  float64\n",
            " 16  V16     284807 non-null  float64\n",
            " 17  V17     284807 non-null  float64\n",
            " 18  V18     284807 non-null  float64\n",
            " 19  V19     284807 non-null  float64\n",
            " 20  V20     284807 non-null  float64\n",
            " 21  V21     284807 non-null  float64\n",
            " 22  V22     284807 non-null  float64\n",
            " 23  V23     284807 non-null  float64\n",
            " 24  V24     284807 non-null  float64\n",
            " 25  V25     284807 non-null  float64\n",
            " 26  V26     284807 non-null  float64\n",
            " 27  V27     284807 non-null  float64\n",
            " 28  V28     284807 non-null  float64\n",
            " 29  Amount  284807 non-null  float64\n",
            " 30  Class   284807 non-null  int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 67.4 MB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Time        V1        V2        V3        V4        V5        V6  \\\n",
              "0    0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
              "1    0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
              "2    1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
              "3    1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
              "4    2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
              "..   ...       ...       ...       ...       ...       ...       ...   \n",
              "95  64.0 -0.658305  0.406791  2.037461 -0.291298  0.147910 -0.350857   \n",
              "96  64.0  0.959602  0.370711  0.888613  2.343244  0.352491  1.365515   \n",
              "97  67.0 -0.653445  0.160225  1.592256  1.296832  0.997175 -0.343000   \n",
              "98  67.0 -1.494668  0.837241  2.628211  3.145414 -0.609098  0.258495   \n",
              "99  68.0  1.232996  0.189454  0.491040  0.633673 -0.511574 -0.990609   \n",
              "\n",
              "          V7        V8        V9  ...       V21       V22       V23       V24  \\\n",
              "0   0.239599  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928   \n",
              "1  -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846   \n",
              "2   0.791461  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281   \n",
              "3   0.237609  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575   \n",
              "4   0.592941 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267   \n",
              "..       ...       ...       ...  ...       ...       ...       ...       ...   \n",
              "95  0.945373 -0.172560  0.025133  ... -0.156096 -0.238805  0.089877  0.421195   \n",
              "96 -0.277771  0.516053 -0.700929  ... -0.155547 -0.403239  0.356504 -0.696881   \n",
              "97  0.469937 -0.132470 -0.197794  ...  0.038363  0.336449 -0.014883  0.102959   \n",
              "98 -0.012189  0.102136 -0.286164  ... -0.140047  0.355044  0.332720  0.718193   \n",
              "99  0.066240 -0.196940  0.075921  ... -0.251566 -0.770139  0.125998  0.369627   \n",
              "\n",
              "         V25       V26       V27       V28  Amount  Class  \n",
              "0   0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
              "1   0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
              "2  -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
              "3   0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
              "4  -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
              "..       ...       ...       ...       ...     ...    ...  \n",
              "95 -0.352487  0.074783 -0.094192 -0.092493   54.99      0  \n",
              "96 -0.198718 -0.220268  0.068546  0.020797    7.55      0  \n",
              "97 -0.265322 -0.348637  0.011238 -0.049478   19.85      0  \n",
              "98 -0.219366  0.118927 -0.317486 -0.340783   28.28      0  \n",
              "99  0.205598  0.094062 -0.033138  0.020990   15.99      0  \n",
              "\n",
              "[100 rows x 31 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>...</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>64.0</td>\n",
              "      <td>-0.658305</td>\n",
              "      <td>0.406791</td>\n",
              "      <td>2.037461</td>\n",
              "      <td>-0.291298</td>\n",
              "      <td>0.147910</td>\n",
              "      <td>-0.350857</td>\n",
              "      <td>0.945373</td>\n",
              "      <td>-0.172560</td>\n",
              "      <td>0.025133</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.156096</td>\n",
              "      <td>-0.238805</td>\n",
              "      <td>0.089877</td>\n",
              "      <td>0.421195</td>\n",
              "      <td>-0.352487</td>\n",
              "      <td>0.074783</td>\n",
              "      <td>-0.094192</td>\n",
              "      <td>-0.092493</td>\n",
              "      <td>54.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>64.0</td>\n",
              "      <td>0.959602</td>\n",
              "      <td>0.370711</td>\n",
              "      <td>0.888613</td>\n",
              "      <td>2.343244</td>\n",
              "      <td>0.352491</td>\n",
              "      <td>1.365515</td>\n",
              "      <td>-0.277771</td>\n",
              "      <td>0.516053</td>\n",
              "      <td>-0.700929</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.155547</td>\n",
              "      <td>-0.403239</td>\n",
              "      <td>0.356504</td>\n",
              "      <td>-0.696881</td>\n",
              "      <td>-0.198718</td>\n",
              "      <td>-0.220268</td>\n",
              "      <td>0.068546</td>\n",
              "      <td>0.020797</td>\n",
              "      <td>7.55</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>67.0</td>\n",
              "      <td>-0.653445</td>\n",
              "      <td>0.160225</td>\n",
              "      <td>1.592256</td>\n",
              "      <td>1.296832</td>\n",
              "      <td>0.997175</td>\n",
              "      <td>-0.343000</td>\n",
              "      <td>0.469937</td>\n",
              "      <td>-0.132470</td>\n",
              "      <td>-0.197794</td>\n",
              "      <td>...</td>\n",
              "      <td>0.038363</td>\n",
              "      <td>0.336449</td>\n",
              "      <td>-0.014883</td>\n",
              "      <td>0.102959</td>\n",
              "      <td>-0.265322</td>\n",
              "      <td>-0.348637</td>\n",
              "      <td>0.011238</td>\n",
              "      <td>-0.049478</td>\n",
              "      <td>19.85</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>67.0</td>\n",
              "      <td>-1.494668</td>\n",
              "      <td>0.837241</td>\n",
              "      <td>2.628211</td>\n",
              "      <td>3.145414</td>\n",
              "      <td>-0.609098</td>\n",
              "      <td>0.258495</td>\n",
              "      <td>-0.012189</td>\n",
              "      <td>0.102136</td>\n",
              "      <td>-0.286164</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.140047</td>\n",
              "      <td>0.355044</td>\n",
              "      <td>0.332720</td>\n",
              "      <td>0.718193</td>\n",
              "      <td>-0.219366</td>\n",
              "      <td>0.118927</td>\n",
              "      <td>-0.317486</td>\n",
              "      <td>-0.340783</td>\n",
              "      <td>28.28</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>68.0</td>\n",
              "      <td>1.232996</td>\n",
              "      <td>0.189454</td>\n",
              "      <td>0.491040</td>\n",
              "      <td>0.633673</td>\n",
              "      <td>-0.511574</td>\n",
              "      <td>-0.990609</td>\n",
              "      <td>0.066240</td>\n",
              "      <td>-0.196940</td>\n",
              "      <td>0.075921</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.251566</td>\n",
              "      <td>-0.770139</td>\n",
              "      <td>0.125998</td>\n",
              "      <td>0.369627</td>\n",
              "      <td>0.205598</td>\n",
