데이터분석_7일차_워크샵_분류_실습.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/younghoonNa/pythonDT/blob/main/%EB%8D%B0%EC%9D%B4%ED%84%B0%EB%B6%84%EC%84%9D_7%EC%9D%BC%EC%B0%A8_%EC%9B%8C%ED%81%AC%EC%83%B5_%EB%B6%84%EB%A5%98_%EC%8B%A4%EC%8A%B5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget http://49.247.133.7/creditcard.csv"
      ],
      "metadata": {
        "id": "rw1qUbDi4tfm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c682545-ab76-4146-d549-32069db36253"
      },
      "id": "rw1qUbDi4tfm",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-01-04 00:54:00--  http://49.247.133.7/creditcard.csv\n",
            "Connecting to 49.247.133.7:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 150828752 (144M) [text/csv]\n",
            "Saving to: ‘creditcard.csv’\n",
            "\n",
            "creditcard.csv      100%[===================>] 143.84M  17.0MB/s    in 9.6s    \n",
            "\n",
            "2022-01-04 00:54:10 (15.0 MB/s) - ‘creditcard.csv’ saved [150828752/150828752]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "71b2dc10",
      "metadata": {
        "id": "71b2dc10"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "f1cdafa4",
      "metadata": {
        "id": "f1cdafa4",
        "outputId": "71c6d0fd-344e-49d6-8c76-a29c1aad79bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284802</th>\n",
              "      <td>172786.0</td>\n",
              "      <td>-11.881118</td>\n",
              "      <td>10.071785</td>\n",
              "      <td>-9.834783</td>\n",
              "      <td>-2.066656</td>\n",
              "      <td>-5.364473</td>\n",
              "      <td>-2.606837</td>\n",
              "      <td>-4.918215</td>\n",
              "      <td>7.305334</td>\n",
              "      <td>1.914428</td>\n",
              "      <td>4.356170</td>\n",
              "      <td>-1.593105</td>\n",
              "      <td>2.711941</td>\n",
              "      <td>-0.689256</td>\n",
              "      <td>4.626942</td>\n",
              "      <td>-0.924459</td>\n",
              "      <td>1.107641</td>\n",
              "      <td>1.991691</td>\n",
              "      <td>0.510632</td>\n",
              "      <td>-0.682920</td>\n",
              "      <td>1.475829</td>\n",
              "      <td>0.213454</td>\n",
              "      <td>0.111864</td>\n",
              "      <td>1.014480</td>\n",
              "      <td>-0.509348</td>\n",
              "      <td>1.436807</td>\n",
              "      <td>0.250034</td>\n",
              "      <td>0.943651</td>\n",
              "      <td>0.823731</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284803</th>\n",
              "      <td>172787.0</td>\n",
              "      <td>-0.732789</td>\n",
              "      <td>-0.055080</td>\n",
              "      <td>2.035030</td>\n",
              "      <td>-0.738589</td>\n",
              "      <td>0.868229</td>\n",
              "      <td>1.058415</td>\n",
              "      <td>0.024330</td>\n",
              "      <td>0.294869</td>\n",
              "      <td>0.584800</td>\n",
              "      <td>-0.975926</td>\n",
              "      <td>-0.150189</td>\n",
              "      <td>0.915802</td>\n",
              "      <td>1.214756</td>\n",
              "      <td>-0.675143</td>\n",
              "      <td>1.164931</td>\n",
              "      <td>-0.711757</td>\n",
              "      <td>-0.025693</td>\n",
              "      <td>-1.221179</td>\n",
              "      <td>-1.545556</td>\n",
              "      <td>0.059616</td>\n",
              "      <td>0.214205</td>\n",
              "      <td>0.924384</td>\n",
              "      <td>0.012463</td>\n",
              "      <td>-1.016226</td>\n",
              "      <td>-0.606624</td>\n",
              "      <td>-0.395255</td>\n",
              "      <td>0.068472</td>\n",
              "      <td>-0.053527</td>\n",
              "      <td>24.79</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284804</th>\n",
              "      <td>172788.0</td>\n",
              "      <td>1.919565</td>\n",
              "      <td>-0.301254</td>\n",
              "      <td>-3.249640</td>\n",
              "      <td>-0.557828</td>\n",
              "      <td>2.630515</td>\n",
              "      <td>3.031260</td>\n",
              "      <td>-0.296827</td>\n",
              "      <td>0.708417</td>\n",
              "      <td>0.432454</td>\n",
              "      <td>-0.484782</td>\n",
              "      <td>0.411614</td>\n",
