Credit_Card_FDS.ipynb

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    "colab": {
      "name": "Credit Card FDS.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNphVBUCTjDWNyJ0qaK8P3C",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tylee33/DLforFinance/blob/master/Credit_Card_FDS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b17cd6heUIzL",
        "colab_type": "text"
      },
      "source": [
        "데이터 셋의 경우 kaggle에서 제공하는 Credit Card Fraud Detection Dataset을 이용하였습니다.\n",
        "위 데이터 셋은 2013년 9월 유럽의 실제 신용 카드 거래 데이터를 담고 있습니다. 데이터는 총 284,807건이며 그 중 492건만이 부정 거래 데이터 입니다.\n",
        "즉, 데이터가 매우 불균형(imbalanced) 합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTVkTW04S_hP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "41f64ad8-e640-485b-8ba5-8246d7e37c1a"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('./creditcard.csv')\n",
        "df.head(10)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</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>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</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>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</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>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</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>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</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>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.425966</td>\n",
              "      <td>0.960523</td>\n",
              "      <td>1.141109</td>\n",
              "      <td>-0.168252</td>\n",
              "      <td>0.420987</td>\n",
              "      <td>-0.029728</td>\n",
              "      <td>0.476201</td>\n",
              "      <td>0.260314</td>\n",
              "      <td>-0.568671</td>\n",
              "      <td>-0.371407</td>\n",
              "      <td>1.341262</td>\n",
              "      <td>0.359894</td>\n",
              "      <td>-0.358091</td>\n",
              "      <td>-0.137134</td>\n",
              "      <td>0.517617</td>\n",
              "      <td>0.401726</td>\n",
              "      <td>-0.058133</td>\n",
              "      <td>0.068653</td>\n",
              "      <td>-0.033194</td>\n",
              "      <td>0.084968</td>\n",
              "      <td>-0.208254</td>\n",
              "      <td>-0.559825</td>\n",
              "      <td>-0.026398</td>\n",
              "      <td>-0.371427</td>\n",
              "      <td>-0.232794</td>\n",
              "      <td>0.105915</td>\n",
              "      <td>0.253844</td>\n",
              "      <td>0.081080</td>\n",
              "      <td>3.67</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.0</td>\n",
              "      <td>1.229658</td>\n",
              "      <td>0.141004</td>\n",
              "      <td>0.045371</td>\n",
              "      <td>1.202613</td>\n",
              "      <td>0.191881</td>\n",
              "      <td>0.272708</td>\n",
              "      <td>-0.005159</td>\n",
              "      <td>0.081213</td>\n",
              "      <td>0.464960</td>\n",
              "      <td>-0.099254</td>\n",
              "      <td>-1.416907</td>\n",
              "      <td>-0.153826</td>\n",
              "      <td>-0.751063</td>\n",
              "      <td>0.167372</td>\n",
              "      <td>0.050144</td>\n",
              "      <td>-0.443587</td>\n",
              "      <td>0.002821</td>\n",
              "      <td>-0.611987</td>\n",
              "      <td>-0.045575</td>\n",
              "      <td>-0.219633</td>\n",
              "      <td>-0.167716</td>\n",
              "      <td>-0.270710</td>\n",
              "      <td>-0.154104</td>\n",
              "      <td>-0.780055</td>\n",
              "      <td>0.750137</td>\n",
              "      <td>-0.257237</td>\n",
              "      <td>0.034507</td>\n",
              "      <td>0.005168</td>\n",
              "      <td>4.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7.0</td>\n",
              "      <td>-0.644269</td>\n",
              "      <td>1.417964</td>\n",
              "      <td>1.074380</td>\n",
              "      <td>-0.492199</td>\n",
              "      <td>0.948934</td>\n",
              "      <td>0.428118</td>\n",
              "      <td>1.120631</td>\n",
              "      <td>-3.807864</td>\n",
              "      <td>0.615375</td>\n",
              "      <td>1.249376</td>\n",
              "      <td>-0.619468</td>\n",
              "      <td>0.291474</td>\n",
              "      <td>1.757964</td>\n",
              "      <td>-1.323865</td>\n",
              "      <td>0.686133</td>\n",
              "      <td>-0.076127</td>\n",
              "      <td>-1.222127</td>\n",
              "      <td>-0.358222</td>\n",
              "      <td>0.324505</td>\n",
              "      <td>-0.156742</td>\n",
              "      <td>1.943465</td>\n",
              "      <td>-1.015455</td>\n",
              "      <td>0.057504</td>\n",
              "      <td>-0.649709</td>\n",
              "      <td>-0.415267</td>\n",
              "      <td>-0.051634</td>\n",
              "      <td>-1.206921</td>\n",
              "      <td>-1.085339</td>\n",
              "      <td>40.80</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7.0</td>\n",
              "      <td>-0.894286</td>\n",
              "      <td>0.286157</td>\n",
              "      <td>-0.113192</td>\n",
              "      <td>-0.271526</td>\n",
              "      <td>2.669599</td>\n",
              "      <td>3.721818</td>\n",
              "      <td>0.370145</td>\n",
              "      <td>0.851084</td>\n",
              "      <td>-0.392048</td>\n",
              "      <td>-0.410430</td>\n",
              "      <td>-0.705117</td>\n",
              "      <td>-0.110452</td>\n",
              "      <td>-0.286254</td>\n",
              "      <td>0.074355</td>\n",
              "      <td>-0.328783</td>\n",
              "      <td>-0.210077</td>\n",
              "      <td>-0.499768</td>\n",
              "      <td>0.118765</td>\n",
              "      <td>0.570328</td>\n",
              "      <td>0.052736</td>\n",
              "      <td>-0.073425</td>\n",
              "      <td>-0.268092</td>\n",
              "      <td>-0.204233</td>\n",
              "      <td>1.011592</td>\n",
              "      <td>0.373205</td>\n",
              "      <td>-0.384157</td>\n",
              "      <td>0.011747</td>\n",
              "      <td>0.142404</td>\n",
              "      <td>93.20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9.0</td>\n",
              "      <td>-0.338262</td>\n",
              "      <td>1.119593</td>\n",
              "      <td>1.044367</td>\n",
              "      <td>-0.222187</td>\n",
              "      <td>0.499361</td>\n",
              "      <td>-0.246761</td>\n",
              "      <td>0.651583</td>\n",
              "      <td>0.069539</td>\n",
              "      <td>-0.736727</td>\n",
              "      <td>-0.366846</td>\n",
              "      <td>1.017614</td>\n",
              "      <td>0.836390</td>\n",
              "      <td>1.006844</td>\n",
              "      <td>-0.443523</td>\n",
              "      <td>0.150219</td>\n",
              "      <td>0.739453</td>\n",
              "      <td>-0.540980</td>\n",
              "      <td>0.476677</td>\n",
              "      <td>0.451773</td>\n",
              "      <td>0.203711</td>\n",
              "      <td>-0.246914</td>\n",
              "      <td>-0.633753</td>\n",
              "      <td>-0.120794</td>\n",
              "      <td>-0.385050</td>\n",
              "      <td>-0.069733</td>\n",
              "      <td>0.094199</td>\n",
              "      <td>0.246219</td>\n",
              "      <td>0.083076</td>\n",
              "      <td>3.68</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0   0.0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62      0\n",
              "1   0.0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69      0\n",
              "2   1.0 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66      0\n",
              "3   1.0 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50      0\n",
              "4   2.0 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99      0\n",
              "5   2.0 -0.425966  0.960523  1.141109  ...  0.253844  0.081080    3.67      0\n",
              "6   4.0  1.229658  0.141004  0.045371  ...  0.034507  0.005168    4.99      0\n",
              "7   7.0 -0.644269  1.417964  1.074380  ... -1.206921 -1.085339   40.80      0\n",
              "8   7.0 -0.894286  0.286157 -0.113192  ...  0.011747  0.142404   93.20      0\n",
              "9   9.0 -0.338262  1.119593  1.044367  ...  0.246219  0.083076    3.68      0\n",
              "\n",
              "[10 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gmwMwEBFUNwT",
        "colab_type": "text"
      },
      "source": [
        "위 데이터 셋은 개인정보 비식별화처리로 인해 칼럼정보를 알 수 없으며, 데이터 또한 스케일(scale) 및 PCA(principal component analysis) 처리 되어있습니다.\n",
        "총 31개의 칼럼으로 이루어져 있고, Time, Amount, Class를 제외한 모든 칼럼은 비식별화처리 되어있습니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aaT1X10zUDNL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "outputId": "756e9c51-8fb3-417d-cd4f-77b8abf2d47c"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284807 entries, 0 to 284806\n",
            "Data columns (total 31 columns):\n",
            "Time      284807 non-null float64\n",
            "V1        284807 non-null float64\n",
            "V2        284807 non-null float64\n",
            "V3        284807 non-null float64\n",
            "V4        284807 non-null float64\n",
            "V5        284807 non-null float64\n",
            "V6        284807 non-null float64\n",
            "V7        284807 non-null float64\n",
            "V8        284807 non-null float64\n",
            "V9        284807 non-null float64\n",
            "V10       284807 non-null float64\n",
            "V11       284807 non-null float64\n",
            "V12       284807 non-null float64\n",
            "V13       284807 non-null float64\n",
            "V14       284807 non-null float64\n",
            "V15       284807 non-null float64\n",
            "V16       284807 non-null float64\n",
            "V17       284807 non-null float64\n",
            "V18       284807 non-null float64\n",
            "V19       284807 non-null float64\n",
            "V20       284807 non-null float64\n",
            "V21       284807 non-null float64\n",
            "V22       284807 non-null float64\n",
            "V23       284807 non-null float64\n",
            "V24       284807 non-null float64\n",
            "V25       284807 non-null float64\n",
            "V26       284807 non-null float64\n",
            "V27       284807 non-null float64\n",
            "V28       284807 non-null float64\n",
            "Amount    284807 non-null float64\n",
            "Class     284807 non-null int64\n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 67.4 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMlXmXexY9-u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "14035621-b2a8-4bc3-8ec6-7aca982c3e15"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</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>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>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>2.848070e+05</td>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94813.859575</td>\n",
              "      <td>3.919560e-15</td>\n",
              "      <td>5.688174e-16</td>\n",
              "      <td>-8.769071e-15</td>\n",
              "      <td>2.782312e-15</td>\n",
              "      <td>-1.552563e-15</td>\n",
              "      <td>2.010663e-15</td>\n",
              "      <td>-1.694249e-15</td>\n",
              "      <td>-1.927028e-16</td>\n",
              "      <td>-3.137024e-15</td>\n",
              "      <td>1.768627e-15</td>\n",
              "      <td>9.170318e-16</td>\n",
              "      <td>-1.810658e-15</td>\n",
              "      <td>1.693438e-15</td>\n",
              "      <td>1.479045e-15</td>\n",
              "      <td>3.482336e-15</td>\n",
              "      <td>1.392007e-15</td>\n",
              "      <td>-7.528491e-16</td>\n",
              "      <td>4.328772e-16</td>\n",
              "      <td>9.049732e-16</td>\n",
              "      <td>5.085503e-16</td>\n",
              "      <td>1.537294e-16</td>\n",
              "      <td>7.959909e-16</td>\n",
              "      <td>5.367590e-16</td>\n",
              "      <td>4.458112e-15</td>\n",
              "      <td>1.453003e-15</td>\n",
              "      <td>1.699104e-15</td>\n",
              "      <td>-3.660161e-16</td>\n",
              "      <td>-1.206049e-16</td>\n",
              "      <td>88.349619</td>\n",