              "      <td>0.094062</td>\n",
              "      <td>-0.033138</td>\n",
              "      <td>0.020990</td>\n",
              "      <td>15.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 31 columns</p>\n",
              "</div>\n",
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              "    .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-29432139-7437-49ff-9cfa-994866d71e31');\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df_credit_card.info()\n",
        "df_credit_card.head(100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXzuGOi1mZ6q"
      },
      "source": [
        "## Análise Exploratória de Dados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "N-YyDAkBmZ6r",
        "outputId": "a7133970-c484-4c32-aa31-201b38126fb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           count          mean           std         min           25%  \\\n",
              "Time    284807.0  9.481386e+04  47488.145955    0.000000  54201.500000   \n",
              "V1      284807.0  1.168375e-15      1.958696  -56.407510     -0.920373   \n",
              "V2      284807.0  3.416908e-16      1.651309  -72.715728     -0.598550   \n",
              "V3      284807.0 -1.379537e-15      1.516255  -48.325589     -0.890365   \n",
              "V4      284807.0  2.074095e-15      1.415869   -5.683171     -0.848640   \n",
              "V5      284807.0  9.604066e-16      1.380247 -113.743307     -0.691597   \n",
              "V6      284807.0  1.487313e-15      1.332271  -26.160506     -0.768296   \n",
              "V7      284807.0 -5.556467e-16      1.237094  -43.557242     -0.554076   \n",
              "V8      284807.0  1.213481e-16      1.194353  -73.216718     -0.208630   \n",
              "V9      284807.0 -2.406331e-15      1.098632  -13.434066     -0.643098   \n",
              "V10     284807.0  2.239053e-15      1.088850  -24.588262     -0.535426   \n",
              "V11     284807.0  1.673327e-15      1.020713   -4.797473     -0.762494   \n",
              "V12     284807.0 -1.247012e-15      0.999201  -18.683715     -0.405571   \n",
              "V13     284807.0  8.190001e-16      0.995274   -5.791881     -0.648539   \n",
              "V14     284807.0  1.207294e-15      0.958596  -19.214325     -0.425574   \n",
              "V15     284807.0  4.887456e-15      0.915316   -4.498945     -0.582884   \n",
              "V16     284807.0  1.437716e-15      0.876253  -14.129855     -0.468037   \n",
              "V17     284807.0 -3.772171e-16      0.849337  -25.162799     -0.483748   \n",
              "V18     284807.0  9.564149e-16      0.838176   -9.498746     -0.498850   \n",
              "V19     284807.0  1.039917e-15      0.814041   -7.213527     -0.456299   \n",
              "V20     284807.0  6.406204e-16      0.770925  -54.497720     -0.211721   \n",
              "V21     284807.0  1.654067e-16      0.734524  -34.830382     -0.228395   \n",
              "V22     284807.0 -3.568593e-16      0.725702  -10.933144     -0.542350   \n",
              "V23     284807.0  2.578648e-16      0.624460  -44.807735     -0.161846   \n",
              "V24     284807.0  4.473266e-15      0.605647   -2.836627     -0.354586   \n",
              "V25     284807.0  5.340915e-16      0.521278  -10.295397     -0.317145   \n",
              "V26     284807.0  1.683437e-15      0.482227   -2.604551     -0.326984   \n",
              "V27     284807.0 -3.660091e-16      0.403632  -22.565679     -0.070840   \n",
              "V28     284807.0 -1.227390e-16      0.330083  -15.430084     -0.052960   \n",
              "Amount  284807.0  8.834962e+01    250.120109    0.000000      5.600000   \n",
              "Class   284807.0  1.727486e-03      0.041527    0.000000      0.000000   \n",
              "\n",
              "                 50%            75%            max  \n",
              "Time    84692.000000  139320.500000  172792.000000  \n",
              "V1          0.018109       1.315642       2.454930  \n",
              "V2          0.065486       0.803724      22.057729  \n",
              "V3          0.179846       1.027196       9.382558  \n",
              "V4         -0.019847       0.743341      16.875344  \n",
              "V5         -0.054336       0.611926      34.801666  \n",
              "V6         -0.274187       0.398565      73.301626  \n",
              "V7          0.040103       0.570436     120.589494  \n",
              "V8          0.022358       0.327346      20.007208  \n",
              "V9         -0.051429       0.597139      15.594995  \n",
              "V10        -0.092917       0.453923      23.745136  \n",
              "V11        -0.032757       0.739593      12.018913  \n",
              "V12         0.140033       0.618238       7.848392  \n",
              "V13        -0.013568       0.662505       7.126883  \n",
              "V14         0.050601       0.493150      10.526766  \n",
              "V15         0.048072       0.648821       8.877742  \n",
              "V16         0.066413       0.523296      17.315112  \n",
              "V17        -0.065676       0.399675       9.253526  \n",
              "V18        -0.003636       0.500807       5.041069  \n",
              "V19         0.003735       0.458949       5.591971  \n",
              "V20        -0.062481       0.133041      39.420904  \n",
              "V21        -0.029450       0.186377      27.202839  \n",
              "V22         0.006782       0.528554      10.503090  \n",
              "V23        -0.011193       0.147642      22.528412  \n",
              "V24         0.040976       0.439527       4.584549  \n",
              "V25         0.016594       0.350716       7.519589  \n",
              "V26        -0.052139       0.240952       3.517346  \n",
              "V27         0.001342       0.091045      31.612198  \n",
              "V28         0.011244       0.078280      33.847808  \n",
              "Amount     22.000000      77.165000   25691.160000  \n",
              "Class       0.000000       0.000000       1.000000  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Time</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>9.481386e+04</td>\n",
              "      <td>47488.145955</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>54201.500000</td>\n",
              "      <td>84692.000000</td>\n",
              "      <td>139320.500000</td>\n",
              "      <td>172792.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V1</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>1.168375e-15</td>\n",
              "      <td>1.958696</td>\n",
              "      <td>-56.407510</td>\n",
              "      <td>-0.920373</td>\n",
              "      <td>0.018109</td>\n",
              "      <td>1.315642</td>\n",
              "      <td>2.454930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V2</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>3.416908e-16</td>\n",
              "      <td>1.651309</td>\n",
              "      <td>-72.715728</td>\n",
              "      <td>-0.598550</td>\n",
              "      <td>0.065486</td>\n",
              "      <td>0.803724</td>\n",
              "      <td>22.057729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V3</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>-1.379537e-15</td>\n",
              "      <td>1.516255</td>\n",
              "      <td>-48.325589</td>\n",
              "      <td>-0.890365</td>\n",
              "      <td>0.179846</td>\n",
              "      <td>1.027196</td>\n",
              "      <td>9.382558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V4</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>2.074095e-15</td>\n",
              "      <td>1.415869</td>\n",
              "      <td>-5.683171</td>\n",
              "      <td>-0.848640</td>\n",
              "      <td>-0.019847</td>\n",
              "      <td>0.743341</td>\n",
              "      <td>16.875344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V5</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>9.604066e-16</td>\n",
              "      <td>1.380247</td>\n",
              "      <td>-113.743307</td>\n",
              "      <td>-0.691597</td>\n",