              "      <td>0.001727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47488.145955</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>1.088850e+00</td>\n",
              "      <td>1.020713e+00</td>\n",
              "      <td>9.992014e-01</td>\n",
              "      <td>9.952742e-01</td>\n",
              "      <td>9.585956e-01</td>\n",
              "      <td>9.153160e-01</td>\n",
              "      <td>8.762529e-01</td>\n",
              "      <td>8.493371e-01</td>\n",
              "      <td>8.381762e-01</td>\n",
              "      <td>8.140405e-01</td>\n",
              "      <td>7.709250e-01</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>-2.458826e+01</td>\n",
              "      <td>-4.797473e+00</td>\n",
              "      <td>-1.868371e+01</td>\n",
              "      <td>-5.791881e+00</td>\n",
              "      <td>-1.921433e+01</td>\n",
              "      <td>-4.498945e+00</td>\n",
              "      <td>-1.412985e+01</td>\n",
              "      <td>-2.516280e+01</td>\n",
              "      <td>-9.498746e+00</td>\n",
              "      <td>-7.213527e+00</td>\n",
              "      <td>-5.449772e+01</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>54201.500000</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>-5.354257e-01</td>\n",
              "      <td>-7.624942e-01</td>\n",
              "      <td>-4.055715e-01</td>\n",
              "      <td>-6.485393e-01</td>\n",
              "      <td>-4.255740e-01</td>\n",
              "      <td>-5.828843e-01</td>\n",
              "      <td>-4.680368e-01</td>\n",
              "      <td>-4.837483e-01</td>\n",
              "      <td>-4.988498e-01</td>\n",
              "      <td>-4.562989e-01</td>\n",
              "      <td>-2.117214e-01</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>84692.000000</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>-9.291738e-02</td>\n",
              "      <td>-3.275735e-02</td>\n",
              "      <td>1.400326e-01</td>\n",
              "      <td>-1.356806e-02</td>\n",
              "      <td>5.060132e-02</td>\n",
              "      <td>4.807155e-02</td>\n",
              "      <td>6.641332e-02</td>\n",
              "      <td>-6.567575e-02</td>\n",
              "      <td>-3.636312e-03</td>\n",
              "      <td>3.734823e-03</td>\n",
              "      <td>-6.248109e-02</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>139320.500000</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>4.539234e-01</td>\n",
              "      <td>7.395934e-01</td>\n",
              "      <td>6.182380e-01</td>\n",
              "      <td>6.625050e-01</td>\n",
              "      <td>4.931498e-01</td>\n",
              "      <td>6.488208e-01</td>\n",
              "      <td>5.232963e-01</td>\n",
              "      <td>3.996750e-01</td>\n",
              "      <td>5.008067e-01</td>\n",
              "      <td>4.589494e-01</td>\n",
              "      <td>1.330408e-01</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>172792.000000</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>2.374514e+01</td>\n",
              "      <td>1.201891e+01</td>\n",
              "      <td>7.848392e+00</td>\n",
              "      <td>7.126883e+00</td>\n",
              "      <td>1.052677e+01</td>\n",
              "      <td>8.877742e+00</td>\n",
              "      <td>1.731511e+01</td>\n",
              "      <td>9.253526e+00</td>\n",
              "      <td>5.041069e+00</td>\n",
              "      <td>5.591971e+00</td>\n",
              "      <td>3.942090e+01</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",
              "</div>"
            ],
            "text/plain": [
              "                Time            V1  ...         Amount          Class\n",
              "count  284807.000000  2.848070e+05  ...  284807.000000  284807.000000\n",
              "mean    94813.859575  3.919560e-15  ...      88.349619       0.001727\n",
              "std     47488.145955  1.958696e+00  ...     250.120109       0.041527\n",
              "min         0.000000 -5.640751e+01  ...       0.000000       0.000000\n",
              "25%     54201.500000 -9.203734e-01  ...       5.600000       0.000000\n",
              "50%     84692.000000  1.810880e-02  ...      22.000000       0.000000\n",
              "75%    139320.500000  1.315642e+00  ...      77.165000       0.000000\n",
              "max    172792.000000  2.454930e+00  ...   25691.160000       1.000000\n",
              "\n",
              "[8 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TG_n93m5UR25",
        "colab_type": "text"
      },
      "source": [
        "데이터는 총 284,807건이며 null값은 존재하지 않는 정형 데이터 입니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-hfMn0TqZF51",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8192c3e0-85ae-4e47-a807-4bc636938d36"
      },
      "source": [
        "df.isnull().sum().max()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUWoQixBZNlN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "f5cf747d-bef0-412f-fc45-170165fa1f4f"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
              "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
              "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
              "       'Class'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTSeQc99ZRqe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "8ac8df77-3f4e-4854-c9cb-d353248fac0b"
      },
      "source": [
        "# The classes are heavily skewed we need to solve this issue later.\n",
        "print('No Frauds', round(df['Class'].value_counts()[0]/len(df) * 100,2), '% of the dataset')\n",
        "print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100,2), '% of the dataset')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No Frauds 99.83 % of the dataset\n",
            "Frauds 0.17 % of the dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENseTGXqZRve",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "dd74ba03-f1f7-4ddb-e5a8-4c359cf8f448"
      },
      "source": [
        "colors = [\"#0101DF\", \"#DF0101\"]\n",
        "\n",
        "sns.countplot('Class', data=df, palette=colors)\n",
        "plt.title('Class Distributions \\n (0: No Fraud || 1: Fraud)', fontsize=14)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Class Distributions \\n (0: No Fraud || 1: Fraud)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lfEouWyrpuEr5DpKuz+U/krRFLt8yv16al49v1XGamdmGWnnmshb4dERMBCYDR0qamJd9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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5SxAkUWMZdBT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "5396a2be-169e-4fbb-fdf2-8d5c52b82fd8"
      },
      "source": [
        "fig, ax = plt.subplots(1, 2, figsize=(18,4))\n",
        "\n",
        "amount_val = df['Amount'].values\n",
        "time_val = df['Time'].values\n",
        "\n",
        "sns.distplot(amount_val, ax=ax[0], color='r')\n",
        "ax[0].set_title('Distribution of Transaction Amount', fontsize=14)\n",
        "ax[0].set_xlim([min(amount_val), max(amount_val)])\n",
        "\n",
        "sns.distplot(time_val, ax=ax[1], color='b')\n",
        "ax[1].set_title('Distribution of Transaction Time', fontsize=14)\n",
        "ax[1].set_xlim([min(time_val), max(time_val)])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 172792.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TpsHkyf636qijfEyLI4+MO8Idp0SFiIiI5JUcteT8Xp1R+SK2DdydWJ7yGCGE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\ndtm+mXsbPwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1296x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4KxypymnUWNw",
        "colab_type": "text"
      },
      "source": [
        "**데이터 탐색(EDA)**\n",
        "\n",
        "*   시간(Time)대별 정상/부정 거래 비율\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0jq9lN9RUY7R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "3b8202fa-e946-44bf-cbd6-92bc03014cd5"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 시간대별 트랜잭션 양\n",
        "f, (ax1, ax2) = plt.subplots(2,1, sharex=True, figsize=(12,4))\n",
        "ax1.hist(df.Time[df.Class==1], bins=50)\n",
        "ax2.hist(df.Time[df.Class==0], bins=50)\n",
        "\n",
        "ax1.set_title('Fraud')\n",
        "ax2.set_title('Normal')\n",
        "plt.xlabel('Time(in Seconds)'); plt.ylabel('Number of Transactions')\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4VW1XVfetqp2q6oFD005V9cAtOckKtyfw9aHla1k6qRSDn4qARwPntqJXtZ95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\nXJIkSerR/wdWjcDwrQGy4gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5PRgqDhvUtNW",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   금액(Amount)대별 정상/부정 거래 비율\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdte8y_cUz7f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "a5511605-b6ff-4ae5-e6e0-df9608fe0506"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 금액대별 트랜잭션 양\n",
        "f, (ax1, ax2) = plt.subplots(2,1, sharex=True, figsize=(12,4))\n",
        "ax1.hist(df.Amount[df.Class==1], bins=30)\n",
        "ax2.hist(df.Amount[df.Class==0], bins=30)\n",
        "ax1.set_title('Fraud')\n",
        "ax2.set_title('Normal')\n",
        "\n",
        "plt.xlabel('Amount ($)')\n",
        "plt.ylabel('Number of Transactions')\n",
        "plt.yscale('log')\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+z5o+upau2Rum+n1ufgcubvr7M8DjB/2ZF8IC/AadIRxXAJc3ywF+Z0d/maZv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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "alyyJoscU7Et",
        "colab_type": "text"
      },
      "source": [
        "정상 거래의 경우 다양한 금액대에서 발생되지만, 부정 거래의 경우 적은 금액에서 주로 발생하는 것 같습니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gPSvph9wVCe0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Since most of our data has already been scaled we should scale the columns that are left to scale (Amount and Time)\n",
        "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
        "\n",
        "# RobustScaler is less prone to outliers.\n",
        "\n",
        "std_scaler = StandardScaler()\n",
        "rob_scaler = RobustScaler()\n",
        "\n",
        "df['scaled_amount'] = rob_scaler.fit_transform(df['Amount'].values.reshape(-1,1))\n",
        "df['scaled_time'] = rob_scaler.fit_transform(df['Time'].values.reshape(-1,1))\n",
        "\n",
        "df.drop(['Time','Amount'], axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VnEMEMTQZp9R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "7986f10b-96d0-4419-a5ac-6c3873586b06"
      },
      "source": [
        "scaled_amount = df['scaled_amount']\n",
        "scaled_time = df['scaled_time']\n",
        "\n",
        "df.drop(['scaled_amount', 'scaled_time'], axis=1, inplace=True)\n",
        "df.insert(0, 'scaled_amount', scaled_amount)\n",
        "df.insert(1, 'scaled_time', scaled_time)\n",
        "\n",
        "# Amount and Time are Scaled!\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>scaled_amount</th>\n",
              "      <th>scaled_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>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</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>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.783274</td>\n",
              "      <td>-0.994983</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>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.269825</td>\n",
              "      <td>-0.994983</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>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.983721</td>\n",
              "      <td>-0.994972</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>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.418291</td>\n",
              "      <td>-0.994972</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>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.670579</td>\n",
              "      <td>-0.994960</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>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</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>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   scaled_amount  scaled_time        V1  ...       V27       V28  Class\n",
              "0       1.783274    -0.994983 -1.359807  ...  0.133558 -0.021053      0\n",
              "1      -0.269825    -0.994983  1.191857  ... -0.008983  0.014724      0\n",
              "2       4.983721    -0.994972 -1.358354  ... -0.055353 -0.059752      0\n",
              "3       1.418291    -0.994972 -0.966272  ...  0.062723  0.061458      0\n",
              "4       0.670579    -0.994960 -1.158233  ...  0.219422  0.215153      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UZNPk_PGVRSx",
        "colab_type": "text"
      },
      "source": [
        "데이터 불균형(Data Imbalance)를 해결하기 위하여, 정상 거래건에 대해 Down sampling을 70% 비율로 진행\n",
        "Splitting the Data (Original DataFrame)\n",
        "\n",