              "      <td>-0.054336</td>\n",
              "      <td>0.611926</td>\n",
              "      <td>34.801666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V6</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>1.487313e-15</td>\n",
              "      <td>1.332271</td>\n",
              "      <td>-26.160506</td>\n",
              "      <td>-0.768296</td>\n",
              "      <td>-0.274187</td>\n",
              "      <td>0.398565</td>\n",
              "      <td>73.301626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V7</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>-5.556467e-16</td>\n",
              "      <td>1.237094</td>\n",
              "      <td>-43.557242</td>\n",
              "      <td>-0.554076</td>\n",
              "      <td>0.040103</td>\n",
              "      <td>0.570436</td>\n",
              "      <td>120.589494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V8</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>1.213481e-16</td>\n",
              "      <td>1.194353</td>\n",
              "      <td>-73.216718</td>\n",
              "      <td>-0.208630</td>\n",
              "      <td>0.022358</td>\n",
              "      <td>0.327346</td>\n",
              "      <td>20.007208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V9</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>-2.406331e-15</td>\n",
              "      <td>1.098632</td>\n",
              "      <td>-13.434066</td>\n",
              "      <td>-0.643098</td>\n",
              "      <td>-0.051429</td>\n",
              "      <td>0.597139</td>\n",
              "      <td>15.594995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V10</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>2.239053e-15</td>\n",
              "      <td>1.088850</td>\n",
              "      <td>-24.588262</td>\n",
              "      <td>-0.535426</td>\n",
              "      <td>-0.092917</td>\n",
              "      <td>0.453923</td>\n",
              "      <td>23.745136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V11</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>1.673327e-15</td>\n",
              "      <td>1.020713</td>\n",
              "      <td>-4.797473</td>\n",
              "      <td>-0.762494</td>\n",
              "      <td>-0.032757</td>\n",
              "      <td>0.739593</td>\n",
              "      <td>12.018913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V12</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>-1.247012e-15</td>\n",
              "      <td>0.999201</td>\n",
              "      <td>-18.683715</td>\n",
              "      <td>-0.405571</td>\n",
              "      <td>0.140033</td>\n",
              "      <td>0.618238</td>\n",
              "      <td>7.848392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V13</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>8.190001e-16</td>\n",
              "      <td>0.995274</td>\n",
              "      <td>-5.791881</td>\n",
              "      <td>-0.648539</td>\n",
              "      <td>-0.013568</td>\n",
              "      <td>0.662505</td>\n",
              "      <td>7.126883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V14</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>1.207294e-15</td>\n",
              "      <td>0.958596</td>\n",
              "      <td>-19.214325</td>\n",
              "      <td>-0.425574</td>\n",
              "      <td>0.050601</td>\n",
              "      <td>0.493150</td>\n",
              "      <td>10.526766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V15</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>4.887456e-15</td>\n",
              "      <td>0.915316</td>\n",
              "      <td>-4.498945</td>\n",
              "      <td>-0.582884</td>\n",
              "      <td>0.048072</td>\n",
              "      <td>0.648821</td>\n",
              "      <td>8.877742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V16</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>1.437716e-15</td>\n",
              "      <td>0.876253</td>\n",
              "      <td>-14.129855</td>\n",
              "      <td>-0.468037</td>\n",
              "      <td>0.066413</td>\n",
              "      <td>0.523296</td>\n",
              "      <td>17.315112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V17</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>-3.772171e-16</td>\n",
              "      <td>0.849337</td>\n",
              "      <td>-25.162799</td>\n",
              "      <td>-0.483748</td>\n",
              "      <td>-0.065676</td>\n",
              "      <td>0.399675</td>\n",
              "      <td>9.253526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V18</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>9.564149e-16</td>\n",
              "      <td>0.838176</td>\n",
              "      <td>-9.498746</td>\n",
              "      <td>-0.498850</td>\n",
              "      <td>-0.003636</td>\n",
              "      <td>0.500807</td>\n",
              "      <td>5.041069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V19</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>1.039917e-15</td>\n",
              "      <td>0.814041</td>\n",
              "      <td>-7.213527</td>\n",
              "      <td>-0.456299</td>\n",
              "      <td>0.003735</td>\n",
              "      <td>0.458949</td>\n",
              "      <td>5.591971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V20</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>6.406204e-16</td>\n",
              "      <td>0.770925</td>\n",
              "      <td>-54.497720</td>\n",
              "      <td>-0.211721</td>\n",
              "      <td>-0.062481</td>\n",
              "      <td>0.133041</td>\n",
              "      <td>39.420904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V21</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>1.654067e-16</td>\n",
              "      <td>0.734524</td>\n",
              "      <td>-34.830382</td>\n",
              "      <td>-0.228395</td>\n",
              "      <td>-0.029450</td>\n",
              "      <td>0.186377</td>\n",
              "      <td>27.202839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V22</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>-3.568593e-16</td>\n",
              "      <td>0.725702</td>\n",
              "      <td>-10.933144</td>\n",
              "      <td>-0.542350</td>\n",
              "      <td>0.006782</td>\n",
              "      <td>0.528554</td>\n",
              "      <td>10.503090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V23</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>2.578648e-16</td>\n",
              "      <td>0.624460</td>\n",
              "      <td>-44.807735</td>\n",
              "      <td>-0.161846</td>\n",
              "      <td>-0.011193</td>\n",
              "      <td>0.147642</td>\n",
              "      <td>22.528412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V24</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>4.473266e-15</td>\n",
              "      <td>0.605647</td>\n",
              "      <td>-2.836627</td>\n",
              "      <td>-0.354586</td>\n",
              "      <td>0.040976</td>\n",
              "      <td>0.439527</td>\n",
              "      <td>4.584549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V25</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>5.340915e-16</td>\n",
              "      <td>0.521278</td>\n",
              "      <td>-10.295397</td>\n",
              "      <td>-0.317145</td>\n",
              "      <td>0.016594</td>\n",
              "      <td>0.350716</td>\n",
              "      <td>7.519589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V26</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>1.683437e-15</td>\n",
              "      <td>0.482227</td>\n",
              "      <td>-2.604551</td>\n",
              "      <td>-0.326984</td>\n",
              "      <td>-0.052139</td>\n",
              "      <td>0.240952</td>\n",
              "      <td>3.517346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V27</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>-3.660091e-16</td>\n",