        "Before proceeding with the Random UnderSampling technique we have to separate the orginal dataframe. Why? for testing purposes, remember although we are splitting the data when implementing Random UnderSampling or OverSampling techniques, we want to test our models on the original testing set not on the testing set created by either of these techniques. The main goal is to fit the model either with the dataframes that were undersample and oversample (in order for our models to detect the patterns), and test it on the original testing set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_r4XB6-VTeZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "df43d7c9-758c-4e50-b828-c0bec7216b86"
      },
      "source": [
        "# Since our classes are highly skewed we should make them equivalent in order to have a normal distribution of the classes.\n",
        "\n",
        "# Lets shuffle the data before creating the subsamples\n",
        "\n",
        "df = df.sample(frac=1)\n",
        "\n",
        "# amount of fraud classes 492 rows.\n",
        "fraud_df = df.loc[df['Class'] == 1]\n",
        "non_fraud_df = df.loc[df['Class'] == 0][:492]\n",
        "\n",
        "normal_distributed_df = pd.concat([fraud_df, non_fraud_df])\n",
        "\n",
        "# Shuffle dataframe rows\n",
        "new_df = normal_distributed_df.sample(frac=1, random_state=42)\n",
        "\n",
        "new_df.head()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>scaled_amount</th>\n",
              "      <th>scaled_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>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</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>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>216879</th>\n",
              "      <td>-0.097813</td>\n",
              "      <td>0.657679</td>\n",
              "      <td>2.223776</td>\n",
              "      <td>-0.598029</td>\n",
              "      <td>-1.285536</td>\n",
              "      <td>-0.874812</td>\n",
              "      <td>-0.532352</td>\n",
              "      <td>-1.421096</td>\n",
              "      <td>-0.223833</td>\n",
              "      <td>-0.501003</td>\n",
              "      <td>-0.704840</td>\n",
              "      <td>0.846708</td>\n",
              "      <td>-0.422143</td>\n",
              "      <td>-0.013066</td>\n",
              "      <td>1.333787</td>\n",
              "      <td>-0.195800</td>\n",
              "      <td>0.200274</td>\n",
              "      <td>0.824426</td>\n",
              "      <td>0.087477</td>\n",
              "      <td>-1.446200</td>\n",
              "      <td>0.411599</td>\n",
              "      <td>0.040097</td>\n",
              "      <td>0.447135</td>\n",
              "      <td>1.368247</td>\n",
              "      <td>-0.026734</td>\n",
              "      <td>0.139097</td>\n",
              "      <td>0.240900</td>\n",
              "      <td>0.062119</td>\n",
              "      <td>-0.031761</td>\n",
              "      <td>-0.062858</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105178</th>\n",
              "      <td>-0.293440</td>\n",
              "      <td>-0.179725</td>\n",
              "      <td>1.140431</td>\n",
              "      <td>1.134243</td>\n",
              "      <td>-1.429455</td>\n",
              "      <td>2.012226</td>\n",
              "      <td>0.622800</td>\n",
              "      <td>-1.152923</td>\n",
              "      <td>0.221159</td>\n",
              "      <td>0.037372</td>\n",
              "      <td>0.034486</td>\n",
              "      <td>-1.879644</td>\n",
              "      <td>2.191665</td>\n",
              "      <td>-0.487850</td>\n",
              "      <td>-1.757137</td>\n",
              "      <td>-4.461051</td>\n",
              "      <td>-0.402884</td>\n",
              "      <td>1.410917</td>\n",
              "      <td>3.619251</td>\n",
              "      <td>2.284927</td>\n",
              "      <td>-0.443999</td>\n",
              "      <td>-0.099712</td>\n",
              "      <td>-0.367136</td>\n",
              "      <td>-0.891627</td>\n",
              "      <td>-0.160578</td>\n",
              "      <td>-0.108326</td>\n",
              "      <td>0.668374</td>\n",
              "      <td>-0.352393</td>\n",
              "      <td>0.071993</td>\n",
              "      <td>0.113684</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171682</th>\n",
              "      <td>0.302662</td>\n",
              "      <td>0.423807</td>\n",
              "      <td>-0.279658</td>\n",
              "      <td>0.949134</td>\n",
              "      <td>-0.436225</td>\n",
              "      <td>0.803801</td>\n",
              "      <td>1.598680</td>\n",
              "      <td>-0.328978</td>\n",
              "      <td>1.389757</td>\n",
              "      <td>-0.243992</td>\n",
              "      <td>-0.660532</td>\n",
              "      <td>-0.021783</td>\n",
              "      <td>-1.937213</td>\n",
              "      <td>-0.160876</td>\n",
              "      <td>0.449054</td>\n",
              "      <td>0.339821</td>\n",
              "      <td>-0.287138</td>\n",
              "      <td>-0.932635</td>\n",
              "      <td>-0.213639</td>\n",
              "      <td>-0.067121</td>\n",
              "      <td>1.139140</td>\n",
              "      <td>0.263281</td>\n",
              "      <td>0.092114</td>\n",
              "      <td>0.497845</td>\n",
              "      <td>-0.297632</td>\n",
              "      <td>-1.036467</td>\n",
              "      <td>0.208580</td>\n",
              "      <td>-0.312834</td>\n",
              "      <td>0.421134</td>\n",
              "      <td>0.241179</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41943</th>\n",
              "      <td>-0.296793</td>\n",
              "      <td>-0.514574</td>\n",
              "      <td>-2.140511</td>\n",
              "      <td>4.104871</td>\n",
              "      <td>-8.996859</td>\n",
              "      <td>4.028391</td>\n",
              "      <td>-5.131359</td>\n",
              "      <td>-4.153568</td>\n",
              "      <td>-9.360095</td>\n",
              "      <td>1.922075</td>\n",
              "      <td>-4.026180</td>\n",
              "      <td>-13.691315</td>\n",
              "      <td>9.095288</td>\n",
              "      <td>-14.168121</td>\n",
              "      <td>-0.140455</td>\n",
              "      <td>-17.620634</td>\n",
              "      <td>1.482740</td>\n",
              "      <td>-13.256833</td>\n",
              "      <td>-21.675484</td>\n",
              "      <td>-7.823469</td>\n",
              "      <td>2.995111</td>\n",
              "      <td>1.725578</td>\n",
              "      <td>1.854267</td>\n",
              "      <td>-0.165534</td>\n",
              "      <td>-0.339939</td>\n",
              "      <td>0.296314</td>\n",
              "      <td>1.364225</td>\n",
              "      <td>-0.518996</td>\n",
              "      <td>2.352333</td>\n",
              "      <td>1.130625</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178208</th>\n",
              "      <td>0.328513</td>\n",
              "      <td>0.456220</td>\n",
              "      <td>-5.904921</td>\n",
              "      <td>4.439911</td>\n",
              "      <td>-8.631802</td>\n",
              "      <td>7.788684</td>\n",
              "      <td>-4.989580</td>\n",
              "      <td>-1.200144</td>\n",
              "      <td>-7.674060</td>\n",
              "      <td>4.125761</td>\n",
              "      <td>-5.315778</td>\n",
              "      <td>-4.891156</td>\n",
              "      <td>4.220419</td>\n",
              "      <td>-6.408301</td>\n",
              "      <td>1.328003</td>\n",
              "      <td>-5.853545</td>\n",
              "      <td>-0.928337</td>\n",
              "      <td>-7.085798</td>\n",
              "      <td>-12.623856</td>\n",
              "      <td>-4.745224</td>\n",
              "      <td>2.797892</td>\n",
              "      <td>-0.505608</td>\n",
              "      <td>1.775030</td>\n",
              "      <td>1.266441</td>\n",
              "      <td>-0.199410</td>\n",
              "      <td>0.014960</td>\n",
              "      <td>-0.000019</td>\n",
              "      <td>0.544210</td>\n",
              "      <td>-1.167566</td>\n",
              "      <td>-1.206354</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        scaled_amount  scaled_time        V1  ...       V27       V28  Class\n",
              "216879      -0.097813     0.657679  2.223776  ... -0.031761 -0.062858      0\n",
              "105178      -0.293440    -0.179725  1.140431  ...  0.071993  0.113684      1\n",
              "171682       0.302662     0.423807 -0.279658  ...  0.421134  0.241179      0\n",
              "41943       -0.296793    -0.514574 -2.140511  ...  2.352333  1.130625      1\n",
              "178208       0.328513     0.456220 -5.904921  ... -1.167566 -1.206354      1\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dEHGa-rbWCwC",
        "colab_type": "text"
      },
      "source": [
        "Isolation Forest는 이상치 점수(outlier score)를 제공합니다. 정상 거래/ 부정 거래에 대한 이상치 점수는 아래와 같습니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CHkym7XWEeD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "baa21cea-3fe0-48fa-b380-4ca051aa5ecd"
      },
      "source": [
        "print('Distribution of the Classes in the subsample dataset')\n",
        "print(new_df['Class'].value_counts()/len(new_df))\n",
        "\n",
        "\n",
        "\n",
        "sns.countplot('Class', data=new_df, palette=colors)\n",
        "plt.title('Equally Distributed Classes', fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Distribution of the Classes in the subsample dataset\n",
            "1    0.5\n",
            "0    0.5\n",
            "Name: Class, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnyWOM3SWG7q",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   n_estimators : 노드 수\n",
        "*   contamination : 이상치 비율\n",
        "\n",
        "이상탐지 예측값은 1이 정상, -1이 이상으로 분류됩니다. 이를 Class 라벨과의 오차를 계산하여야 하기 때문에, 같은 범위로 바꿔주었습니다.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EW1Sb-rPWT7A",
        "colab_type": "text"
      },
      "source": [
        "**시각화**\n",
        "\n",
        "이상탐지 결과를 2d 및 3d로 시각화한 결과 입니다. ( 시각화를 위해 차원을 축소하였습니다.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQLFXn0-WXK_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "59024dec-07a0-4422-84ae-4f6fe38fb898"
      },
      "source": [
        "# Make sure we use the subsample in our correlation\n",
        "\n",
        "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(24,20))\n",
        "\n",
        "# Entire DataFrame\n",
        "corr = df.corr()\n",
        "sns.heatmap(corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax1)\n",
        "ax1.set_title(\"Imbalanced Correlation Matrix \\n (don't use for reference)\", fontsize=14)\n",
        "\n",
        "\n",
        "sub_sample_corr = new_df.corr()\n",
        "sns.heatmap(sub_sample_corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax2)\n",
        "ax2.set_title('SubSample Correlation Matrix \\n (use for reference)', fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JOIEky5Ic2De2MMmxSc5MckuS07vKJ0mSJEmSJA2LnYgTWwzMB3pXcp4PvB3Y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P2SbAPMBx40wbGeBmw1NN/hWsADgZ2AQ6pqPnBZkmOXIO9fquqsNtMfgGOqqpKcBWw6lOOfkryz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\nAP+3m+P5Lle+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1728x1440 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_XORD3FaqRp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "3d3c197f-6f0b-40e5-c8f0-140baa3f2c64"
      },
      "source": [
        "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
        "\n",
        "# Negative Correlations with our Class (The lower our feature value the more likely it will be a fraud transaction)\n",
        "sns.boxplot(x=\"Class\", y=\"V17\", data=new_df, palette=colors, ax=axes[0])\n",
        "axes[0].set_title('V17 vs Class Negative Correlation')\n",
        "\n",
        "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df, palette=colors, ax=axes[1])\n",
        "axes[1].set_title('V14 vs Class Negative Correlation')\n",
        "\n",
        "\n",
        "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, palette=colors, ax=axes[2])\n",
        "axes[2].set_title('V12 vs Class Negative Correlation')\n",
        "\n",
        "\n",
        "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, palette=colors, ax=axes[3])\n",
        "axes[3].set_title('V10 vs Class Negative Correlation')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/7rhWTsgc0vKfgya/chy3F1YdRzN6xStewcqVK3eVX/nKV1YYTftqRMeOjRs3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\n",
            "text/plain": [
              "<Figure size 1440x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDJiXh7-aved",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "a75d8120-6c53-4b8a-dc54-c68d6ed162eb"
      },
      "source": [
        "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