              "      <td>0.403632</td>\n",
              "      <td>-22.565679</td>\n",
              "      <td>-0.070840</td>\n",
              "      <td>0.001342</td>\n",
              "      <td>0.091045</td>\n",
              "      <td>31.612198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V28</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>-1.227390e-16</td>\n",
              "      <td>0.330083</td>\n",
              "      <td>-15.430084</td>\n",
              "      <td>-0.052960</td>\n",
              "      <td>0.011244</td>\n",
              "      <td>0.078280</td>\n",
              "      <td>33.847808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Amount</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>8.834962e+01</td>\n",
              "      <td>250.120109</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>77.165000</td>\n",
              "      <td>25691.160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <td>284807.0</td>\n",
              "      <td>1.727486e-03</td>\n",
              "      <td>0.041527</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-660cc288-de94-49bc-8fad-da4b42f582b9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-660cc288-de94-49bc-8fad-da4b42f582b9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-660cc288-de94-49bc-8fad-da4b42f582b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df_credit_card.describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "pDpcreZ7mZ6s",
        "outputId": "fbcdc5d0-ec53-4936-8585-9725206be9f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O Valor que as classes do DataSet Possui São: [0 1]\n"
          ]
        }
      ],
      "source": [
        "# Classe \n",
        "print(f\"O Valor que as classes do DataSet Possui São: {np.unique(df_credit_card['Class'])}\" )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "xGiTfmjWmZ6t",
        "outputId": "070bd622-e411-4ec1-867b-b02f86631911",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6676a405e0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df_credit_card.groupby('Class')['Class'].count().plot.bar(logy=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlação das V com Class\n",
        "df_credit_card.corr()['Class'].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcdTUHrhhuwT",
        "outputId": "feb81897-cdbf-4a67-95ae-9e4291451c9e"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Class     1.000000\n",
              "V11       0.154876\n",
              "V4        0.133447\n",
              "V2        0.091289\n",
              "V21       0.040413\n",
              "V19       0.034783\n",
              "V20       0.020090\n",
              "V8        0.019875\n",
              "V27       0.017580\n",
              "V28       0.009536\n",
              "Amount    0.005632\n",
              "V26       0.004455\n",
              "V25       0.003308\n",
              "V22       0.000805\n",
              "V23      -0.002685\n",
              "V15      -0.004223\n",
              "V13      -0.004570\n",
              "V24      -0.007221\n",
              "Time     -0.017082\n",
              "V6       -0.043643\n",
              "V5       -0.094974\n",
              "V9       -0.097733\n",
              "V1       -0.101347\n",
              "V18      -0.111485\n",
              "V7       -0.187257\n",
              "V3       -0.192961\n",
              "V16      -0.196539\n",
              "V10      -0.216883\n",
              "V12      -0.260593\n",
              "V14      -0.302544\n",
              "V17      -0.326481\n",
              "Name: Class, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Estatísticas \n",
        "df_credit_card.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "AxxKleA9irS2",
        "outputId": "2c247117-6a52-4b22-ee85-64c690c6dd2d"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Time            V1            V2            V3            V4  \\\n",
              "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean       14.537951  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
              "std         5.847061  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
              "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
              "25%        10.598194 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
              "50%        15.010833  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
              "75%        19.329722  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
              "max        23.999444  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
              "\n",
              "                 V5            V6            V7            V8            V9  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
              "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
              "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
              "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
              "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
              "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
              "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
              "\n",
              "       ...           V21           V22           V23           V24  \\\n",
              "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
              "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
              "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
              "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
              "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
              "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
              "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
              "\n",
              "                V25           V26           V27           V28         Amount  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
              "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
              "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
              "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
              "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
              "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
              "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
              "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
              "\n",
              "               Class  \n",
              "count  284807.000000  \n",
              "mean        0.001727  \n",
              "std         0.041527  \n",
              "min         0.000000  \n",
              "25%         0.000000  \n",
              "50%         0.000000  \n",
              "75%         0.000000  \n",
              "max         1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e04671db-d9cf-4a55-a50f-7ebaad1f934f\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>284807.000000</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>...</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.537951</td>\n",
              "      <td>1.168375e-15</td>\n",
              "      <td>3.416908e-16</td>\n",
              "      <td>-1.379537e-15</td>\n",
              "      <td>2.074095e-15</td>\n",
              "      <td>9.604066e-16</td>\n",
              "      <td>1.487313e-15</td>\n",
              "      <td>-5.556467e-16</td>\n",
              "      <td>1.213481e-16</td>\n",
              "      <td>-2.406331e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>1.654067e-16</td>\n",
              "      <td>-3.568593e-16</td>\n",
              "      <td>2.578648e-16</td>\n",
              "      <td>4.473266e-15</td>\n",
              "      <td>5.340915e-16</td>\n",
              "      <td>1.683437e-15</td>\n",
              "      <td>-3.660091e-16</td>\n",
              "      <td>-1.227390e-16</td>\n",
              "      <td>88.349619</td>\n",
              "      <td>0.001727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.847061</td>\n",
              "      <td>1.958696e+00</td>\n",
              "      <td>1.651309e+00</td>\n",
              "      <td>1.516255e+00</td>\n",
              "      <td>1.415869e+00</td>\n",
              "      <td>1.380247e+00</td>\n",
              "      <td>1.332271e+00</td>\n",
              "      <td>1.237094e+00</td>\n",
              "      <td>1.194353e+00</td>\n",
              "      <td>1.098632e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>7.345240e-01</td>\n",
              "      <td>7.257016e-01</td>\n",
              "      <td>6.244603e-01</td>\n",
              "      <td>6.056471e-01</td>\n",
              "      <td>5.212781e-01</td>\n",
              "      <td>4.822270e-01</td>\n",
              "      <td>4.036325e-01</td>\n",
              "      <td>3.300833e-01</td>\n",
              "      <td>250.120109</td>\n",
              "      <td>0.041527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-5.640751e+01</td>\n",
              "      <td>-7.271573e+01</td>\n",
              "      <td>-4.832559e+01</td>\n",
              "      <td>-5.683171e+00</td>\n",
              "      <td>-1.137433e+02</td>\n",
              "      <td>-2.616051e+01</td>\n",
              "      <td>-4.355724e+01</td>\n",
              "      <td>-7.321672e+01</td>\n",
              "      <td>-1.343407e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.483038e+01</td>\n",
              "      <td>-1.093314e+01</td>\n",
              "      <td>-4.480774e+01</td>\n",
              "      <td>-2.836627e+00</td>\n",
              "      <td>-1.029540e+01</td>\n",
              "      <td>-2.604551e+00</td>\n",
              "      <td>-2.256568e+01</td>\n",
              "      <td>-1.543008e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>10.598194</td>\n",
              "      <td>-9.203734e-01</td>\n",
              "      <td>-5.985499e-01</td>\n",
              "      <td>-8.903648e-01</td>\n",
              "      <td>-8.486401e-01</td>\n",
              "      <td>-6.915971e-01</td>\n",
              "      <td>-7.682956e-01</td>\n",
              "      <td>-5.540759e-01</td>\n",
              "      <td>-2.086297e-01</td>\n",
              "      <td>-6.430976e-01</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.283949e-01</td>\n",
              "      <td>-5.423504e-01</td>\n",
              "      <td>-1.618463e-01</td>\n",
              "      <td>-3.545861e-01</td>\n",
              "      <td>-3.171451e-01</td>\n",
              "      <td>-3.269839e-01</td>\n",
              "      <td>-7.083953e-02</td>\n",
              "      <td>-5.295979e-02</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>15.010833</td>\n",
              "      <td>1.810880e-02</td>\n",
              "      <td>6.548556e-02</td>\n",
              "      <td>1.798463e-01</td>\n",
              "      <td>-1.984653e-02</td>\n",
              "      <td>-5.433583e-02</td>\n",
              "      <td>-2.741871e-01</td>\n",
              "      <td>4.010308e-02</td>\n",
              "      <td>2.235804e-02</td>\n",
              "      <td>-5.142873e-02</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.945017e-02</td>\n",
              "      <td>6.781943e-03</td>\n",
              "      <td>-1.119293e-02</td>\n",
              "      <td>4.097606e-02</td>\n",
              "      <td>1.659350e-02</td>\n",
              "      <td>-5.213911e-02</td>\n",
              "      <td>1.342146e-03</td>\n",
              "      <td>1.124383e-02</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>19.329722</td>\n",
              "      <td>1.315642e+00</td>\n",
              "      <td>8.037239e-01</td>\n",
              "      <td>1.027196e+00</td>\n",
              "      <td>7.433413e-01</td>\n",
              "      <td>6.119264e-01</td>\n",
              "      <td>3.985649e-01</td>\n",
              "      <td>5.704361e-01</td>\n",
              "      <td>3.273459e-01</td>\n",
              "      <td>5.971390e-01</td>\n",
              "      <td>...</td>\n",
              "      <td>1.863772e-01</td>\n",
              "      <td>5.285536e-01</td>\n",
              "      <td>1.476421e-01</td>\n",
              "      <td>4.395266e-01</td>\n",
              "      <td>3.507156e-01</td>\n",
              "      <td>2.409522e-01</td>\n",
              "      <td>9.104512e-02</td>\n",
              "      <td>7.827995e-02</td>\n",
              "      <td>77.165000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>23.999444</td>\n",
              "      <td>2.454930e+00</td>\n",
              "      <td>2.205773e+01</td>\n",
              "      <td>9.382558e+00</td>\n",
              "      <td>1.687534e+01</td>\n",
              "      <td>3.480167e+01</td>\n",
              "      <td>7.330163e+01</td>\n",
              "      <td>1.205895e+02</td>\n",
              "      <td>2.000721e+01</td>\n",
              "      <td>1.559499e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>2.720284e+01</td>\n",
              "      <td>1.050309e+01</td>\n",
              "      <td>2.252841e+01</td>\n",
              "      <td>4.584549e+00</td>\n",
              "      <td>7.519589e+00</td>\n",
              "      <td>3.517346e+00</td>\n",
              "      <td>3.161220e+01</td>\n",
              "      <td>3.384781e+01</td>\n",
              "      <td>25691.160000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e04671db-d9cf-4a55-a50f-7ebaad1f934f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          document.querySelector('#df-e04671db-d9cf-4a55-a50f-7ebaad1f934f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e04671db-d9cf-4a55-a50f-7ebaad1f934f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oc5kiSjkmZ6v"
      },
      "source": [
        "## Transformando os dados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "RbAMgbkhmZ6w"
      },
      "outputs": [],
      "source": [
        "# Atualizando o Tempo no Dia\n",
        "df_credit_card['Time'] = df_credit_card['Time'].apply(lambda t: (t/3600) % 24 )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "V_kBzxXrmZ6x",
        "outputId": "55982d99-0aac-4004-fec1-acfb488a0900",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Time        V1        V2        V3        V4        V5        V6  \\\n",
              "143548  0.006592 -0.128611  1.075975  0.138519  0.456091 -0.576381 -0.760609   \n",
              "61743   0.003855 -1.543238  1.491762  0.714397 -0.756657 -0.460294 -0.225442   \n",
              "90159   0.004852 -3.169848  0.948929 -0.032998  0.540360 -1.609779  1.274438   \n",
              "225085  0.004452  1.605885  0.763472 -1.959910  4.134407  1.176256 -0.023991   \n",
              "236085  0.004804 -0.388313  0.969079  0.461054 -1.294712  1.089623 -0.522115   \n",
              "\n",
              "              V7        V8        V9  ...       V21       V22       V23  \\\n",
              "143548  0.044779 -0.301536  0.451043  ...  0.091089 -0.301917  0.492016   \n",
              "61743  -0.125885  1.086177 -0.433652  ... -0.058679 -0.180161  0.051298   \n",
              "90159  -2.338781  2.536940 -1.778784  ...  0.259608  0.707830 -0.132287   \n",
              "225085  0.535264 -0.002322 -1.399431  ... -0.286041 -1.105800  0.182600   \n",
              "236085  1.223895 -0.125373 -0.676752  ...  0.379878  1.107738 -0.419110   \n",
              "\n",
              "             V24       V25       V26       V27       V28  Amount  Class  \n",
              "143548  0.483813 -1.356924 -0.177239 -1.313575 -0.145341    0.99      0  \n",
              "61743   0.035290 -0.213853  0.314645  0.172725  0.100800    4.00      0  \n",
              "90159  -0.590894 -0.551618 -0.133874 -0.779636 -0.344006   14.84      0  \n",
              "225085  0.458314 -0.243379 -0.369177 -0.031390  0.041792  139.61      0  \n",
              "236085  0.819028  0.287261 -0.284595  0.100693  0.135495    3.00      0  \n",
              "\n",
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              "      <td>-0.133874</td>\n",
              "      <td>-0.779636</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "# Simplificando o Dataset\n",
        "normal_trans = df_credit_card[df_credit_card['Class'] == 0].sample(4000)\n",
        "fraud_trans = df_credit_card[df_credit_card['Class'] == 1]\n",
        "\n",
        "normal_trans.head()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Total de Transações\n"