        "\n",
        "# Positive correlations (The higher the feature the probability increases that it will be a fraud transaction)\n",
        "sns.boxplot(x=\"Class\", y=\"V11\", data=new_df, palette=colors, ax=axes[0])\n",
        "axes[0].set_title('V11 vs Class Positive Correlation')\n",
        "\n",
        "sns.boxplot(x=\"Class\", y=\"V4\", data=new_df, palette=colors, ax=axes[1])\n",
        "axes[1].set_title('V4 vs Class Positive Correlation')\n",
        "\n",
        "\n",
        "sns.boxplot(x=\"Class\", y=\"V2\", data=new_df, palette=colors, ax=axes[2])\n",
        "axes[2].set_title('V2 vs Class Positive Correlation')\n",
        "\n",
        "\n",
        "sns.boxplot(x=\"Class\", y=\"V19\", data=new_df, palette=colors, ax=axes[3])\n",
        "axes[3].set_title('V19 vs Class Positive Correlation')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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17n5s8rjb3Xvcvdndj3L3Oe6uXrNUlLa2NgYGBgAYGBjQqCMRkRKixJFIJLff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\nQeMIAAAAAMDejwDaYqkaADQ1Zx0BAAAUFns/Alg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            "text/plain": [
              "<Figure size 1440x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZPK2A17a1gP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "38964463-36d3-4285-c75d-c690a4edd5bc"
      },
      "source": [
        "from scipy.stats import norm\n",
        "\n",
        "f, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(20, 6))\n",
        "\n",
        "v14_fraud_dist = new_df['V14'].loc[new_df['Class'] == 1].values\n",
        "sns.distplot(v14_fraud_dist,ax=ax1, fit=norm, color='#FB8861')\n",
        "ax1.set_title('V14 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
        "\n",
        "v12_fraud_dist = new_df['V12'].loc[new_df['Class'] == 1].values\n",
        "sns.distplot(v12_fraud_dist,ax=ax2, fit=norm, color='#56F9BB')\n",
        "ax2.set_title('V12 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
        "\n",
        "\n",
        "v10_fraud_dist = new_df['V10'].loc[new_df['Class'] == 1].values\n",
        "sns.distplot(v10_fraud_dist,ax=ax3, fit=norm, color='#C5B3F9')\n",
        "ax3.set_title('V10 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Oj+mnCMiLafNDYHXk5wygCvjEKeKfC/x3S//71UsvvdL7Uo5IeLxyhHKEXnrpFXkpTyQ8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YVkiLjUhHFTOZV2jGPHj2CxgmGRFUbeIgkjUSetTdfuaDGtUUGhbs9ZuW03gQEBDO/tfvc00bQN\n7dUDuy2AFVcoS1NKQYsEyD2HNq+wEkkI0WzsWrYO0+Gkvwd2R/vKeZI8KhlqxMvOaM1cVMtYug7q\ny9aFyy+UoEXh6o8lfYyEEDfq5MmT7Nu3z+vNrsG1wigwGK+WvTVmo0ZMwmq1snzVQtdOaXZZYeQt\nkjASdcs5BxVlqDZd3A5rrVm5fQ/Devcg2B7o4+BEQxAUaGN4nyRWbd+DaZp1nqdiE8BZDfnZPoxO\nCNFQbV24nFZdEkno1vGG5jliFjDfPMlwoyUtlTR6EDBw2nhyz2RwfNcBAOzKih2LrDASQtywhQtd\nyYkpU6Z49TqmqamsgMAgSRbVJSI8kkEDRrJy9WKcTqdrp7RKpF+dF0jCSNRJp6dCQCDEtXE7fuB4\nGhm5+YzrL+Vozdn4/n3Iyi9k//FTdZ8U3QqUQudKHyMhmrtzx05xcs8hBt826YaenBbrKt507CMW\nO49YunswQtGY9R4/HFuQnS1ffXPhWJTslCaEuEGmafLVV18xZMgQWrZs6dVrVZUDWvoXXc2EsVPJ\nzctm154t2OygTVezcOFZkjASbunqKsg+Da06ogz3/SVWbNuFxTAY3S/Zx9GJhmRUv2SsFgvfbNlZ\n5znKGgCRsZCb4cPIhBAN0ZYvl2FYLQy4gXI0U2v+7jhIIVX80ppMsJJNF4RLYHAQfSaOYNeytVRV\nuFYVRcpOaUKIG7Rjxw7S09O57bbbvH6tinLXzyrZIe3KhgwaRUhIGMtXff39Tmnlfg6qCZKEkXAv\nKw1ME9Uq0e2w1pqlm3cyqGc3IsPc9zcSzUN4SDBDk29i2eadV/xjXMW0hqJctHSkE6LJq9BO0nUp\nB8189pt57DPzOGYWkVVVwpYFy+k5cjBhMVHXNbdDm/zFuZ+dOocHLV3paIR7OHrR2A2cNoGKkjL2\nrd4EQBSBVGFSgKwyEkJcn/nz5xMcHMy4cTfee+9qKsvAGgDWAFlhdCU2WyCjR0xk/YYVOHUZIH2M\nvEESRsItfe4EBIVCRAu34wdPpHE6M5vJg1N8HJloiCYPSeFsTh77jp2s+6SYVq73eZk+iUkI4VuF\nuopFzlPMc57gQ/M7Fpun2aAz2aSz2KyzWKXP8sG6xZTkFRAwPYV9Zh7F11gmVKmdvO7Yy0Yzk/st\nnZlkaeulf41ozLoM6E1Uy1i2LnCVpUUqV+PrM7rUn2EJIRqp8vJyli1bxuTJkwkO9v6yn4oyTaCU\no9XL+LFTKS8vY/PWtVhtUCXt6jxO1nCLWnRlOeRlQIeedfaXWLJpB1aLwbgBfXwcnWiIxqT0IsBq\nZemmHfTqXMc2o+ExYA1A5571bXBCCK8q0dXMd55kqXmaakxisTNAxRKr7ERiw1LzbKqUaj6Y/wH2\nFhEED+/O5ppEUgvsdFBhdFRhdV5Da80mM5OPnankUsGPLd2ZYHHfX08IwzAYOH0i37z9Kfnnsohq\nGQ3AGV1CMtF+jk4I0disXLmS0tJSpk+f7vVrOao1zmqwSzlavfTqmUJcbEuWr1pI0o8nyQojL5CE\nkagt8xRojWqZ6HZYa82yzTsYknwTkaEhvo1NNEjhIcEM6+UqS3v6/tsxjNqLF5VhoKNbQu45P0Qo\nhPA0rTUrzHQ+caZSjoORRituNdpzRBe4Pb8qp5CT63cw9qE7mR7YhSJdxQldzAldzDadzTadzbqq\nDLoaEbRXoQRiQaE4ros4aOZzljLaq1Aet/QjyZCbfnFlQ26fzDezPmHTl8uY8tgDBGKQLiuMhBDX\nYf78+bRu3Zr+/ft7/VoVrsoqWWFUT4ZhMH7MLcye+x5l/5EHldGYpsYw5P/PU6QkTdSiz52A0EhU\nmPv+EvuOneRsTp6Uo4lLTB6cQkZePntST9R5jopuBeUl6IIsH0YmhPC0XF3B7x27+JfzMB1VGH+0\nDuZxaxJtjbp72m1dsALTaTLotkkAhCsbvY0YbrMkcq/RiUEqjgQVzC4zhw+c3/G28zCznIfYYGYQ\np4L4maUHr1gHSbJI1EtMQku6DenH5i+WoE2TSAKlJE0Icc0yMzPZtGkT06dPd/tA1NMqyzRKQaDd\n65dqMiaOn4ZpOtmwdREA1VKW5lGywkhcQpeXQkE2qnPdpWZLN+0gwGplbP/ePoxMNHRjUnphC3CV\npfXt2sn9STV9jPTJg6g+cT6MTojmZYXzzHV/7firlHrtNXP5s2M/VTj5kaUbE4w2GHWUL5+ntWbj\nvMV06NODlh3b1RoPUwH0UtGMt7RBa00J1VRj4kQTg/2q8wvhztA7b+bfT/2WQxu2EzW8nSSMhBDX\nbOHChZimybRp03xyvcoyjS0IlKyQqbf27TrRo3tvVqz9kqG9HqCqQhMYJP9/niIrjMSlstJc7+so\nRzNNk2WbdzKs102Eh0hxrfheaHAQI3on8c2WnZim6f6kkHAIDEafPOjb4IQQN0xrzVfOk/zesYsI\nZeOVgEFMsrStVzLn6NbdZJ9KZ9hdU696rlKKMGUjWtmJVUGSLBLXLXnMEEKjI9k4dwmRBFJMNUXX\n2GhdCNF8VJZrKkq/74GjtWb+/Pn07duXxMREr1/fNDWV5WCXZMc1u3nSDNJOH+PE6X3IhsyeJQkj\ncQmdmQYhEagQ99sU70k9QUZevpSjCbcmDU4hK7+QnUeOuR1XSkFMK/SpQ2hdR1JJCNHgOLTJLOch\nPnGmMsiI42XrAFqr+vew2/DZIoLDw+g7aaQXoxTiUtaAAAZNn8j+tZsIyHGtLpJVRkKIy1WWa86d\ncJKearJzdTW71laTmebkwIEDHDt2zCfNrgHKijRaQ2CIJIyu1eiRk7EHBrFhx3xpfO1hkjASF+iq\nCsjPgvja5QLnLdm4HVuAlTEpvXwYmWgsRqckY7cFsHjjtjrPUTGtoKIUMk/7MDIhxPWq0A7+5NjD\nKvMsM4xEfmlJxq7qX9FelJPPnpXfMui2idjsgV6MVIjaht4xBdNpkvrVWkASRkKIS5UVadJTTSrL\nIbqlIrGHBYsFju118ulHX2Kz2ZgyZYpPYinOdyU67EE+uVyTEhISyqgRE9mycynFhWX+DqdJkYSR\n+F7WaUCj4twnjKodTpZs2s7ofr0IDZafZKK2ELudsf17s2TTDqocDvcnXehjdMCHkQkhrkeJruZ3\njl3s1Xn8xHIT91o7u1YKXoPNXy7FdDgZdtctXopSiLrFJbahy4DebJu7FLtTcUaX+DskIUQD4ajS\nZJ0xsdmhbTeDyFiD1h0t9BxqJSiiimXLFzFi2FjCw91XXnhacb7GYgVLgE8u1+TcPPl2yitK2bpr\nJU6HrDLyFEkYiQt01mmwh0C4+x1oNuw9SF5RCbcOH+jjyERjMm3EIIpKy1i/a7/bcRUYBC0SpI+R\nEA1cka7it44dnNBF/Kc1mXGWhGuewzRNNsxdTJeBfYjv0NYLUQpxdSPuvZW89EwsG46RLiuMhBC4\n+hNlnjbRGuLbGVgs3z8MMQzFmfy1lJYV0rfbbVRV+ib5UFxgEhjMNT+YES7JSf1o3bId67d9KX2M\nPEgSRgIA7aiG3LMQ167OH1IL128hMjSE4X2SfBydaEyGJN9ETHgYC7/dWuc5KjEJfeYoulqajwrR\nEBXpKl507OCsLuMZax8GGte3q+HhjTvIS8+Q1UXCr3qNHUZEXAzps1dLSZoQAoCCbE1lGcQmKAIC\na9/7fPHlXFq3TqBbx0GcOer0ejxVla547MGSLLpeSikmT5zB0eM7OHnypL/DaTIkYSRcctLBNFHx\n7p8AF5eVs3rHXqYM7Y/NWv/eFaL5sVosTBnanzU791FY4v4Pc5XYA5wOdPp3vg1OCHFVxbqK3zp2\nkqXL+ZW1D32MmOuea/3sBYRGR9J7/DAPRijEtbEEWBl21y2kbdhN5qkzlOhqf4ckhPAjp1NTmK0J\nDoPQyNq3w2lpaWzevJk777yDVolWMtNMyku8u8qo5Hz/IkkY3ZDJE6ehlMHyVV/5O5QmQxJGAqjZ\nHS0gEKLcP0VevmUXldXVUo4m6uXW4YOodjj4Zssut+OqbVcwLFKWJkQDU6Wd/M6xi3O6jGesvUky\n3Jco10d22lkOrN3CsLtuwRogDRmEfw2982YsVgu5n60nTfoYCdGsFWZrTBOi4t3fCs+bNw/DMLj9\n9ttp29WCMiDtSB29OT2kKN9EKbBJm9gbEtsint5Jw1iz4SscdfVTFddEEkYCbTpdK4zi2qKU+5fE\ngm+30L5lHL06d/BxdKIxSurYjo6tW7Lw2y1ux5XNDq07oU9JwkiIhsKhTZaZZzitS/gvay963cDK\nIoB1n36FshiMuGeqhyIU4vpFxMYwZuIE8r7awtHSbH+HI4TwE6dDU5irCYlQBAbVXs3jcDj44osv\nGDlyJPHx8dgCFa07GuSe05QUmF6LqyhXExqpMAxZYXSjxo+aQX5hNt9++62/Q2kSJGEkIDcDHNWo\nePe7o53NyWPbwaNMHT5QmrCJelFKMXX4QHYcTiU9O9ftOUZiD8hIQ5fLk96mQCk1WSl1RCmVqpT6\nlZvxQKXUnJrxLUqpxJrjE5RSO5RS+2rej/V17AJMrVllniWDcn5uSaKv0eKG5qsoLWPzl0vpO3Ek\nEXE3NpcQnvLQD+7HLC5n+deL/R2KEMJPCrI12oSoOPf3NGvXriUnJ4e77rrrwrGEThYsVjh3wjsJ\nI6dDU1qoCY+R+yxPGDxoFGEhUXz+2Tx/h9IkSMJIoLPSwGKF6FZuxxfVNC+WcjRxLabWvF6+WrfZ\n7bhK7AFo9KlDvgtKeIVSygL8DZgC9AB+oJTqcdlpjwD5WuvOwBvAqzXHc4BbtdbJwEPAh76JWpyn\ntWajzuQUJQxV8QyztLzhObd89Q0VJWWMfmCGByIUwjNSUlKI6taO7Z8sQmvZclmI5sZ0aoryXKuL\nbHb3yZnPP/+c2NhYRo4ceeGYxaqIa2OQc9akqsLzPzuKCzRaQ3i03Jp7QkiojWEDprN27WoyMjL8\nHU6jJ6/KZk5rE7JOQ2wCymJxM66Zt2YjKd070zY+1g8RisYqITaGwT27MX/tJkzTzROZVh3AFoQ+\necD3wQlPGwikaq2Pa62rgNnA9MvOmQ68X/PxXGCcUkpprXdprc/WHD8ABCmlAn0StQBgv87nkC6g\nl4omyYi64flM02Ttx/Npn9ydxF43eSBCITxDKcXQ+6dTlHqG9RukVEGI5qa4wLW6KKKOlTwZGems\nW7eOSeNmkJOuyDjlvPBmsYHWcGyv48IxTynKdf2dHBYlK4w8wRYIowffhalNPv/8c3+H0+hJwqi5\nK8iGqgpUnPtytG2HvuN0ZjZ3jh3u48BEU3DHmGGkZ+ey5cCRWmPKsKDad5fG101DAnD6os/P1Bxz\ne47W2gEUApc3ybkD2Km1rrz8AkqpnyiltiultmdnS/8RTzmli9mss+hAGAOVZx4KHNqwnexT6bK6\nSDRIN0+9BWuLcGa9+29/hyKE8CGtNUW5msAgCAx2f86CxZ+hlOLWW+6qNWYLVASFQlGeRpueXWV0\nftWTNUASRp5gWBStWrWhb/IwPv/8c6qrZWfMGyH7ozdzOvM0KANiL7+3c5m76lvCgoOYMLCvjyMT\nTcG4/n2ICA1h3uoNDEmuvdJAJfZAf7cLnZ+FqmOHPtE8KKWScJWpTXQ3rrWeBcwC6N+/v9SSeEC+\nrmS1eY4W2BlttLrQo26F88wNzbvmg3mEt4imz8QRnghTCI/qHBhDzL0j2fHXrzl69Chdu3b1d0hC\nCB8oL4XqSohto9z2ZK2qqmTxsi8YNngMcbHu23REtDDIOGlSWuRqUO0JplNTkq+Jby/rODwp0A5j\nht7N6/94glWrVjFp0iR/h9RoySuzGdNaQ1YaxLRCWW21xgtKSlm+dRdThw8kKLD2uBBXE2gL4Nbh\nA1mxbQ/5RbWbW6v2rjY3UpbW6KUDbS/6vE3NMbfnKKWsQASQW/N5G+BL4EGt9TGvRyuo0k6Wm+lY\nUUwwErDWsUPmtTp9KJXDm3Yy6oEZWAMCPDKnEJ7UWgUTf9dwrHYb77///tW/QAjRJBTlmBgWCI1w\nn+hZs24ZhYX5TJ96b51zBIWC1eba0cxTSgo1pgnhMXJb7km2IEX3DsNp3bo1s2fP9nc4jZq8Mpuz\nVB5sSQAAIABJREFUrDQoL6lzd7RF326lqtrBHWOG+Tgw0ZTcMWYY1Q4HC7/dUnswuiWERUtZWuO3\nDeiilOqglLIB9wILLjtnAa6m1gB3Aqu01lopFQksAn6ltd7gs4ibMa01a8xzFFHFOCOBUOW5xM6K\nf8/BHhLM8LunemxOITzJqgwSo1rScfpIFi5ciJS4CtH0VZZryoohPFqh6ti2/quv59C2TQf69R1c\n5zxKKcKjFBVlUFXpmaRRUZ5rnvBoKUfzpEC7wjAsTL/1bjZv3szx48f9HVKjVa+EkWyX3DSZR3YA\nCuLa1hrTWjN31QaSOrTjpsTa40LUV9d2CSR3SmTe6g21dqVRSqESe6DTDqPdNcYWjUJNT6KZwDLg\nEPCZ1vqAUuolpdS0mtPeAWKUUqnAU8D53yUzgc7AC0qp3TVvUp/oRft1PqcoYbCKo5Wqo5HDdcg5\nc45dy9Yx7K5bCA4P9di8QnhaexVK+AOjcDgcfPzxx/4ORwjhZZlprgbVYXUkZY5+d5CDh/cwberd\nbsvVLhZa05i6OM8zCaPCHJOgMEWATRJGnmQLcr0fP2oGAQEBssroBlw1YSTbJTdd+uhOiIpD2ey1\nxvYdO8XR0+myukh4xB1jhpF65hx7vjtRa0wl9oCKUsg85fvAhMdorRdrrbtqrTtprV+uOfaC1npB\nzccVWuu7tNadtdYDtdbHa47/TmsdorXuc9Fblj//LU1Zti5nq86iPaEkqRvfEe1iqz+Yh2EYjP6P\n2z06rxCelqjCqGoXychxY5g9ezYlJbVLpoUQTYM2NVmnTYJCqTMpM3/hp9gDg5g8/vINXmuzBiiC\nw6E4X2PeYPNrp0NTlKeJjJVkkadZrK7yQZsRzeTJk/niiy/kZ/11qs8KI9kuuQnSuecg92yd5Wif\nr1xPUKCNm4cN8HFkoim6eWh/gu2BzFmxrtaY9DESwjeqtJOV5lmCsTLyoibXnlCcV8CmL5YyYOo4\nIuNbeGxeIbyhvXKtgJvw6H0UFhby6aef+jkiIYS35GdrqiogPNr9bW9BQR4rVi9i/NiphIaG12vO\n8CgD0wl5GTeWMCrM1WgTomKlS4ynKaUICVeUFmkefPBBSktLmTdvnr/DapTq8+qU7ZKbIH10p+sD\nNwmjguISFm3YxtThAwkLDvJxZKIpCgmyM33kYJZs2kFuYdElYyokHOLaSh8jIbxsk86ihGrGGK2x\nK4tH5173yVdUV1Qy7uHaWxEL0dC0V2EAWJISGDZsGO+//z7l5eV+jkoI4Q2ZaU4CAiG4jlzQV4vm\nUF1dxZ0zHqj3nEFhYAmArNPOG4qtIMvViFv6F3lHSLiirFiT1COJlJQUPvroI5zOG/ueNUc+SWde\ntF3yT92Na61naa37a637x8bG+iKkZs88uh1adUTZQ2qNzVu9kcrqau6bONr3gYkm676Jo6l2OJi3\nunZfY5WYhE5PRVfVyicLITzgpC7mqC6kj4qhpQf7FgGUF5ey9pP59Bo7jJad2nt0biG8IVQFEEcQ\nx80ifvazn5Gbm8vcuXP9HZYQwsMqyzX5mZq4tobbVbVVVZV8tfBTBg0YQft2neo9r1KKsChFQbam\nouz6VxkVZJtExCgMiySMvCEkXKFNKC91rTI6c+YMK1eu9HdYjU59EkayXXITowtzIOMURreUWmNO\n02T28rUM6NGVru0uX0gmxPXrmNCSIT27M2f5ehwOxyVjKrEHOB3oM0f9E5wQTVi5drDezCCGQPoq\nz5eLrf34S8qLSpj8WP2fzgrhb52McI7rIvr3709KSgrvvPMOVVVV/g5LCOFBWaddG6rEt3W/qnbF\n6kXkF+Rx1+0PuR2/krCa5tfXu8qovERTUQaRcVKO5i3B4a7vUWmhZty4cSQkJPDBBx/4OarGpz6v\nUNkuuYk5X46muvSrNbZmx17O5uRx/6TRPo5KNAf3TRpNRl4+q1atuuS4atMFLFbpYySEF2zQmVRj\nMtpojcWDfYvAtbpo1QfzSB47lLY3dfbo3EJ4U0cVRjYVFOkqHnvsMTIzM5k/f76/wxJCeIjWmqzT\nTiJaKOwhtX/3aa35/IsP6NihK/36DLrm+QNsishYRdZpE30dza8Lsl3JLOlf5D1BoQplQGmRxmKx\n8MADD7Bjxw4OHJD7jWtx1VeobJfc9JhHd0JsG1R0fK2xj5etoWVMFGNSevkhMtHUjeqXTEJsDB99\n9NElx1VAIKpNF+ljJISHndIlnNDF9FUxRHthz4nzq4um/ExWF4nGpZNyNTQ5posYOnQoycnJzJo1\nS1YZCdFEFGRrKsshvp37293tOzdy8lQqd93+0HVvAhHfzkJVhaux9rXKzzKxh+A2mSU8wzAUwWGu\nxtcAd9xxB8HBwbz77rt+jqxxqVdKU7ZLbjp0SSGc+Q6ja+1ytNTTZ9ly4Aj3ThiF1eLZhqhCAFgM\ng3snjGTbtm0cPXpp+ZlK7AHZZ9Clhf4JTogmplqbbDQziMRGL3X5PhQ3rry4lNUffEHymCG07dHF\n4/ML4U0dVTgKV8JIKcXMmTNJT0+XXXSEaCIy05xYbRDd0v3t7mfz3ic6qgVjR0257mtExSsCbJCV\ndm1laY4qTWGuJkrK0bwuNEJRWqjRWhMWFsY999zDkiVLOH369NW/WAA+anotGg6dugvQKDf9iz5c\nuhpbgJU7xwzzfWCi2bh99FCCgoJ47733Ljmu2vcAQJ865IeohGh6dugcSnAwwmjp8VI0gLWfzKes\nqJjJsrpINEJBykprQjhmunbuHDFiBCkpKbz11luyY5oQjVxVhSYvUxPXxsAwav/+O3J0P9t3buSO\n2x7AZrNd93UMQxHb1iAvS1NZXv9VRjnnTLQJsW3kVtzbQiMUjmqoKHN9/sMf/hCLxcI777zj38Aa\nEXmVNjP6yA6IioMWlza0zs4v5Kt1m7lt5BCiwkP9FJ1oDiLDQrnjjjv4+uuvycjI+H4gvj3YQ9An\npK5YiBuVoyvYr/PoriI9visaQEl+ISvf/YzkMUNol9TV4/ML4QudjHCO6SK01iilePLJJ8nOzubT\nTz/1d2hCiBuQddoE7SoZc+fj2W8TGhrO9Kn33vC1WiVaUMDZ4/VfZZR12iQ4TBESLuVo3hYa6fo/\nLilw9YyKi4vjtttu48svvyQ7O9ufoTUakjBqRnRFKTrtMKprSq1a3Q+XrsLpdPLDqeP9FJ1oTn74\nwx9imuYlOxUow0C1vwl98gBaX/8WpUI0d6bWrDczsGNhoIr1yjW+eftTKssquPXJH3l03hXOM9f9\nJsS16qTCKaSKXCoBGDBgAMOGDePtt9+mpKTEz9E1PUqpyUqpI0qpVKXUr9yMByql5tSMb1FKJdYc\nn6CU2qGU2lfzfqyvYxeNhzY1Gadcza6DQmsnZE6cTGX9xpXMmHYfISE3/pA8MEjRIsEgM82kuurq\nf7+WFWtKCjRxbY3r7p0k6i8oTGEYUFLw/ffmkUceweFw8P777/sxssZDEkbNiE7dDaYTo+ulu6MV\nl5UzZ/k6Jg7qR/uW0pNceF9CQgJTpkxhzpw5FBZ+37NIdUyGkgLIlrpiIa7XMvM0OVQwRMUTqDzf\njy43PYP1ny5g0PSJtOqc6PH5hfCVjjWNr4/rogvHfvnLX1JQUCA3Eh6mlLIAfwOmAD2AHyilelx2\n2iNAvta6M/AG8GrN8RzgVq11Mq5dmT/0TdSiMcrL0lRVQMv27n//fTLnbez2IO64zXPl1AmdDEwn\nZJw0r3pu1hknKGiRILfhvmAYipAIdUnCqH379kyePJnZs2dfch8i3JNXajOiD22FsGho1eGS43NW\nrKOkvIJHpk30U2SiOXrkkUcoKytj9uzZF46pjskA6GP7/BWWEI1anq5gtvMYbQihowrzyjUW/fV9\nlKG4+fH/8Mr8QvhKogrFgrrQxwigZ8+eTJgwgX//+99SruBZA4FUrfVxrXUVMBuYftk504Hzmbq5\nwDillNJa79Jan605fgAIUsoL2z6KJiHjpBObHaLja6/eOXvuNKvWLmHaLXcTER7psWsGhxlExSvO\nnXDidNS9ykibmuwzJlFxClugrC7yldDImsbX5vffm0cffZTS0lI++eQTP0bWOEjCqJnQpUXoEwdQ\nPQah1Pff9sqqaj5csoohPbvTo0M7P0Yompvu3bszYsQIPvzwQyorXeUAKjQS4ttjHt/r5+iEaJw+\ncabiRDPMiPfKUvczh4+x/euVjLr/NqJayYpU0bjZlIW2KpRjF60wAnjqqaeorq7mzTff9FNkTVIC\ncPHy4TM1x9yeo7V2AIXA5Vs83gHs1FpXXn4BpdRPlFLblVLbJdnXPJWVaApzNC3bW1Buml1/+tm/\nsVis3H37Dz1+7TadLTiq4Uxq3b2MMtJMqishvq3sRu1LoREK03S9Ps7r3r07Y8aM4f3336e4uNiP\n0TV8kjBqJvThraBNjKQhlxz/av1mcgqK+PH0SX6KTDRnjzzyCLm5uZdsY6w6JkN6Krqi1I+RCdH4\nHDULWW9mMNVoR7i6/l1f6qK15qvXZ2EPC2HCIzfeKFSIhqCLCidVF2Je1DsvMTGR++67jy+++ILD\nhw/7MTpxMaVUEq4ytZ+6G9daz9Ja99da94+N9U7/NtGwZZx0ogyIa1f7FvfsudMs+eZLbp50OzEx\nnn99hEUZxLU1SE81Kc6vXZpWVaFJO+zqrRTlZvWT8J7QSNfr4eKyNICZM2dSWFgoJchXIQmjZsI8\nsBni2qJi21w4VlVVxaz5S0nulMigpG5+jE40VwMHDiQlJYVZs2ZdWGVkdOoFWstuaUJcA1Nr3nce\nIRIbt1kSvXKNfas3cXjTTm5+/EGCI7xT7iaEr3VTkZTjJE1f2uT6scceIzw8nFdffVU2YvCMdKDt\nRZ+3qTnm9hyllBWIAHJrPm8DfAk8qLU+5vVoRaPjqNJknTZp0cpwW+71/sdvYbFYeOAHP/FaDIk9\nLNjskLrHgdN56c+NkwedmCZ07GmVZtc+Zg8BS0DthFGPHj2YMGEC7733Hvn5+X6KruGThFEzoPMy\n4dxxjB6DLzk+d+5czuXkMfOuW+UHl/Apc/cazN1r0HvW8vMpI8jMzGTOGy+7jmelQYANc/vyC+ed\nfxNCuLfBzCBVF/EDS2fsyurx+asrq/jij2/RqnMiI+651ePzC+EvNxlRABzSl94sREREMHPmTDZv\n3szq1av9EVpTsw3oopTqoJSyAfcCCy47ZwGuptYAdwKrtNZaKRUJLAJ+pbXe4LOIRYOXccp54S11\njwPTCbagS49nnHKyed1Rlq9cyIxbf0CLGO+VU1sDFJ17WykvgcPbHJSVaBzVmtNHneScNWnT2eJ2\n5zbhXUopQi9rfH3ezJkzKSsr49133/VDZI2DJIyaAX1wM6BQNw26cKyiooJ//vOf9OvWiWG9bvJf\ncKLZG5TUjQE9ujJr/jLKK6tcPbZaJEBOujzVFaIeKrSTT5ypdFThjDRaeeUaq977nNwzGdzxP49j\nCfB8QkoIf2mh7MQQyGFdUGvsnnvuoWPHjrz66qsXVsGK61PTk2gmsAw4BHymtT6glHpJKTWt5rR3\ngBilVCrwFPCrmuMzgc7AC0qp3TVv0kRNXKBNTWGuJijUtc395d798K8EBQXzg7sf8XoskbEGHZMt\nlBRo9qytZsfKak4fdRIdr0joJLfe/hIaqSgt1rVWfnXt2pWbb76Zjz76iJycHD9F17DJq7aJ09rE\nPLAJ1a4bKjz6wvE5c+aQlZXFL+6eJquLhN/94q6p5BYWMWf5OgBUiwSoqoBC+cEtxNUscJ4kj0oe\nsnTF8MLP8/xzWXzzr9n0mTiCboP6enx+IfytuxHFEbOg1kOKgIAAnnvuOdLS0nj77bf9FF3TobVe\nrLXuqrXupLV+uebYC1rrBTUfV2it79Jad9ZaD9RaH685/jutdYjWus9Fb1n+/LeIhqWkUON0QESL\n2re2R747wLpvl3PXjAeJiIjySTwt21voOyaA+PYGUXEGvYZb6T4gAMMi91z+EhZlgIaS/NoPo3/+\n859TWVnJrFmz/BBZwycJoyZOnzoMBVmoXiMuHCsrK+Ptt99m8ODBDOzR1Y/RCeGS0r0LQ5Nv4l8L\nllFaUQGxCaAUOuv01b9YiGYsR1ewwDzFECOe7obntgi+2Bd/+gfaNJnxtNs+s0I0et1VJPlUkUl5\nrbGhQ4dyyy23MGvWLE6cOOGH6IQQV6K1piBbY7NDUGjtsX+9+ybhYRHcdftD7ifwElugomNPK137\nWS80XRb+Ex7tStYV5dVOGHXo0IEZM2Ywe/ZsTp486ePIGj559TZxevcaCApFdet/4dhHH31Ebm4u\nTzzxhP8CE+Iyv7j7VvKLS3h/0UpUQCBExYMkjIS4oo+d3wHwgKWLV+bfu2oju79Zz6Sf3k9063iv\nXEMIf+uuXMnWI2btsjSAZ599Frvdzm9/+1splRaigSkrgupKiGihalVNbNm2nu07N3L/vT8hJCS0\njhlEc2ANUISEK4pya+9gB/DEE08QEBDA66+/7uPIGj5JGDVhuqQQ/d0uVM9hKGsAADk5OcyaNYsx\nY8bQt6+UFoiGo1fnDkwY2Jd/L/yGrLwCVHw7KC1ElxT6OzQhGqTDZgEbzUymGe1poewen7+8uJTP\nfvdnWnftyPiH7/b4/EI0FG1UCCFYOeSmjxFAbGwsTz75JJs2bWLx4sU+jk4IURetNflZJlabq0fN\nxRyOav4+60+0SWjPjGn3+SlC0ZCERyuK8zWmWTvxHxcXx49//GNWrFjB1q1b/RBdwyUJoyZM71sP\nphOjz6gLx/785z9TWVnJM88848fIhHDvv+6bgcNp8uZnCyCuZvfdrDT/BiVEA2RqzfvOo0QRyDRL\noleuseD/vUNRTj4/ePE/pdG1aNIMpeimIutcYQRw7733kpyczO9//3vy8vJ8GJ0Qoi5lxa6Wl1Fx\ntVcXffX1HE6fOcFjjz5DQECAnyIUDUl4jIFpQmmh+5WiDz/8MK1ateKPf/wjpul+JVJzJAmjJkqb\nJubutaj2N6GiWwJw+PBh5s6dy/3330+HDh38HKEQtbWNj+WByWP4at1mDp3LhfAY6WMkhBvrzXMc\n10XcZ+mMXVk8Pv+xnfv5ds5CRj8wg8Tk7h6fX4iGprsRyVnKKNRVbsctFgu/+93vKC4u5qWXXpLS\nNCH8TGtNfqb71UWFRQW8/9HfSek7mCGDRtUxg2huwmr6GBXmuv/5bbfb+c///E8OHDjAggULfBla\ngyYJoyZKp+6GolxUn9Guz7XmlVdeISIigscee8y/wQlxBT+dMYXI0BD++OFc1yqjwhx0RZm/wxKi\nwajQDj51ptJZhTPcaOnx+SvLyvn4+T8R3TqeW2b6tkmoEP6SpFy7J+0361491LVrV2bOnMmyZctY\nsmSJr0ITQriRl6ldq4tia68uev+jv1NaVsLPf/qsV3eDzjjlrNebaBhsgYqgUCjKq3v10C233EKv\nXr14/fXXKSoq8mF0DZckjJogrTXm5kUQGYvq2g+AVatWsWXLFmbOnElERISfIxSibmHBQcy8ayrb\nDn3H8jM1/YukLE2IC+Y7T5JPFQ9ZumF44Q/hL/74D3JOn+M/fv/fBAYHeXx+IRqijiqcUALYrXOv\neN6PfvQjevXqxUsvvUR2draPohNCXEybmrTDDgICITTq0t+DR47u56uvZ3PrzXfTIdE7G0KIxis8\n2qA4T9e5StQwDF544QXy8vJ44403fBxdwyQJoyZInzoE505gDJqCMiyUlpby8ssv07lzZ+655x5/\nhyfEVd05djjd2rfhlTmLKLYGozNO+jskIRqELF3O12Yaw4yWdDU8n/zft3ojG+cuZtyP7qZz/14e\nn1+IhspQil5GNHvN3CuWm1mtVv7whz9QXl7OCy+8IKVpQvhB5mmT8hKIjjcuWUHkdDp47c3fEBUZ\nw48fftKPEYqGKjxa4XRAaVHdP7uTkpK4//77mTNnDnv27PFhdA2TJIyaIL15EYRGoHoOA+DNN98k\nIyODl156CatVGpeKhs9qsfDio/eTXVDEX/ZlQn4WurzE32EJ4XcfO79DAfdZOnt87qKcPD554f9o\n072TlKKJZqm3iqGAKk7pK/++6dixI08//TRr1qzhww8/9FF0QggAp0Nz+qiTsChFcPilY/Pmf0Tq\nscP84vFfExoS5p8ARYMW0cKV/ijIvnKy/4knniA2NpYXX3wRh8Phi9AaLEkYNTE6/Rj61CGMAZNR\n1gD27t3LRx99xL333kvfvn39HZ4Q9ZbcKZH7J41m9tYD7M4qhnMn/R2SEH51yMxns5nFNEsiLZTd\no3ObTicf/vqPVJaV8+Cr/4NVdpQRzVAvIwaAPVcpSwN44IEHGDt2LK+99hr79+/3dmhCiBpnj5tU\nV0L7myyXrC7KyEjn3Q/+xtBBoxk5bLwfIxQNmc2uCAlXFGRdeRe00NBQnnvuOQ4dOtTsHwxIwqgJ\n0VpjrpsH9hBUn1FUV1fzwgsvEBcXx1NPPeXv8IS4Zk/cPY34qEhe3HKKqjPH/B2OEH7j1CbvOo8Q\nQyDTjPYen3/pPz7m8MYd3PGrx2nVyfPzC9EYRKtA2qlQ9phXTxgppXj55Zdp0aIFTz31FMXFxT6I\nUIjmrbJck57qJLqlIjz6+9tYrTX/95eXUIbiyZnPebXRtWj8ouIURfkaR9WVVxlNmDCBMWPG8Oab\nb3L8+HEfRdfwSMKoCdFHd6DTDmOMmIGy2XnnnXc4cuQIzz//PKGhof4OT4hrFhJk57mH7yE1t5h/\nbdqPzj7j75CE8ItvzDOc0iU8aO1KoLJ4dO4D67ey9B8fMei2iQy982aPzi1EY9NbxXBYF1Chr16C\nEBkZyWuvvcbZs2f53//9X+lnJISXnTzk2nEsscelLTbmL5zNth0b+MmPniIutpU/QhONSFS8ARoK\nsq+8ykgpxYsvvkhQUBDPPvss1dXVPoqwYZGEUROhq6swV82B2DaoPqPYt28ff/vb35g8eTLjx8uy\nTNF4je3fm6lDUvjnnjPs+nquv8MRwucKdCWfOY+TrKIZpOI8OnduegYfPPsKrbt24O7nfiFPZUWz\n19uIwYnmgM6v1/n9+vXjqaeeYtmyZfzzn//0cnRCNF+FuSa5Z00SOluwB3//u+pU2jH+8a/XGDRg\nBNOnyuY+4upCIxVWG+RnXT3JHxsby29+8xv279/PrFmzfBBdwyMJoyZCb10KRbkY4++jrLyCZ555\n5sILXIjG7vkf3098WDDP/uNDSmTZv2hmPnGmUomTh63dPJrQqSgt4+0n/j+0NnnkjRewBXm2L5IQ\njVF3FYkdCzvMnHp/zcMPP8ytt97Km2++ycqVK70YnRDNk2lqTux3EhgErTt9f/taXV3Ny3/8H+z2\nIJ755Uvy0EPUi1KKqFiD/CyzXitDJ02axK233spbb73VLHvWScKoCdCZaZibvkZ164/RrjuvvPIK\naWlpvPLKK0REeH7bZSF8LSw4iFcemsHZ4jJ+/+v/9nc4QvjECucZPnZ8x1rzHD1VFIfMfFY4z9Tr\n7WqcDifvPv0y51JP8vBrzxHbLsEH/yIhGr4AZZBitGCrmYVTX7lc4TylFC+99BLJycn893//N0eP\nHvVylEI0L2ePm5QVazr0tGKxfJ8Ueu+jv/Fd6kGefvI3xMTE+jFC0dhExSkc1VBSUL9S4ueff57Y\n2Fj+67/+q9n1rJOEUSOnK8txLngLgkIxJj7AsmXLmDt3Lo8++igDBw70d3hCeEy/wUN5pF9Hvlyx\nhkWLFvk7HCG8ztSaDWYGIVjpq1p4bF6tNfNe/TsH12/lrud+wU3DBnhsbiGagiFGPMVU17ssDcBu\nt/OXv/yFkJAQfvazn5GZmenFCIVoPspLNKePuhpdR8d/f+u6evVqPpnzL26edDsjZFc0cY0iYw1Q\nkJdZvwcD4eHhvPbaa6Snp/Pcc881q551kjBqxLTWmN98CPlZWG79Kanpmfz617+mV69e/PznP/d3\neEJ4lDIMHn/kh/SNC+N/n3+eI0eO+DskIbzqsC4gl0oGqTgClOd+Xa/+YB7rP13A2IfuZPjdUz02\nrxBNRW8Vgx0Lm8xrS/rEx8fz1ltvUVRUxKOPPkphYaGXIhSiacs45STjlJNzJx0c2uZqNBwaqS4c\n37HpBM888yxdOvfgicd/7edoRWNktSkiWyhy0utXlgaQkpLC008/zfLly3nvvfe8G2ADIgmjRkxv\nXow+uBlj2HSKIloyc+ZMgoOD+fOf/4zNZvN3eEJ4XGC/Mfzf6G6E2QP4xS9+QUFBgb9DEsIrCnUV\n23Q2rQmmowrz2Lwb5y7myz/9kz4TRjD9qR97bF4hmhKbstDfiGWrmY2jnmVp5yUlJfHXv/6VkydP\n8vjjj1NeXu6lKIVo+orzNBWlENNSYQ1wlaKVV5TxwktPYjEMXnr+DQIDpf+euD6xCQaV5VCUV//V\nQg899BATJ07k9ddfZ/v27V6MruGQhFEjZW5dhrluHqrHYMyBU3jmmWc4d+4cb775JvHx8f4OTwiv\nUBEtiE1O4f/GJZGRkcHTTz+N0+n0d1hCeNy7ziM40Aw14j3WxHP74lXMfvH/cdPwATz46q8wLBaP\nzCtEUzTEiKeEavZfQ1naeYMHD+ZPf/oTu3bt4sknn6SystILEQrRtFVXanIzNEGhEBbt+j1omiZ/\neuMFTpxK5bln/0jLltJ/T1y/6JYGhgWyz9T/wYBSipdffpm2bdvyxBNPkJaW5sUIGwZJGDUyWmvM\nzYsxV89BdeuPuvlHvPb666xfv55fP3gXfYwizN1r6v0mRGOj+o6hd5ji+UfuZ8OGDfzhD39oVnXE\nounbZmaxycykr4ohSgV6ZM49Kzfw4f+8SqeUZH78xgsEyCpUIa6ol4omCAubzIzr+vpJkybxm9/8\nhvX/f3t3Hh9VfS5+/POcmewbISxhDQFkCagUAhEBRQXbK4KFIoKtlaoVr+ut+hOXW/WKSl3Pjjr/\nAAAgAElEQVToD6sWr1TcilrEIhRRlCoCCsgWRfY1EAhJIED2ZeZ87x8zaIgBsgyZhef9es0rJzNn\nzjzfOTPnmXnm+/2e5cu56667tGikVD0YY8jPthE8vUBO/HDytzde4IsvP+GWifcwIH2Qf4NUQc/h\nFJLaWBzJsXG76/5dIjY2lhkzZmCMYdKkSRw9Wv8fFoKJFoyCiKkow/7wr9hfzkV69McaeSuzXn+D\nN998k9/85jeMGzbE3yEqddZJ5/OhZXvGxBQyceJEZs+ezauvvurvsJTyiRJTxWuuraRILH0kySfb\nXLPw38y69wk69urGpJenEB6l3feVOpNwcTDAasVqO48y42rQNsaNG8cTTzzBihUruP322ykvL/dx\nlEqFpmP5hvJSSGorOMM9xaJ5C97l3TmvMWrEOCaMu9nPEdbPibmX6nJRTatVewu3CwoO1W/4cadO\nnXjppZc4cOAAd911F5WVlWcpQv/TglGQsHd9h/uN/8Hs2IB12TisUbcxb/4Cpk2bxogRI3jooYf8\nHaJSTULEwrpoBBzJ4b5RlzFy5EimT5/O+++/7+/QlGq0N9zbOU4VtznSsHwwFG35e//y9iy6gDtm\nPkNkTLQPolTq3DDcak8ZbpY3sJcRwLXXXsuTTz7JypUrmTRpEoWFhT6MUKnQU3jE5miuISZBiG3m\nyYPLv1rCizOe5uKLLuPu2x/x2VBtpeKThPDI+g1LO6Ffv35MnTqVdevW8cADD+ByNezHhUCnBaMA\nZ3L34f7gL9hzp4MlOCb8P6wBv2DJkiU8+uijDBo0iKeffhrL0l2pzh3Soz8ktobVi3jyyScZMmQI\njz/+OJ988om/Q1OqwVa6c1lm5zDa6kRnK75R2zLG8Mn/zmbOk3+h16UZ/OeMp7RYpFQ9dZV4Oksc\ni+39jRr6PGbMGJ599lk2bNjAb37zG3JycnwYpVKho6rSsH2DC2c4tGwniAgrVy/lian306P7+fzx\nwWdx6Px7yodEhNYpDo7lG0oK6180GjFiBJMnT2bx4sVMnjw5JItGTn8HoH7KGIPZswnzzSeYrM0Q\nHoE19FokfTjicPLRRx8xefJkevfuzQsvvKBnRFPnHLEsrIEjsBfNwrl/C9OnT+fWW2/lvvvuo7Ky\nklGjRvk7RKXq5bApZ6Z7C10lnl85Uhu1raqKSt559M+s/ejf9B85jF8/cR+OME33StWXiHCl1Z5X\n3FvYYo6RJokN3tbVV19NUlISd999NxMmTOCVV16hR48ePoxWncvqOpQpOSVwiy22bdi+3kVVJbTt\nbGE5hK9XLeWxJ/+LLqndefbJV4iMjPJ3mCqI1PV9YTkMlgMO7LTp1rf+nTAmTpyIy+Vi2rRpOBwO\npk6dGlKFTf0EeZbVZ2JpY7shZy9m7yYoPgYRUUi3vtC+G4SFYzau4IMvvubRmX8nvUdXXr5nIlE7\n1lD/WqhSwU/SLoKvFmAvnUvU7x7n1Vdf5Y477uDBBx+kvLyccePG+TtEperEbWxedm3CheEuZ28c\n0vAeo4WHC5h5z+Ps/XYLV9/9O678/QTtuq9UI1xsJfO2ewef2tmkWQ0vGAEMHDiQ2bNnc+utt3L9\n9dczZcoURowY4aNIlQpueze5OX7Y0PVCB7YNK1Z+zv88dS9dO/fguadfJTa2cT1vlToVh0NITrE4\nuNumQ3dDVEz9PzfdcsstuN1upk+fjjGGp556KmQ6deg4pgBgKssxuzdilv0T8/1XAEjvQcglY5DU\n3khYOMYY3li4hD+++jYDz+/BjMl3EqMTl6pzmDicWJePh8MHMBu+IDo6mhkzZjB48GAee+wxZs6c\nqWdPU0HhH+5dbDZHudnRnWRp+LCx7d9k8szY/+Tg9j3c/P8f5ee3Xq/FIqUaKUIcDLXa8o2dx2HT\n+Emru3Xrxpw5c0hLS+P+++9n6tSpVFVV+SBSpYLXob1uDmXZtO1s0aqDg/kL3+OxKf/FeV3TeG7q\nTC0WqbOubWcHYsHBXQ2feHzSpEnce++9LFy4kNtuu43i4mIfRug/WjDyI1NahL15FebLDzA7NkBs\nItJvGHLxSKRdF8TydGWrdLl49NW/89zsD7gyoy8v3/+fREWERsVSqcaQ836GdOqFvfxDTGkhkZGR\nvPTSS1x11VX8+c9/5uGHHw7psxao4PeNncd8O4thVjsudbRt0DZst5tFf32bl26ZTGRcDPf+/QX6\nDNezZirlK1c5OiLAXPdun2yvVatWvP7669xwww289dZb/Pa3v2Xfvn0+2bZSwebwQTe7v3eT2Ero\n0F147rnnmP7Sk2T0H8K0qX8jNibO3yGqc0B4pNCqvUVetk15ScN/cP7973/P008/zZo1a7jhhhvI\ny8vzYZT+oQUjPzDlpZ5C0YoPIXsntElFLh6JlT4MadH2pF+ECwqL+P1Tf+GfS7/mtjFXMe3umwkP\nC/Nj9EoFDhHBumICVFVgf/kBAOHh4Tz//PPceeedfPjhh9x0000cOXLEz5Eq9VP77WL+6tpEF4ln\noqN7g7aRtzebF353Px//9S3SR1zOA/94mXbdO/s4UqXObS0kkiut9iy1D3LAlPhkm2FhYTz88MM8\n//zz7Nq1i9GjRzNnzhztGavOKQW5Njs2uIlvLrTpVsYf/vAHZs2axTVXj2fKoy8QFaUna1BNp/15\nDiwLdn/vatSxePTo0cyYMYN9+/YxduxY1q1b58Mom54WjJqQqarA3r4es3weZO+A9t2QS8dg9b4Y\nifvpuPhV32/lVw8+xXe79vDMnb/jrmtH6tnQlKpBWrRF0odjvluOvTPTc50Id9xxB9OmTeP777/n\nl7/8JV999ZWfI1XqRwWmgqmuDUTg4F7nBYTVc94it8vNkllz+NOvJpGzcy83PP0Av506mYhonRBU\nqbNhtCOVCBz8w7XLp9sdMWIE8+fP54ILLuCxxx5j0qRJHDhwwKePoVQgOnzQzbZ1LmLiBTtmO9dd\ndy2ff/45kydP5p47HsHh0Kl2VdOKiBI6dvecMe3IwcbNEjx48GDeeecdoqKiuPHGG3njjTeC9gcB\nrT40AWPbmKwtmGXzYM/30LojMviXWGkZSMRPK+dVLjd/fncetzz9F2KjonjniQe4etAAP0SuVHCw\nhoyGVh2xF83CFBb8cP1VV13FnDlzSEhI4JZbbuG5557TIWrK70qNiz+5NlCCiwedfWgh9ZuPbufa\n73h+/B3M//NMegxK55H5f2PAqOFnKVqlFEC8hHO11ZHVJo+d9nGfbrtNmza89tprPPzww6xbt46R\nI0eyYsUKnz6GUoHk4G4329e7iY43ZO7+Bzf89noqKip46623mDhxos6/p/wmuZNFTIKwZ7MbV2Xj\nCjzdu3dn7ty5DB06lGeeeYY777yT/Px8H0XadLRgdJaZo7mYlR9htq6BhCRk4NVYFwxBomsfj7th\n+y7GPTyV1xZ8ytjLB/GPpx6kZ6cOTRy1UsFFnGE4rrkNXFW4F77qOeOgV7du3Xj//fe57rrrmDVr\nFmPGjAn6rqEqeJUZF8+4MtlvSviD8wJSrbpP5Hk4O4dZ903hhYn3UXKskJum/Te/f+FxElomncWI\nlVInjHCk0Ixw/te9hSrj23PUWpbFDTfcwL/+9S+GDx9O7969fbp9pQKB22XYkeli72Y3ZSaL5165\nmal/eoqMjAzmzZtH3759/R2iOseJCF0ucOCqhO0bXBi7cUWjuLg4XnzxRSZPnsyKFSsYOXIkCxcu\nDKreRhJowaanp5u1a9f6O4xGMyXHsZfO9Zz1LDIa6d7f07PoFBXzY0XFTP/HfN7/9wqSkxJ5ZOJ1\nXJ5+YRNHrVRgs/oMPe3t9vdfY3/0N+T8wVj/MRGpMcxn6dKlTJkyhYMHDzJ27FjuvfdeEhMbd5rk\npiYi64wx6f6Ow5+CNU+c6Fm0wxRyt6M3Ax2tT7v+Enc2AAUHc1n86jus+nAxDqeT4TdfxxUTryVc\nz5SpVL0Mc7Rv9DbW24d5xpXJNVYK1zvP80FUvqU5wiNY80RDHco6+cxOtm1wVYLbDSe+6jkc0KaT\ng7AI/NKDp7DAZmemi8Lj5azeNJt35/4vERERTJ48mdGjR58UU832KHW2Jac4Tvo/N8vNro1u2qRa\npPbyzfDI3bt38/DDD/Ptt99yySWX8OCDD5KamuqTbddHffOEDg71MWO7MRu+wF4+D6oqIbU30vl8\nxFn7RNUl5eX8/eMvmPWvTymrqGTiiGHcPnYEMZH6RUCp+rJ6X4w5mof5egF2RBTW5eNP+gAydOhQ\nBgwYwMsvv8ybb77Jxx9/zE033cSNN95ITEyMHyNXoe6YqeA517fsMUXc4+zNRdbpi0UAB3fsYenb\n/+SbBUsQEQZdO4Irb5lAs9YtmiBipVRt+lotuMJqxwI7i752S3pYzfwdkjrHGdtQXmooLzaUlxkq\nysBdVfu6B3baOMIgJk6ISxQSWljENRccjrNXQCovMWRtdZN/wMW6zR/z4Scvcig3h2HDhvHHP/6R\nVq1anbXHVqqhWqc4KC0y5OyxiYx20ybVceY7nUHnzp2ZPXs2b7/9Ni+99BKjRo1iwoQJ3H777TRr\nFri5RHsY+ZDZtw33v9+BvP1Ip15Yw67H7N9W67pFpWXM/XwFry9cwpHjhVyefiH3jBtF1w4NO62y\nUueCM/UwAjDGYP/7Xcy6JUj/K7GGjkNqmSx+165dTJ8+nSVLlpCUlMTEiRMZN24c8fF1HyLkD/rr\ncfDliX12Mc+4MimkkrudvelvnfrDscvlYtmyZcyePZuvv/6asMgIMq65kitvGU9iG/1QrVRj+KKH\nEUC5cfFA1WoqsXkiLJ1WEjiTzWuO8Ai2PFFflRWGY3k2R/NsjuUb3C7P9WHhnol7wyI9y5ZTOPG7\nme2C6DihtNhQctxzMQbEgrhmnuJRQgshtplgWY0rIBnbcPyI4dBeN4dzXKzftIRPl89i156tpKWl\n8cADD5CRkXHK+2sPI9XUavYwAs/reOs6F0dzDR26OWh/nuWz3nmHDx/mxRdf5P333yc6Oppf//rX\n3HjjjTRv3twn2z+d+uYJLRj5gDmSg730fczOTIhLxLp8AtK9HyKCnbn0pHX35ebz3qdfMveLrygp\nKyejV3fuHjeKPt30NMhKnUldCkYAxtjYS97FrP830vkCrFG31jrBPMC3337LCy+8wMqVK4mJieHa\na69l/PjxpKSk+DBy39EvA8GTJ4wxLLcP8Zp7K1E4ecB5IZ1rmbPIGMOOHTtYsGAB8+fP5/Dhw7Rs\n2ZKMCVczeNzVxDQL7CKmUsHCVwUj8BSCH3etJZ5w/icsnQQJ99m2G0NzhEew5InTqV40McZQWQ6l\nRYbSQk8vIgCH01MEioqFqFjB4Tz9l9nqX4rdLkNhgeH4YZvjRzwFJADLAfFJQkKSRVyiEB0nOMNO\nvd1DWW6MbaisgIoyQ3mJJ86SkhK++XYRny1/m5zcLDp16sRtt93GyJFnPuuzFoxUoDDGkJ9tKD5m\niGsupGU4fdojb/v27cyYMYPFixcTGRnJmDFjGD9+PF27dvXZY9SkBaMmZEoKsb+aj8n8EsLCsS4a\ngaQPR8J+/NBgZy6lpKycJWsymbf0a9Zs2YHDsvjFwH5MHDGMtNSOfmyBUsGlrgWjE+wNX2AveQcS\nkrB+fiNWSs9TrrtlyxZef/11Fi1ahNvtJj09nV/96lcMGzaM2NjYRkbuO/plIDjyRKGpZJZ7Gyvt\nXHpIM+5x9qZ5tbOh2bbNd999x2effcaSJUvYt28fDoeDoUOHMmbMGIYMGcKXVq4fW6BU6PFlwQhg\nq32Mp1zraSsxPOTsQzOJ8On2G0JzhEcw5IkzydnjoqwESgsNJUXmh2FmEVGeIlF0vBAeWb/5iGrr\nRXFCVaWh8Ijh2GGbwsM2ZSU/3uYMh/AI8c5/5OmVZLvB7YKykh9jM8awN3sjKzd8yFffLKK8oozz\nuqbx6+tuYeyEK3E46jasRwtGKpAYYyjINRzPN0TFCt1+5iAmwbfnDtu1axczZ85k0aJFVFVV0b9/\nf8aOHcsVV1zh82kztGDUBEzRUew1n2Iyl4KrCvnZUKyLRyExP/4KnJ+fz/Lly/nsg/f4euMWKqtc\ndExuyehLBzJqyEUkJwXXRLtKBYL6FowAzP7tuBe9BsfykV4DsYaMRhJOPQdMXl4e8+fP54MPPiAr\nK4uwsDAGDRrE8OHDGTx4sN/H2uuXgcDOEy5j86mdzVz3bspxM87RhVFWCpYI2dnZrF69mtWrV7Nq\n1Sry8/MJCwsjIyOD4cOHc/nll9OixY+vzROTXiulfMPXBSOATPsw01zfEUMY9zrPp5uf5zTSHOER\nyHnidKoqDUfzbI7m2hTkGoztKdBExXnmHYo6Q2+fMzldwaimijJDSaGhrMgzR1JlhWcibdsGjKcn\nksMJFeUusg5s4psNS/hq9afk5ecQGRHFZZf+gquvupae3c9HROr12FowUoGotMhwJMemqhKSO1q0\n7+YgPMK3838VFBTwwQcfMGfOHLKzs4mIiGDo0KE/fA9JSEho9GNowegsMcbAgZ3YmUsxW9eAbSM9\nB2BdPBJJasPRo0dZs2bND18Gdu3aBUCbFs0Z1r8Pwwf8jL7du/jlrARKncusPkMxVZXYKxdiVn8M\nxiBpGVj9hkFyp1O+J40xZGZmsnjxYj777DMOHjwIeCasy8jI4KKLLiI9Pb1JxhpXp18GAjNPVBo3\nS+0cFrqzyKWMnqVR9N/uJn/zbjZt2sT69et/eA0lJSUxYMAALrvsMi699NJTzpulBSOlfOtsFIwA\nsuwiprm+4zDljLY6MdLRiUhp/ASpDaE5wiMQ88SplJcYCnJtCg7ZFB41YCAsAiJjhJg4ITKWRs8p\ndEJ9ijan4na72blzJ5mZmaxatYoVK76muLgQp9NJet+LGXrJzxk08HJiY+Ia/NhaMFKBKqmNxb5t\nbnL32VgWtE6xaNPJQWS0b7/j27ZNZmYmixYt4pNPPuHIkSNYlkWfPn24+OKLGTBgABdeeCHh4fUf\nDq0FIx8yxkB+Nva2tZ4iUcEhXM4IDrXtxZ649mzOOsCWLVvYvHkzOTk5AERHR9O3b18yMjIYOHAg\nPSrztEiklB9V75VkCguw137qGUZaVQGJrT2F39TzoW0qYtX+YcYYw5YtW1i1ahWrVq1i3bp1lJaW\nAtCmTRvS0tLo2bMnaWlpdO7cmXbt2uF0np2TUOqXgcDJE7Zts/5YNouzMlm9ewvH9hwgbG8BZnc+\nh/bu50R+TU5O5vzzzycjI4OMjAy6dKnbjwdaMFLKt85WwQig2FTxmnsrX9u5JBLBaEcnhlhtiJam\nPSGx5giPQMkTtbHdhqKjnqFfBYcMZcWeXBEdJzRvLSQmW8QmCLn7bD9HCpWVFezP3kvWvl3s2rOd\nLds2snXbRsrKPJ+BWiS1on+/QfRPH0T6zwYSF9f43g9KBbIThc+yYsP+7W6O5NgYA81aCi3aWTRv\nbTWqF2Bt3G43GzduZNmyZSxbtozNmzdjjOHDDz+ke/fu9d7eWSkYicgvgBcAB/A3Y8yfatweAbwF\n9AOOANcZY/Z6b3sIuBlwA3cbYxaf7rH8eYC3qyopztpB7tZvyduxhbzd28g7epy80kqyXU72FVeR\nnX8El8tzKgIRISUl5Ycvi/369aN3796EhYX9uM0ak14rpZpWbcPYTHkpZttazOZVmH3bAAPhkdA6\nBWnVwXvpCC3aIs6wn9y/qqqKjRs3kpmZyebNm9m8eTN79+79oUDgdDpp3749KSkpdOjQgdatW590\nSU5OJiKiYXNdBOqXgVDKE8YYCgsLKSgo4OjRoxQUFFBQUEDe4Xx2HdxPVk42h3IOcTwnH7u88of7\nOZwOUjqmkJqaSs+ePenduze9evU6aZhZfWjBSCnfOpsFoxO22cd4272DHeY44VhkWK3oIy3obSU2\nyRxHgZojILTyRF0ZY6iq8AxlKSywKTxiKDrmGWqGQEJzIbG1RfNk6yc9FJqil015eRlHCvLJzz9E\nXv4h8g4fIi8vh7z8Qxw4uI+DOfuxbU/hyrIcdO3SnbQeF5LW80J69byQNsnt9YdxdU6p2VOuosyQ\nm+Um/4BNRZlnCGlcouesg3GJnrMO+rqAdPz4cdavX8+ll156xgnka1PfPHHGnz1ExAG8DAwHsoE1\nIrLAGLO52mo3A0eNMV1FZDzwDHCdiKQB44FeQFtgiYh0M8b4/AiYm5vLwYMHqSgvo7y0lMryMsrL\nyqgsL6e8vIyKsjIqSksoLSmiuLCQwsIiiooKKSoqorC4lKKycooqqqhw/7SaHx0dTYcOHeh2fgrD\nU1Lo2LEjqamp9OjRw+eTUCmlzj6JjEYuvAQuvARTVozJ2orZtwWTuw/z3TJM1Y9FAGLiIS4RiWsO\n8c2R6HgckdH0iYimz6X9kCuHgDOckopKtu3NYm92DlkHDpC1P5usfftZu3YtJSUlJz3+Y489xvjx\n45u41WdPsOSJL774go0bN1JQUkhJSQklJSWUlpRSUlJMSXEppaUllJaUUFpUjNtV+8M7k+IIS04k\nuksreg/uQ/e2KWS060Za1260b9/+pB8MlFLnnu5WM6ZIOrtMIZ/bB1ll57KcQ+CGJCLoILG0ligS\nJJx4wmkm4cQRTiQOIsRBNM6AOeOaLwVLnjgdYwy2Dcamxl+Dq8rTcbmywqay3FBeZlNWZFNSbHsm\nhDZgMETHQ/M2ENdciEsUHE7Pdt0Gir09jYwxGGMoKnLhcrtwuVy4XT8uu1xVnkuN26pcVVSUl1Ne\nXkpZeVm1v55LSUkxhUXHOF54jMKi4xQWHqOysuIn7UxISKRVi2Q6p3bj8qH/QUrHLnTq2IX27To1\naPiLUqEsIkro2MNJh+6eM6kV5Nocy/f0PjohLAIio4XIGCEyWgiPAmeY4HR6JpR3OAXL8kwmf2JS\neZETl58WmxISErjsssuarI116Sc7ANhpjNkNICLvAdcA1Q/w1wCPe5fnAi+Jp3XXAO8ZYyqAPSKy\n07u9lb4J/0fvzfwrr8yec8b1wiwhLtxJfLiDuHAncVGRJLduRnx8ArEJzWiR3JZWnbvRqlMXWrVq\nRcuWLbUopFQIk6hYpEc69PAU2o1tw7E8TN5+OJKDKSqAwgLM0TzYtxVz4ly2NUQCF3ovALQH2icB\nSZRUucktqSSvtJLc0gr69q5/99EAFxR5YsmSJfzzn//Eio7AionAio7EEROBFROJo20kVnQCYTGR\nNI+LwpkYizMxlmbNm9GyeQvaNG9Jx+at6RLZnM5WPK2JwtJfVZVStRARukoCXa0EbjE92GOK2GyO\nkmWK2GeK2WEfpwRXrfe9QJrzSFjfJo64SQRFnpg0aRJr1qz5oWgDJwpF3v+9hZ8T18OP6wUih8NJ\nZGQUUZFRxMTEEh/XjOTW7eh2XhoJcc2Ij29GYmISrVq2oVXLZFq2aE1kZJS/w1Yq6Ih4isBxiRYp\nPcBV5SkgFR83lJd4LscP2+SX13/bPxsaRlSs/z5z1qVg1A7YX+3/bCDjVOsYY1wichxI8l6/qsZ9\n29V8ABG5FbjV+2+xiGyrU/SBoQVw2N9B+EAotCMU2gDajkBydtrwt48ac+8UX4XhQ5onTi8U3gs1\naZuCg7YpOPzQpjnAfzduW4GYIyBw8kSgv340vsbR+Bov0GPU+BqnBfXME007E98pGGNeBV71dxwN\nISJrA3WseH2EQjtCoQ2g7QgkodCGUKF5IrBom4KDtik4hGKb/KEueSLQn2uNr3E0vsYL9Bg1vsbx\nxtepPvepyyxJB4AO1f5v772u1nVExAkk4Jmsri73VUopFdw0TyillDodzRNKKRWE6lIwWgOcJyKp\nIhKOZ9K5BTXWWQDc6F0eC3xuPAN6FwDjRSRCRFKB84BvfBO6UkqpAKF5Qiml1OlonlBKqSB0xiFp\n3jHEdwKL8ZwGc5YxZpOIPAGsNcYsAF4D3vZOQleAJwngXW8OngntXMAdTX1GgyYQlEMkahEK7QiF\nNoC2I5CEQhvOOs0TZxSKryNtU3DQNgWHUGzTSQIoTwT6c63xNY7G13iBHqPG1zj1jk8CeWZ/pZRS\nSimllFJKKdX06jIkTSmllFJKKaWUUkqdQ7RgpJRSSimllFJKKaVOogWjBhKR50Rkq4h8JyLzRKRZ\ntdseEpGdIrJNRH7uzzhPR0SuFZFNImKLSHq16zuJSJmIZHovr/gzzjM5VTu8twXFvqhJRB4XkQPV\n9sFV/o6prkTkF97ne6eIPOjveBpKRPaKyEbv87/W3/Go4BMKeaKmUMkb1YViDqkumPNJTaGSX6rT\nXHP2icgU73E4U0Q+FZG23utFRP7ifT19JyJ9/RRfrbkikI6rgZ7PAj03BVOeCdScEQzH/0A7novI\nLBHJE5Hvq13XXEQ+E5Ed3r+JZ9yQMUYvDbgAVwJO7/IzwDPe5TTgWyACSAV2AQ5/x3uKNvQEugNL\ngfRq13cCvvd3fD5oR9Dsi1ra9Dhwv7/jaEDcDu/z3BkI9z7/af6Oq4Ft2Qu08HccegneSyjkiVra\nFBJ5o45tCtr9VKN9QZlPamlHyOSXGu3SXHP2n+P4ast3A694l68CPgYEuAhY7af4TpUrAua4Guj5\nLNBzUzDlmUDMGcFy/A+04zlwCdC3+nsAeBZ40Lv84In38uku2sOogYwxnxpjXN5/VwHtvcvXAO8Z\nYyqMMXuAncAAf8R4JsaYLcaYbf6Oo7FO046g2RchZACw0xiz2xhTCbyHZz8odc4JhTxRU6jkjeo0\nhwQNzS+qQYwxhdX+jQFOnPHnGuAt47EKaCYibfwQ36lyRcAI9HwW6LlJ80yj6fG/AYwxy/CccbK6\na4A3vctvAr8803a0YOQbN+H5hQKgHbC/2m3Z3uuCTaqIbBCRL0VkiL+DaaBg3xd3erv+zqpTd8HA\nEOzPeXUG+FRE1onIrf4ORgW9UMwTNYVC3qgulPZTMOaTmkJpf1SnuaYJiMhTIrIf+ArBmvYAAAOF\nSURBVDXwqPfqQHxNVc8VEJjH1WDLZ4H4HJ4QqM9foOWMQH2eagqG43lrY0yOd/kQ0PpMd3Ce3XiC\nm4gsAZJruekRY8x87zqPAC5gdlPGVld1aUMtcoCOxpgjItIP+FBEetX4haZJNbAdAe10bQJmAFPw\nHHimANPwJGjVdAYbYw6ISCvgMxHZ6q3UK/WDUMgTNYVK3qguFHNIdZpPgprmGh8403vcGPMI8IiI\nPATcCTwWSPF516mZK5r0uBro+SzQc1Mw5RnNGWdNUB3PjTFGRMyZ1tOC0WkYY4ad7nYRmQhcDVxh\nvAMBgQNAh2qrtfde5xdnasMp7lMBVHiX14nILqAb4LfJuxrSDgJsX9RU1zaJyExg4VkOx1cC+jmv\nD2PMAe/fPBGZh6c7bMAe9JV/hEKeqClU8kZ1oZhDqgvRfFJT0OyP+tBc4xv1eI/PBhbhKRg12Wuq\nIbmiqY+rgZ7PAj03BVOeCcKcERTH/yA5nueKSBtjTI53CG7eme6gQ9IaSER+ATwAjDLGlFa7aQEw\nXkQiRCQVOA/4xh8xNpSItBQRh3e5M5427PZvVA0StPuixhj60cD3p1o3wKwBzhORVBEJB8bj2Q9B\nRURiRCTuxDKeyR6DZR+oABHKeaKmEMob1YXEfgrifFJTSOSX6jTXNA0ROa/av9cAW73LC4DfisdF\nwPFqQzWaMr5ac0UgHVeDNZ8F0nN4CgH3/AVozgj4438QHc8XADd6l28Eztj7TXsYNdxLeGa0/0xE\nAFYZY24zxmwSkTnAZjxdNu8wxrj9GOcpicho4EWgJfCRiGQaY36OZ0b1J0SkCrCB24wxNSfMChin\nakcw7YtaPCsiffB0B90LTPJvOHVjjHGJyJ3AYjxnNJhljNnk57AaojUwz/vedgLvGGM+8W9IKggF\nfZ6oKVTyRnUhmkOqC8p8UlMI5ZfqNNc0jT+JSHc8x6Ys4Dbv9YvwnCltJ1AK/M4/4dWeKwis42pA\n57NAz01BlmcCLmcEyfE/4I7nIvIuMBRoISLZeHpW/gmYIyI34zkejjvjdn7sUaiUUkoppZRSSiml\nlA5JU0oppZRSSimllFI1aMFIKaWUUkoppZRSSp1EC0ZKKaWUUkoppZRS6iRaMFJKKaWUUkoppZRS\nJ9GCkVJKKaWUUkoppZQ6iRaMlFJKKaWUUkoppdRJtGCklFJKKaWUUkoppU7yf5xrN9kAqAQiAAAA\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JlH_yli2a6d9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "cd9dc0be-5444-4b68-ae3d-7f7d0d3187e0"
      },
      "source": [
        "# # -----> V14 Removing Outliers (Highest Negative Correlated with Labels)\n",
        "v14_fraud = new_df['V14'].loc[new_df['Class'] == 1].values\n",
        "q25, q75 = np.percentile(v14_fraud, 25), np.percentile(v14_fraud, 75)\n",
        "print('Quartile 25: {} | Quartile 75: {}'.format(q25, q75))\n",
        "v14_iqr = q75 - q25\n",
        "print('iqr: {}'.format(v14_iqr))\n",
        "\n",
        "v14_cut_off = v14_iqr * 1.5\n",
        "v14_lower, v14_upper = q25 - v14_cut_off, q75 + v14_cut_off\n",
        "print('Cut Off: {}'.format(v14_cut_off))\n",
        "print('V14 Lower: {}'.format(v14_lower))\n",
        "print('V14 Upper: {}'.format(v14_upper))\n",
        "\n",
        "outliers = [x for x in v14_fraud if x < v14_lower or x > v14_upper]\n",
        "print('Feature V14 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
        "print('V10 outliers:{}'.format(outliers))\n",
        "\n",
        "new_df = new_df.drop(new_df[(new_df['V14'] > v14_upper) | (new_df['V14'] < v14_lower)].index)\n",
        "print('----' * 44)\n",
        "\n",
        "# -----> V12 removing outliers from fraud transactions\n",
        "v12_fraud = new_df['V12'].loc[new_df['Class'] == 1].values\n",
        "q25, q75 = np.percentile(v12_fraud, 25), np.percentile(v12_fraud, 75)\n",
        "v12_iqr = q75 - q25\n",
        "\n",
        "v12_cut_off = v12_iqr * 1.5\n",
        "v12_lower, v12_upper = q25 - v12_cut_off, q75 + v12_cut_off\n",
        "print('V12 Lower: {}'.format(v12_lower))\n",
        "print('V12 Upper: {}'.format(v12_upper))\n",
        "outliers = [x for x in v12_fraud if x < v12_lower or x > v12_upper]\n",
        "print('V12 outliers: {}'.format(outliers))\n",
        "print('Feature V12 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
        "new_df = new_df.drop(new_df[(new_df['V12'] > v12_upper) | (new_df['V12'] < v12_lower)].index)\n",
        "print('Number of Instances after outliers removal: {}'.format(len(new_df)))\n",
        "print('----' * 44)\n",
        "\n",
        "\n",
        "# Removing outliers V10 Feature\n",
        "v10_fraud = new_df['V10'].loc[new_df['Class'] == 1].values\n",
        "q25, q75 = np.percentile(v10_fraud, 25), np.percentile(v10_fraud, 75)\n",
        "v10_iqr = q75 - q25\n",
        "\n",
        "v10_cut_off = v10_iqr * 1.5\n",
        "v10_lower, v10_upper = q25 - v10_cut_off, q75 + v10_cut_off\n",
        "print('V10 Lower: {}'.format(v10_lower))\n",
        "print('V10 Upper: {}'.format(v10_upper))\n",
        "outliers = [x for x in v10_fraud if x < v10_lower or x > v10_upper]\n",
        "print('V10 outliers: {}'.format(outliers))\n",
        "print('Feature V10 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
        "new_df = new_df.drop(new_df[(new_df['V10'] > v10_upper) | (new_df['V10'] < v10_lower)].index)\n",
        "print('Number of Instances after outliers removal: {}'.format(len(new_df)))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Quartile 25: -9.692722964972385 | Quartile 75: -4.282820849486866\n",
            "iqr: 5.409902115485519\n",
            "Cut Off: 8.114853173228278\n",
            "V14 Lower: -17.807576138200663\n",
            "V14 Upper: 3.8320323237414122\n",
            "Feature V14 Outliers for Fraud Cases: 4\n",
            "V10 outliers:[-18.049997689859396, -18.4937733551053, -19.2143254902614, -18.8220867423816]\n",
            "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "V12 Lower: -17.3430371579634\n",
            "V12 Upper: 5.776973384895937\n",
            "V12 outliers: [-18.4311310279993, -18.553697009645802, -18.047596570821604, -18.683714633344298]\n",
            "Feature V12 Outliers for Fraud Cases: 4\n",
            "Number of Instances after outliers removal: 976\n",
            "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "V10 Lower: -14.89885463232024\n",
            "V10 Upper: 4.920334958342141\n",
            "V10 outliers: [-18.2711681738888, -24.5882624372475, -15.1237521803455, -16.6011969664137, -15.2399619587112, -16.2556117491401, -15.124162814494698, -16.3035376590131, -22.1870885620007, -19.836148851696, -18.9132433348732, -22.1870885620007, -17.141513641289198, -15.563791338730098, -24.403184969972802, -16.7460441053944, -15.563791338730098, -15.2399619587112, -22.1870885620007, -15.346098846877501, -14.9246547735487, -22.1870885620007, -20.949191554361104, -23.2282548357516, -16.6496281595399, -14.9246547735487, -15.2318333653018]\n",
            "Feature V10 Outliers for Fraud Cases: 27\n",
            "Number of Instances after outliers removal: 943\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KnIs09h_a-zC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "6e59a45a-6af6-4e8b-835c-fe0dad9d416d"
      },
      "source": [
        "f,(ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,6))\n",
        "\n",
        "colors = ['#B3F9C5', '#f9c5b3']\n",
        "# Boxplots with outliers removed\n",
        "# Feature V14\n",
        "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df,ax=ax1, palette=colors)\n",
        "ax1.set_title(\"V14 Feature \\n Reduction of outliers\", fontsize=14)\n",
        "ax1.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.5), xytext=(0, -12),\n",
        "            arrowprops=dict(facecolor='black'),\n",
        "            fontsize=14)\n",
        "\n",
        "# Feature 12\n",
        "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, ax=ax2, palette=colors)\n",
        "ax2.set_title(\"V12 Feature \\n Reduction of outliers\", fontsize=14)\n",
        "ax2.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.3), xytext=(0, -12),\n",
        "            arrowprops=dict(facecolor='black'),\n",
        "            fontsize=14)\n",
        "\n",
        "# Feature V10\n",
        "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, ax=ax3, palette=colors)\n",
        "ax3.set_title(\"V10 Feature \\n Reduction of outliers\", fontsize=14)\n",
        "ax3.annotate('Fewer extreme \\n outliers', xy=(0.95, -16.5), xytext=(0, -12),\n",
        "            arrowprops=dict(facecolor='black'),\n",
        "            fontsize=14)\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iIxSupaUFgObm5kJzSJIkqTpUZUEJBh7yMJYcyuBQBnAogyRJkiRJw1G1BaVK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\nuVjnXFPn3NXFjv2nc66Nc+4S59zqYvXXFY5ydc7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            "text/plain": [
              "<Figure size 1440x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CGQTkucqbEdt",
        "colab_type": "text"
      },
      "source": [
        "Dimensionality Reduction and Clustering:\n",
        "Understanding t-SNE:\n",
        "\n",
        "In order to understand this algorithm you have to understand the following terms: \n",
        "*   Euclidean Distance\n",