      ],
      "metadata": {
        "id": "0UHv8bGg0w2B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Total de transações fraudulentas\n",
        "fraud_trans.Amount.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x_8vuF7TywAE",
        "outputId": "64b15944-ea0c-4d42-f306-335456725eb8"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     492.000000\n",
              "mean      122.211321\n",
              "std       256.683288\n",
              "min         0.000000\n",
              "25%         1.000000\n",
              "50%         9.250000\n",
              "75%       105.890000\n",
              "max      2125.870000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Total de transações Não Fraude\n",
        "normal_trans.Amount.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkBzlDY20SwY",
        "outputId": "37562070-267d-4fb2-c1b6-cd5a29248877"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    4000.000000\n",
              "mean       89.519435\n",
              "std       231.461975\n",
              "min         0.000000\n",
              "25%         5.127500\n",
              "50%        20.505000\n",
              "75%        78.817500\n",
              "max      4276.820000\n",
              "Name: Amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f, (ax1, ax2) = plt.subplots(2, 1, sharex=True) \n",
        "f.suptitle('Valor por transação por Classe') \n",
        "\n",
        "bins = 10\n",
        "ax1.hist(fraud_trans.Amount, bins = bins) \n",
        "ax1.set_title ('Fraude') \n",
        "\n",
        "ax2.hist(normal_trans.Amount, bins = bins) \n",
        "ax2.set_title('Não Fraude') \n",
        "\n",
        "plt.xlabel('Valor ($)') \n",
        "plt.ylabel('Número de Transações') \n",
        "plt.xlim( (0, 5000)) \n",
        "plt.yscale('log') \n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "3bvpaW1bzYxB",
        "outputId": "df63c427-e0b5-4969-85ef-30beb924b147"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "NhMEAva9mZ6x"
      },
      "outputs": [],
      "source": [
        "reduced_set = normal_trans.append(fraud_trans).reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "GvL4BhjQmZ6y",
        "outputId": "2cd39b7f-5727-4964-f42b-afc40861f7d6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cleansed dataset shape : (4492, 31)\n"
          ]
        }
      ],
      "source": [
        "print(f\"Cleansed dataset shape : {reduced_set.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uWg2OegOmZ6z"
      },
      "source": [
        "## Dividir o conjunto de dados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "wbcbYSX6mZ6z"
      },
      "outputs": [],
      "source": [
        "# Dividindo o Banco de Dados com a Classificação\n",
        "y = reduced_set['Class']\n",
        "X = reduced_set.drop('Class', axis=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "UFU3t13RmZ60",
        "outputId": "1b94fb22-c2b2-473e-8cdb-9c504da508ad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of Features : (4492, 30) and Target: (4492,)\n"
          ]
        }
      ],
      "source": [
        "print(f\"Shape of Features : {X.shape} and Target: {y.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W_hOwdx-mZ60"
      },
      "source": [
        "## Trabalhando a Diminuição de Dimensionalidade "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "6rynyHRrmZ61",
        "outputId": "7dcd3251-668b-49e3-b364-0e43abe9567a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def tsne_plot(X, y):\n",
        "      \n",
        "    # Definindo o plano de fundo da plotagem\n",
        "    sns.set(style =\"whitegrid\")\n",
        "    #pca = PCA()\n",
        "    tsne = TSNE(n_components = 2, random_state = 0)\n",
        "      \n",
        "    # Reduzindo a dimensionalidade dos dados\n",
        "    X_transformed = tsne.fit_transform(X)\n",
        "    \n",
        "    plt.figure(figsize =(12, 8))\n",
        "      \n",
        "    # Construindo o gráfico de dispersão\n",
        "    plt.scatter(X_transformed[np.where(y == 0), 0], \n",
        "                X_transformed[np.where(y == 0), 1],\n",
        "                marker ='o', color ='yellow', linewidth =1,\n",
        "                alpha = 0.8, label ='Dados Normais')\n",
        "    plt.scatter(X_transformed[np.where(y == 1), 0],\n",
        "                X_transformed[np.where(y == 1), 1],\n",
        "                marker ='o', color ='red', linewidth =1,\n",
        "                alpha = 0.8, label ='Fraudes no Cartão de Crédito')\n",
        "  \n",
        "    # Especificando a localização da legenda\n",
        "    plt.legend(loc ='best')\n",
        "      \n",
        "    # Plotando os dados reduzidos\n",
        "    plt.show()\n",
        "\n",
        "tsne_plot(X,y)\n",
        "\n",
        "\n",
        "#from sklearn.datasets import load_breast_cancer\n",
        "#from sklearn.preprocessing import StandardScaler\n",
        "#import mglearn\n",
        "#cancer = load_breast_cancer()\n",
        "#scaler = StandardScaler()\n",
        "#scaler.fit(cancer.data)\n",
        "#X_scaled = scaler.transform(cancer.data)\n",
        "\n",
        "#from sklearn.decomposition import PCA\n",
        "# keep the first two principal components of the data\n",
        "#pca = PCA(n_components=2)\n",
        "# fit PCA model to breast cancer data\n",
        "#pca.fit(X_scaled)\n",
        "# transform data onto the first two principal components\n",
        "#X_pca = pca.transform(X_scaled)\n",
        "#print(\"Original shape: {}\".format(str(X_scaled.shape)))\n",
        "#print(\"Reduced shape: {}\".format(str(X_pca.shape)))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ADVS2IBmZ63"
      },
      "source": [
        "## Normalizando os Valores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "FzNeIwjhmZ63"
      },
      "outputs": [],
      "source": [
        "scaler = RobustScaler().fit_transform(X)\n",
        "\n",
        "# Dados dimensionados\n",
        "X_scaled_normal = scaler[y == 0]\n",
        "X_scaled_fraud = scaler[y == 1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zH8Juum-mZ63"
      },
      "source": [
        "## Construindo um modelo de Auto Encoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "Yn8BS35jmZ64",
        "outputId": "f84f461c-38ff-47ff-a0dd-490e18f501c8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Formato dos dados de entrada : 30\n"
          ]
        }
      ],
      "source": [
        "print(f\"Formato dos dados de entrada : {X.shape[1]}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "4iGKv9KZmZ65"
      },
      "outputs": [],
      "source": [
        "# Camada de entrada com uma forma de features/colunas do conjunto de dados\n",
        "input_layer = Input(shape = (X.shape[1], ))\n",
        "\n",
        "# Construindo uma rede encoder\n",
        "encoded = Dense(500, activation= 'tanh', activity_regularizer=regularizers.l1(10e-5))(input_layer)\n",
        "encoded = Dense(250, activation= 'tanh', activity_regularizer=regularizers.l1(10e-5))(input_layer)\n",
        "encoded = Dense(125, activation='tanh', activity_regularizer=regularizers.l1(10e-5))(encoded)\n",
        "encoded = Dense(62, activation='tanh', activity_regularizer=regularizers.l1(10e-5))(encoded)\n",
        "encoded = Dense(31, activation = 'tanh', activity_regularizer=regularizers.l1(10e-5))(encoded)\n",
        "encoded = Dense(50, activation='tanh')(encoded)\n",
        "\n",
        "# Decodificando a Rede\n",
        "decoded = Dense(31, activation='tanh')(encoded)\n",
        "decoded = Dense(62, activation='tanh')(decoded)\n",
        "decoded = Dense(125, activation='tanh')(decoded)\n",
        "decoded = Dense(250, activation='tanh')(decoded)\n",
        "decoded = Dense(500, activation='tanh')(decoded)\n",
        "\n",
        "output_layer = Dense(X.shape[1], activation='tanh')(decoded)\n",
        "\n",
        "auto_encoder = Model(input_layer, output_layer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "PEP-YyyAmZ65",
        "outputId": "27ef9b30-855c-4a21-cf87-1ac7ffc91952",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "100/100 [==============================] - 2s 10ms/step - loss: 1.5036 - val_loss: 2.3010\n",
            "Epoch 2/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.4938 - val_loss: 2.2899\n",
            "Epoch 3/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.4843 - val_loss: 2.2790\n",
            "Epoch 4/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.4752 - val_loss: 2.2687\n",
            "Epoch 5/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.4664 - val_loss: 2.2585\n",
            "Epoch 6/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.4579 - val_loss: 2.2488\n",
            "Epoch 7/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.4498 - val_loss: 2.2396\n",
            "Epoch 8/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.4419 - val_loss: 2.2304\n",
            "Epoch 9/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.4341 - val_loss: 2.2215\n",
            "Epoch 10/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.4265 - val_loss: 2.2128\n",
            "Epoch 11/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.4192 - val_loss: 2.2045\n",
            "Epoch 12/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.4121 - val_loss: 2.1964\n",
            "Epoch 13/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.4053 - val_loss: 2.1886\n",
            "Epoch 14/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.3987 - val_loss: 2.1811\n",
            "Epoch 15/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.3924 - val_loss: 2.1740\n",
            "Epoch 16/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.3863 - val_loss: 2.1670\n",
            "Epoch 17/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.3804 - val_loss: 2.1604\n",
            "Epoch 18/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.3746 - val_loss: 2.1539\n",
            "Epoch 19/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.3690 - val_loss: 2.1476\n",
            "Epoch 20/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.3637 - val_loss: 2.1416\n",
            "Epoch 21/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.3585 - val_loss: 2.1359\n",
            "Epoch 22/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.3535 - val_loss: 2.1303\n",
            "Epoch 23/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.3487 - val_loss: 2.1248\n",
            "Epoch 24/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.3439 - val_loss: 2.1196\n",
            "Epoch 25/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.3394 - val_loss: 2.1146\n",
            "Epoch 26/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.3350 - val_loss: 2.1096\n",
            "Epoch 27/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.3307 - val_loss: 2.1050\n",
            "Epoch 28/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.3266 - val_loss: 2.1004\n",
            "Epoch 29/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.3226 - val_loss: 2.0961\n",
            "Epoch 30/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.3188 - val_loss: 2.0919\n",
            "Epoch 31/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.3150 - val_loss: 2.0878\n",
            "Epoch 32/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.3114 - val_loss: 2.0838\n",
            "Epoch 33/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.3079 - val_loss: 2.0800\n",
            "Epoch 34/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.3045 - val_loss: 2.0763\n",
            "Epoch 35/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.3011 - val_loss: 2.0726\n",
            "Epoch 36/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2979 - val_loss: 2.0691\n",
            "Epoch 37/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2947 - val_loss: 2.0657\n",
            "Epoch 38/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2917 - val_loss: 2.0624\n",
            "Epoch 39/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2887 - val_loss: 2.0592\n",
            "Epoch 40/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2858 - val_loss: 2.0561\n",
            "Epoch 41/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2829 - val_loss: 2.0530\n",
            "Epoch 42/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2801 - val_loss: 2.0499\n",
            "Epoch 43/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2774 - val_loss: 2.0470\n",
            "Epoch 44/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2747 - val_loss: 2.0441\n",
            "Epoch 45/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2721 - val_loss: 2.0413\n",
            "Epoch 46/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2695 - val_loss: 2.0385\n",
            "Epoch 47/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2670 - val_loss: 2.0358\n",
            "Epoch 48/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2645 - val_loss: 2.0332\n",
            "Epoch 49/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2621 - val_loss: 2.0306\n",
            "Epoch 50/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2597 - val_loss: 2.0280\n",
            "Epoch 51/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2574 - val_loss: 2.0255\n",
            "Epoch 52/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2551 - val_loss: 2.0231\n",
            "Epoch 53/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2529 - val_loss: 2.0208\n",
            "Epoch 54/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2508 - val_loss: 2.0185\n",
            "Epoch 55/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2486 - val_loss: 2.0162\n",
            "Epoch 56/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2465 - val_loss: 2.0139\n",
            "Epoch 57/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2445 - val_loss: 2.0117\n",
            "Epoch 58/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2424 - val_loss: 2.0096\n",
            "Epoch 59/100\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 1.2405 - val_loss: 2.0074\n",
            "Epoch 60/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2385 - val_loss: 2.0054\n",
            "Epoch 61/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2366 - val_loss: 2.0033\n",
            "Epoch 62/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2347 - val_loss: 2.0013\n",
            "Epoch 63/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2329 - val_loss: 1.9993\n",
            "Epoch 64/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2310 - val_loss: 1.9973\n",
            "Epoch 65/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2292 - val_loss: 1.9954\n",
            "Epoch 66/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2275 - val_loss: 1.9935\n",
            "Epoch 67/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2257 - val_loss: 1.9917\n",
            "Epoch 68/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2240 - val_loss: 1.9898\n",
            "Epoch 69/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2223 - val_loss: 1.9880\n",
            "Epoch 70/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2206 - val_loss: 1.9862\n",
            "Epoch 71/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2190 - val_loss: 1.9845\n",
            "Epoch 72/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2174 - val_loss: 1.9827\n",
            "Epoch 73/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.2158 - val_loss: 1.9810\n",
            "Epoch 74/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2142 - val_loss: 1.9793\n",
            "Epoch 75/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2127 - val_loss: 1.9777\n",
            "Epoch 76/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2112 - val_loss: 1.9761\n",
            "Epoch 77/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2096 - val_loss: 1.9744\n",
            "Epoch 78/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2081 - val_loss: 1.9728\n",
            "Epoch 79/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2067 - val_loss: 1.9712\n",
            "Epoch 80/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2052 - val_loss: 1.9697\n",
            "Epoch 81/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2038 - val_loss: 1.9682\n",