        "*   Conditional Probability\n",
        "*   Normal and T-Distribution Plots\n",
        "\n",
        "Note: If you want a simple instructive video look at StatQuest: t-SNE, Clearly Explained by Joshua Starmer\n",
        "\n",
        "Summary:\n",
        "\n",
        "*   t-SNE algorithm can pretty accurately cluster the cases that were fraud and non-fraud in our dataset.\n",
        "*   Although the subsample is pretty small, the t-SNE algorithm is able to detect clusters pretty accurately in every scenario (I shuffle the dataset before running t-SNE)\n",
        "*   This gives us an indication that further predictive models will perform pretty well in separating fraud cases from non-fraud cases."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OwfkzJ0BbaJP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "68c215b8-7620-4b27-fc7d-1d4c4b0c2cd6"
      },
      "source": [
        "import time\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.decomposition import PCA, TruncatedSVD\n",
        "\n",
        "# New_df is from the random undersample data (fewer instances)\n",
        "X = new_df.drop('Class', axis=1)\n",
        "y = new_df['Class']\n",
        "\n",
        "\n",
        "# T-SNE Implementation\n",
        "t0 = time.time()\n",
        "X_reduced_tsne = TSNE(n_components=2, random_state=42).fit_transform(X.values)\n",
        "t1 = time.time()\n",
        "print(\"T-SNE took {:.2} s\".format(t1 - t0))\n",
        "\n",
        "# PCA Implementation\n",
        "t0 = time.time()\n",
        "X_reduced_pca = PCA(n_components=2, random_state=42).fit_transform(X.values)\n",
        "t1 = time.time()\n",
        "print(\"PCA took {:.2} s\".format(t1 - t0))\n",
        "\n",
        "# TruncatedSVD\n",
        "t0 = time.time()\n",
        "X_reduced_svd = TruncatedSVD(n_components=2, algorithm='randomized', random_state=42).fit_transform(X.values)\n",
        "t1 = time.time()\n",
        "print(\"Truncated SVD took {:.2} s\".format(t1 - t0))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "T-SNE took 6.9 s\n",
            "PCA took 0.029 s\n",
            "Truncated SVD took 0.004 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jwqk1ndFb1Cm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "5f743e9c-72cd-49aa-db8e-21eddaa402f9"
      },
      "source": [
        "import matplotlib.patches as mpatches\n",
        "\n",
        "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24,6))\n",
        "# labels = ['No Fraud', 'Fraud']\n",
        "f.suptitle('Clusters using Dimensionality Reduction', fontsize=14)\n",
        "\n",
        "\n",
        "blue_patch = mpatches.Patch(color='#0A0AFF', label='No Fraud')\n",
        "red_patch = mpatches.Patch(color='#AF0000', label='Fraud')\n",
        "\n",
        "\n",
        "# t-SNE scatter plot\n",
        "ax1.scatter(X_reduced_tsne[:,0], X_reduced_tsne[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n",
        "ax1.scatter(X_reduced_tsne[:,0], X_reduced_tsne[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n",
        "ax1.set_title('t-SNE', fontsize=14)\n",
        "\n",
        "ax1.grid(True)\n",
        "\n",
        "ax1.legend(handles=[blue_patch, red_patch])\n",
        "\n",
        "\n",
        "# PCA scatter plot\n",
        "ax2.scatter(X_reduced_pca[:,0], X_reduced_pca[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n",
        "ax2.scatter(X_reduced_pca[:,0], X_reduced_pca[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n",
        "ax2.set_title('PCA', fontsize=14)\n",
        "\n",
        "ax2.grid(True)\n",
        "\n",
        "ax2.legend(handles=[blue_patch, red_patch])\n",
        "\n",
        "# TruncatedSVD scatter plot\n",
        "ax3.scatter(X_reduced_svd[:,0], X_reduced_svd[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n",
        "ax3.scatter(X_reduced_svd[:,0], X_reduced_svd[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n",
        "ax3.set_title('Truncated SVD', fontsize=14)\n",
        "\n",
        "ax3.grid(True)\n",
        "\n",
        "ax3.legend(handles=[blue_patch, red_patch])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JmVknM3NmdlBTxyLS2JSUFWkEZpZhZo9EUO6vZjbHzHLMbLOZzTezfwWtH+mfkD6pZltn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rnW6+NLCjP1+4SM7Vmwt44tPh5KXnMe/OdeTqbbt31k7Zjd5iZF/VdmT4Ob4oOGPWGvj1m43c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\nQgghhLhEEpS9jvV8ZjjHujdl81s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            "text/plain": [
              "<Figure size 1728x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QPVtK6qEcDL1",
        "colab_type": "text"
      },
      "source": [
        "**Classifiers (UnderSampling):**\n",
        "In this section we will train four types of classifiers and decide which classifier will be more effective in detecting fraud transactions. Before we have to split our data into training and testing sets and separate the features from the labels.\n",
        "\n",
        "**Summary:**\n",
        "*  Logistic Regression classifier is more accurate than the other three classifiers in most cases. (We will further analyze Logistic Regression)\n",
        "*  GridSearchCV is used to determine the paremeters that gives the best predictive score for the classifiers.\n",
        "*  Logistic Regression has the best Receiving Operating Characteristic score (ROC), meaning that LogisticRegression pretty accurately separates fraud and non-fraud transactions.\n",
        "\n",
        "**Learning Curves:**\n",
        "\n",
        "*  The wider the gap between the training score and the cross validation score, the more likely your model is overfitting (high variance).\n",
        "*  If the score is low in both training and cross-validation sets this is an indication that our model is underfitting (high bias)\n",
        "*  Logistic Regression Classifier shows the best score in both training and cross-validating sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-XbRgercfzI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Undersampling before cross validating (prone to overfit)\n",
        "X = new_df.drop('Class', axis=1)\n",
        "y = new_df['Class']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wo6WD1niciaf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Our data is already scaled we should split our training and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# This is explicitly used for undersampling.\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7YL2_xKackbx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Turn the values into an array for feeding the classification algorithms.\n",
        "X_train = X_train.values\n",
        "X_test = X_test.values\n",
        "y_train = y_train.values\n",
        "y_test = y_test.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "urHh1o3TcklY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Classifier Libraries\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# Let's implement simple classifiers\n",
        "\n",
        "classifiers = {\n",
        "    \"LogisiticRegression\": LogisticRegression(),\n",
        "    \"KNearest\": KNeighborsClassifier(),\n",
        "    \"Support Vector Classifier\": SVC(),\n",
        "    \"DecisionTreeClassifier\": DecisionTreeClassifier()\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4yXmiQoc2pK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "80c67153-7129-41f4-bc12-c3b01cf62ee6"
      },
      "source": [
        "# Wow our scores are getting even high scores even when applying cross validation.\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "\n",
        "for key, classifier in classifiers.items():\n",
        "    classifier.fit(X_train, y_train)\n",
        "    training_score = cross_val_score(classifier, X_train, y_train, cv=5)\n",
        "    print(\"Classifiers: \", classifier.__class__.__name__, \"Has a training score of\", round(training_score.mean(), 2) * 100, \"% accuracy score\")"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classifiers:  LogisticRegression Has a training score of 95.0 % accuracy score\n",
            "Classifiers:  KNeighborsClassifier Has a training score of 94.0 % accuracy score\n",
            "Classifiers:  SVC Has a training score of 94.0 % accuracy score\n",
            "Classifiers:  DecisionTreeClassifier Has a training score of 90.0 % accuracy score\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pr2bDzXLc7Il",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d729716a-ca6b-455a-a2e2-26379b12595b"
      },
      "source": [
        "# Use GridSearchCV to find the best parameters.\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "\n",
        "# Logistic Regression \n",
        "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
        "\n",
        "\n",
        "\n",
        "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n",
        "grid_log_reg.fit(X_train, y_train)\n",
        "# We automatically get the logistic regression with the best parameters.\n",
        "log_reg = grid_log_reg.best_estimator_\n",
        "\n",
        "knears_params = {\"n_neighbors\": list(range(2,5,1)), 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']}\n",
        "\n",
        "grid_knears = GridSearchCV(KNeighborsClassifier(), knears_params)\n",
        "grid_knears.fit(X_train, y_train)\n",
        "# KNears best estimator\n",
        "knears_neighbors = grid_knears.best_estimator_\n",
        "\n",
        "# Support Vector Classifier\n",
        "svc_params = {'C': [0.5, 0.7, 0.9, 1], 'kernel': ['rbf', 'poly', 'sigmoid', 'linear']}\n",
        "grid_svc = GridSearchCV(SVC(), svc_params)\n",
        "grid_svc.fit(X_train, y_train)\n",
        "\n",
        "# SVC best estimator\n",
        "svc = grid_svc.best_estimator_\n",
        "\n",
        "# DecisionTree Classifier\n",
        "tree_params = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": list(range(2,4,1)), \n",
        "              \"min_samples_leaf\": list(range(5,7,1))}\n",
        "grid_tree = GridSearchCV(DecisionTreeClassifier(), tree_params)\n",
        "grid_tree.fit(X_train, y_train)\n",
        "\n",
        "# tree best estimator\n",
        "tree_clf = grid_tree.best_estimator_"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NdivVokoduQx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "4b180d21-d3c3-4362-c06c-db6049243951"
      },
      "source": [
        "# Overfitting Case\n",
        "\n",
        "log_reg_score = cross_val_score(log_reg, X_train, y_train, cv=5)\n",
        "print('Logistic Regression Cross Validation Score: ', round(log_reg_score.mean() * 100, 2).astype(str) + '%')\n",
        "\n",
        "\n",
        "knears_score = cross_val_score(knears_neighbors, X_train, y_train, cv=5)\n",
        "print('Knears Neighbors Cross Validation Score', round(knears_score.mean() * 100, 2).astype(str) + '%')\n",
        "\n",
        "svc_score = cross_val_score(svc, X_train, y_train, cv=5)\n",
        "print('Support Vector Classifier Cross Validation Score', round(svc_score.mean() * 100, 2).astype(str) + '%')\n",
        "\n",
        "tree_score = cross_val_score(tree_clf, X_train, y_train, cv=5)\n",
        "print('DecisionTree Classifier Cross Validation Score', round(tree_score.mean() * 100, 2).astype(str) + '%')"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logistic Regression Cross Validation Score:  95.1%\n",
            "Knears Neighbors Cross Validation Score 94.43%\n",
            "Support Vector Classifier Cross Validation Score 94.56%\n",
            "DecisionTree Classifier Cross Validation Score 93.11%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DjnKVGendx4i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Let's Plot LogisticRegression Learning Curve\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "def plot_learning_curve(estimator1, estimator2, estimator3, estimator4, X, y, ylim=None, cv=None,\n",
        "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
        "    f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(20,14), sharey=True)\n",
        "    if ylim is not None:\n",
        "        plt.ylim(*ylim)\n",
        "    # First Estimator\n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator1, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    ax1.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"#ff9124\")\n",
        "    ax1.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
        "    ax1.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
        "             label=\"Training score\")\n",
        "    ax1.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
        "             label=\"Cross-validation score\")\n",
        "    ax1.set_title(\"Logistic Regression Learning Curve\", fontsize=14)\n",
        "    ax1.set_xlabel('Training size (m)')\n",
        "    ax1.set_ylabel('Score')\n",
        "    ax1.grid(True)\n",
        "    ax1.legend(loc=\"best\")\n",
        "    \n",