            "Epoch 82/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2024 - val_loss: 1.9667\n",
            "Epoch 83/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.2010 - val_loss: 1.9652\n",
            "Epoch 84/100\n",
            "100/100 [==============================] - 1s 9ms/step - loss: 1.1996 - val_loss: 1.9637\n",
            "Epoch 85/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.1983 - val_loss: 1.9623\n",
            "Epoch 86/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.1970 - val_loss: 1.9609\n",
            "Epoch 87/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1956 - val_loss: 1.9595\n",
            "Epoch 88/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.1943 - val_loss: 1.9581\n",
            "Epoch 89/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1930 - val_loss: 1.9567\n",
            "Epoch 90/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.1917 - val_loss: 1.9553\n",
            "Epoch 91/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1905 - val_loss: 1.9540\n",
            "Epoch 92/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1893 - val_loss: 1.9526\n",
            "Epoch 93/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1880 - val_loss: 1.9513\n",
            "Epoch 94/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.1868 - val_loss: 1.9500\n",
            "Epoch 95/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.1856 - val_loss: 1.9488\n",
            "Epoch 96/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1844 - val_loss: 1.9475\n",
            "Epoch 97/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1833 - val_loss: 1.9462\n",
            "Epoch 98/100\n",
            "100/100 [==============================] - 1s 7ms/step - loss: 1.1821 - val_loss: 1.9450\n",
            "Epoch 99/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1809 - val_loss: 1.9438\n",
            "Epoch 100/100\n",
            "100/100 [==============================] - 1s 8ms/step - loss: 1.1798 - val_loss: 1.9425\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f666d7f6f70>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# Compilando o modelo\n",
        "auto_encoder.compile(optimizer='adadelta', loss='mse')\n",
        "\n",
        "# Treinando o Modelo\n",
        "auto_encoder.fit(X_scaled_normal, X_scaled_normal, batch_size=32, epochs=100, shuffle=True, validation_split=0.20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ioM7-7-lmZ66"
      },
      "source": [
        "## Utilizar Auto Encoder para os dados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "TxR1jozFmZ66"
      },
      "outputs": [],
      "source": [
        "latent_model = Sequential()\n",
        "latent_model.add(auto_encoder.layers[0])\n",
        "latent_model.add(auto_encoder.layers[1])\n",
        "latent_model.add(auto_encoder.layers[2])\n",
        "latent_model.add(auto_encoder.layers[3])\n",
        "latent_model.add(auto_encoder.layers[4])\n",
        "latent_model.add(auto_encoder.layers[5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "srvrEjxNmZ66",
        "outputId": "c0ae2110-d5d6-4a08-9a82-cbb9304bafe4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "125/125 [==============================] - 0s 2ms/step\n",
            "16/16 [==============================] - 0s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "normal_tran_points = latent_model.predict(X_scaled_normal)\n",
        "fraud_tran_points = latent_model.predict(X_scaled_fraud)\n",
        "\n",
        "\n",
        "# Fazendo como uma coleção\n",
        "encoded_X = np.append(normal_tran_points, fraud_tran_points, axis=0)\n",
        "y_normal = np.zeros(normal_tran_points.shape[0])\n",
        "y_fraud = np.ones(fraud_tran_points.shape[0])\n",
        "encoded_y = np.append(y_normal, y_fraud, axis=0)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "Hx4AnCW2mZ67",
        "outputId": "55e701d0-90ee-4666-8b14-850972e99b19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Chamando a função de gráfico TSNE\n",
        "tsne_plot(encoded_X, encoded_y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uG0nQvYYmZ67"
      },
      "source": [
        "## Divisão entre Treino e Teste"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "p3iQGNMgmZ67"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
        "X_enc_train, X_enc_test, y_enc_train, y_enc_test = train_test_split(encoded_X, encoded_y, test_size=0.3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "iOGwygXpmZ67",
        "outputId": "aca7359e-b99c-42b7-a5e2-b48be1001d77",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Encoded train data X: (3144, 50), Y: (3144,), X_test :(1348, 50), Y_test: (1348,)\n",
            "Treino Versos Teste: (3144, 30), Y: (3144, 30), X_test :(1348, 30), Y_test: (1348,)\n"
          ]
        }
      ],
      "source": [
        "print(f\"Encoded train data X: {X_enc_train.shape}, Y: {y_enc_train.shape}, X_test :{X_enc_test.shape}, Y_test: {y_enc_test.shape}\")\n",
        "print(f\"Treino Versos Teste: {X_train.shape}, Y: {X_train.shape}, X_test :{X_test.shape}, Y_test: {y_test.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pBkGinubmZ68"
      },
      "source": [
        "## Classificação Não Linear"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "7Uv4sWdRmZ68"
      },
      "outputs": [],
      "source": [
        "# Instância de SVM\n",
        "svc_clf = SVC()\n",
        "\n",
        "svc_clf.fit(X_train, y_train)\n",
        "\n",
        "svc_predictions = svc_clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "FGHrPzZ2mZ68",
        "outputId": "2462b28f-6d03-4b86-bd3f-f3fbb41dfae5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relatório de resultados na Classificação \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      1.00      0.97      1196\n",
            "           1       0.99      0.50      0.66       152\n",
            "\n",
            "    accuracy                           0.94      1348\n",
            "   macro avg       0.96      0.75      0.82      1348\n",
            "weighted avg       0.95      0.94      0.93      1348\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Relatório de resultados na Classificação \\n {0}\".format(classification_report(y_test, svc_predictions)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "Ya5wSKI3mZ69",
        "outputId": "d5035a4a-91f9-484b-87e6-d0a8d0b4547a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia \n",
            " 0.94\n"
          ]
        }
      ],
      "source": [
        "print(\"Acurácia \\n {:.2f}\".format(accuracy_score(y_test, svc_predictions)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UmInVzaRmZ69"
      },
      "source": [
        "## Classificação Linear\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "_JiuhjkEmZ69"
      },
      "outputs": [],
      "source": [
        "lr_clf = LogisticRegression()\n",
        "\n",
        "lr_clf.fit(X_enc_train, y_enc_train)\n",
        "\n",
        "# Predição dos dados de teste\n",
        "predictions = lr_clf.predict(X_enc_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "EcED4APdmZ6-",
        "outputId": "8caabdf9-1558-4adb-9198-9c1ac7a2bcb0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dados de Classificação \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.97      1.00      0.98      1199\n",
            "         1.0       1.00      0.72      0.84       149\n",
            "\n",
            "    accuracy                           0.97      1348\n",
            "   macro avg       0.98      0.86      0.91      1348\n",
            "weighted avg       0.97      0.97      0.97      1348\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Dados de Classificação \\n {0}\".format(classification_report(y_enc_test, predictions)))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "cCN8T54DmZ6-",
        "outputId": "cb9c566f-3725-430b-858c-681c46f90aa7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fator de Acurácia : 0.97\n"
          ]
        }
      ],
      "source": [
        "print(\"Fator de Acurácia : {:.2f}\".format(accuracy_score(y_enc_test, predictions)))"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
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