        "    # Second Estimator \n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator2, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    ax2.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"#ff9124\")\n",
        "    ax2.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
        "    ax2.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
        "             label=\"Training score\")\n",
        "    ax2.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
        "             label=\"Cross-validation score\")\n",
        "    ax2.set_title(\"Knears Neighbors Learning Curve\", fontsize=14)\n",
        "    ax2.set_xlabel('Training size (m)')\n",
        "    ax2.set_ylabel('Score')\n",
        "    ax2.grid(True)\n",
        "    ax2.legend(loc=\"best\")\n",
        "    \n",
        "    # Third Estimator\n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator3, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    ax3.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"#ff9124\")\n",
        "    ax3.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
        "    ax3.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
        "             label=\"Training score\")\n",
        "    ax3.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
        "             label=\"Cross-validation score\")\n",
        "    ax3.set_title(\"Support Vector Classifier \\n Learning Curve\", fontsize=14)\n",
        "    ax3.set_xlabel('Training size (m)')\n",
        "    ax3.set_ylabel('Score')\n",
        "    ax3.grid(True)\n",
        "    ax3.legend(loc=\"best\")\n",
        "    \n",
        "    # Fourth Estimator\n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator4, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    ax4.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"#ff9124\")\n",
        "    ax4.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
        "    ax4.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
        "             label=\"Training score\")\n",
        "    ax4.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
        "             label=\"Cross-validation score\")\n",
        "    ax4.set_title(\"Decision Tree Classifier \\n Learning Curve\", fontsize=14)\n",
        "    ax4.set_xlabel('Training size (m)')\n",
        "    ax4.set_ylabel('Score')\n",
        "    ax4.grid(True)\n",
        "    ax4.legend(loc=\"best\")\n",
        "    return plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bAsblMm0eEbw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "outputId": "88192ce6-e8fc-4d22-fab1-85813dd406d5"
      },
      "source": [
        "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=42)\n",
        "plot_learning_curve(log_reg, knears_neighbors, svc, tree_clf, X_train, y_train, (0.87, 1.01), cv=cv, n_jobs=4)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py'>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9V//uqVSq1/Xe1NTEbrvttqlFkxpQG0w2leq0+lSn1TfU6lRtsMqoDVZcpe2v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HxK9E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\n",
            "text/plain": [
              "<Figure size 1440x1008 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uoXA-7PveMwr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "# Create a DataFrame with all the scores and the classifiers names.\n",
        "\n",
        "log_reg_pred = cross_val_predict(log_reg, X_train, y_train, cv=5,\n",
        "                             method=\"decision_function\")\n",
        "\n",
        "knears_pred = cross_val_predict(knears_neighbors, X_train, y_train, cv=5)\n",
        "\n",
        "svc_pred = cross_val_predict(svc, X_train, y_train, cv=5,\n",
        "                             method=\"decision_function\")\n",
        "\n",
        "tree_pred = cross_val_predict(tree_clf, X_train, y_train, cv=5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IGb36bUVePYS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "e451b77d-f66e-4e51-9c60-8c31a4dbd844"
      },
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "print('Logistic Regression: ', roc_auc_score(y_train, log_reg_pred))\n",
        "print('KNears Neighbors: ', roc_auc_score(y_train, knears_pred))\n",
        "print('Support Vector Classifier: ', roc_auc_score(y_train, svc_pred))\n",
        "print('Decision Tree Classifier: ', roc_auc_score(y_train, tree_pred))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logistic Regression:  0.9845853754718044\n",
            "KNears Neighbors:  0.94339755506732\n",
            "Support Vector Classifier:  0.9803320939665371\n",
            "Decision Tree Classifier:  0.9299687341558222\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3dwQLLfeT3a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "outputId": "b409e2c7-8134-459a-ae25-2ff564335ac4"
      },
      "source": [
        "log_fpr, log_tpr, log_thresold = roc_curve(y_train, log_reg_pred)\n",
        "knear_fpr, knear_tpr, knear_threshold = roc_curve(y_train, knears_pred)\n",
        "svc_fpr, svc_tpr, svc_threshold = roc_curve(y_train, svc_pred)\n",
        "tree_fpr, tree_tpr, tree_threshold = roc_curve(y_train, tree_pred)\n",
        "\n",
        "\n",
        "def graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr):\n",
        "    plt.figure(figsize=(16,8))\n",
        "    plt.title('ROC Curve \\n Top 4 Classifiers', fontsize=18)\n",
        "    plt.plot(log_fpr, log_tpr, label='Logistic Regression Classifier Score: {:.4f}'.format(roc_auc_score(y_train, log_reg_pred)))\n",
        "    plt.plot(knear_fpr, knear_tpr, label='KNears Neighbors Classifier Score: {:.4f}'.format(roc_auc_score(y_train, knears_pred)))\n",
        "    plt.plot(svc_fpr, svc_tpr, label='Support Vector Classifier Score: {:.4f}'.format(roc_auc_score(y_train, svc_pred)))\n",
        "    plt.plot(tree_fpr, tree_tpr, label='Decision Tree Classifier Score: {:.4f}'.format(roc_auc_score(y_train, tree_pred)))\n",
        "    plt.plot([0, 1], [0, 1], 'k--')\n",
        "    plt.axis([-0.01, 1, 0, 1])\n",
        "    plt.xlabel('False Positive Rate', fontsize=16)\n",
        "    plt.ylabel('True Positive Rate', fontsize=16)\n",
        "    plt.annotate('Minimum ROC Score of 50% \\n (This is the minimum score to get)', xy=(0.5, 0.5), xytext=(0.6, 0.3),\n",
        "                arrowprops=dict(facecolor='#6E726D', shrink=0.05),\n",
        "                )\n",
        "    plt.legend()\n",
        "    \n",
        "graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr)\n",
        "plt.show()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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q0j0J+1RjtbW4huyCfiDpBGAvrIaGDUu4nk11VeNqRKRn+vTTT5k7dy7z58+n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VL1ANyBZg94/33gfYA33njj8U+QlYourzYglV5R8mRmZrJw4UK+/PJLkpKSqFWrFo0bN2bAgAGG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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGFIf0-2eZH6",
        "colab_type": "text"
      },
      "source": [
        "A Deeper Look into LogisticRegression:\n",
        "In this section we will ive a deeper look into the logistic regression classifier.\n",
        "\n",
        "Terms:\n",
        "\n",
        "* True Positives: Correctly Classified Fraud Transactions\n",
        "* False Positives: Incorrectly Classified Fraud Transactions\n",
        "* True Negative: Correctly Classified Non-Fraud Transactions\n",
        "* False Negative: Incorrectly Classified Non-Fraud Transactions\n",
        "* Precision: True Positives/(True Positives + False Positives)\n",
        "* Recall: True Positives/(True Positives + False Negatives)\n",
        "* Precision as the name says, says how precise (how sure) is our model in detecting fraud transactions while recall is the amount of fraud cases our model is able to detect.\n",
        "* Precision/Recall Tradeoff: The more precise (selective) our model is, the less cases it will detect. Example: Assuming that our model has a precision of 95%, Let's say there are only 5 fraud cases in which the model is 95% precise or more that these are fraud cases. Then let's say there are 5 more cases that our model considers 90% to be a fraud case, if we lower the precision there are more cases that our model will be able to detect.\n",
        "\n",
        "### Summary:\n",
        "\n",
        "* Precision starts to descend between 0.90 and 0.92 nevertheless, our precision score is still pretty high and still we have a descent recall score."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGnRPfnverfB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "7d1330dc-e908-44bb-d478-da7ac34494cb"
      },
      "source": [
        "def logistic_roc_curve(log_fpr, log_tpr):\n",
        "    plt.figure(figsize=(12,8))\n",
        "    plt.title('Logistic Regression ROC Curve', fontsize=16)\n",
        "    plt.plot(log_fpr, log_tpr, 'b-', linewidth=2)\n",
        "    plt.plot([0, 1], [0, 1], 'r--')\n",
        "    plt.xlabel('False Positive Rate', fontsize=16)\n",
        "    plt.ylabel('True Positive Rate', fontsize=16)\n",
        "    plt.axis([-0.01,1,0,1])\n",
        "    \n",
        "    \n",
        "logistic_roc_curve(log_fpr, log_tpr)\n",
        "plt.show()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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svnmxNUnthKFbkiQ17quv4IAD8jLuAD16wEUXwXzzFVuX1I4YuiVJUsPuuSfP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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZG7sQuPewDs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "precision, recall, threshold = precision_recall_curve(y_train, log_reg_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_a72VupewQi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "bc9ea142-35df-4a62-c930-d6299e3d65d6"
      },
      "source": [
        "from sklearn.metrics import recall_score, precision_score, f1_score, accuracy_score\n",
        "y_pred = log_reg.predict(X_train)\n",
        "\n",
        "# Overfitting Case\n",
        "print('---' * 45)\n",
        "print('Overfitting: \\n')\n",
        "print('Recall Score: {:.2f}'.format(recall_score(y_train, y_pred)))\n",
        "print('Precision Score: {:.2f}'.format(precision_score(y_train, y_pred)))\n",
        "print('F1 Score: {:.2f}'.format(f1_score(y_train, y_pred)))\n",
        "print('Accuracy Score: {:.2f}'.format(accuracy_score(y_train, y_pred)))\n",
        "print('---' * 45)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---------------------------------------------------------------------------------------------------------------------------------------\n",
            "Overfitting: \n",
            "\n",
            "Recall Score: 0.92\n",
            "Precision Score: 0.99\n",
            "F1 Score: 0.95\n",
            "Accuracy Score: 0.96\n",
            "---------------------------------------------------------------------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ypZkVqqsWboo",
        "colab_type": "text"
      },
      "source": [
        "예측값을 실제 부정거래여부 칼럼인 Class와 비교하여 성능을 살펴보겠습니다.\n",
        "측정 지표로는 Accuracy(정확도), Recall(재현율), Precision(정밀도), F1-score 입니다.\n",
        "\n",
        "금융 거래에서는 정확도도 물론 중요하지만, 실제 부정거래를 부정거래로 예측하는 비율인 Recall(재현율) 값이 중요하게 여겨집니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wSa3hRNPWeNu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "867bd19d-bebb-4b3b-a99c-2bb5c032ed21"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
        "import itertools\n",
        "import numpy as np\n",
        "\n",
        "class_name = [0, 1]\n",
        "def plot_confusion_matrix(classes, pred, y_test, \n",
        "                          normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
        "    \n",
        "    cm = confusion_matrix(y_test, pred)\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=0)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "    \n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "\n",
        "# 평가\n",
        "print('confusion matrix\\n', confusion_matrix(pred_outlier, y))\n",
        "print('Accuracy: ',accuracy_score(pred_outlier, y))\n",
        "print('classification_report\\n', classification_report(pred_outlier, y))\n",
        "plot_confusion_matrix(class_name, pred_outlier, y, title='Isolation Forest')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "confusion matrix\n",
            " [[170862     22]\n",
            " [113453    470]]\n",
            "Accuracy:  0.6015722928158367\n",
            "classification_report\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.60      1.00      0.75    170884\n",
            "           1       0.96      0.00      0.01    113923\n",
            "\n",
            "    accuracy                           0.60    284807\n",
            "   macro avg       0.78      0.50      0.38    284807\n",
            "weighted avg       0.74      0.60      0.45    284807\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S66SOyT7vr+Pv018F4LGnX6VrcbvK/bsWt+WO//sGg390F2UbPtxLVxavJWs3s3n7Do46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\n7Ev86NcdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}