              "      <td>0.063119</td>\n",
              "      <td>-0.183699</td>\n",
              "      <td>-0.510602</td>\n",
              "      <td>1.329284</td>\n",
              "      <td>0.140716</td>\n",
              "      <td>0.313502</td>\n",
              "      <td>0.395652</td>\n",
              "      <td>-0.577252</td>\n",
              "      <td>0.001396</td>\n",
              "      <td>0.232045</td>\n",
              "      <td>0.578229</td>\n",
              "      <td>-0.037501</td>\n",
              "      <td>0.640134</td>\n",
              "      <td>0.265745</td>\n",
              "      <td>-0.087371</td>\n",
              "      <td>0.004455</td>\n",
              "      <td>-0.026561</td>\n",
              "      <td>67.88</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284805</th>\n",
              "      <td>172788.0</td>\n",
              "      <td>-0.240440</td>\n",
              "      <td>0.530483</td>\n",
              "      <td>0.702510</td>\n",
              "      <td>0.689799</td>\n",
              "      <td>-0.377961</td>\n",
              "      <td>0.623708</td>\n",
              "      <td>-0.686180</td>\n",
              "      <td>0.679145</td>\n",
              "      <td>0.392087</td>\n",
              "      <td>-0.399126</td>\n",
              "      <td>-1.933849</td>\n",
              "      <td>-0.962886</td>\n",
              "      <td>-1.042082</td>\n",
              "      <td>0.449624</td>\n",
              "      <td>1.962563</td>\n",
              "      <td>-0.608577</td>\n",
              "      <td>0.509928</td>\n",
              "      <td>1.113981</td>\n",
              "      <td>2.897849</td>\n",
              "      <td>0.127434</td>\n",
              "      <td>0.265245</td>\n",
              "      <td>0.800049</td>\n",
              "      <td>-0.163298</td>\n",
              "      <td>0.123205</td>\n",
              "      <td>-0.569159</td>\n",
              "      <td>0.546668</td>\n",
              "      <td>0.108821</td>\n",
              "      <td>0.104533</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284806</th>\n",
              "      <td>172792.0</td>\n",
              "      <td>-0.533413</td>\n",
              "      <td>-0.189733</td>\n",
              "      <td>0.703337</td>\n",
              "      <td>-0.506271</td>\n",
              "      <td>-0.012546</td>\n",
              "      <td>-0.649617</td>\n",
              "      <td>1.577006</td>\n",
              "      <td>-0.414650</td>\n",
              "      <td>0.486180</td>\n",
              "      <td>-0.915427</td>\n",
              "      <td>-1.040458</td>\n",
              "      <td>-0.031513</td>\n",
              "      <td>-0.188093</td>\n",
              "      <td>-0.084316</td>\n",
              "      <td>0.041333</td>\n",
              "      <td>-0.302620</td>\n",
              "      <td>-0.660377</td>\n",
              "      <td>0.167430</td>\n",
              "      <td>-0.256117</td>\n",
              "      <td>0.382948</td>\n",
              "      <td>0.261057</td>\n",
              "      <td>0.643078</td>\n",
              "      <td>0.376777</td>\n",
              "      <td>0.008797</td>\n",
              "      <td>-0.473649</td>\n",
              "      <td>-0.818267</td>\n",
              "      <td>-0.002415</td>\n",
              "      <td>0.013649</td>\n",
              "      <td>217.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>284807 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-24f16783-4806-434d-bc95-d3399bbe7560')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-24f16783-4806-434d-bc95-d3399bbe7560 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-24f16783-4806-434d-bc95-d3399bbe7560');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            Time         V1         V2  ...       V28  Amount  Class\n",
              "0            0.0  -1.359807  -0.072781  ... -0.021053  149.62      0\n",
              "1            0.0   1.191857   0.266151  ...  0.014724    2.69      0\n",
              "2            1.0  -1.358354  -1.340163  ... -0.059752  378.66      0\n",
              "3            1.0  -0.966272  -0.185226  ...  0.061458  123.50      0\n",
              "4            2.0  -1.158233   0.877737  ...  0.215153   69.99      0\n",
              "...          ...        ...        ...  ...       ...     ...    ...\n",
              "284802  172786.0 -11.881118  10.071785  ...  0.823731    0.77      0\n",
              "284803  172787.0  -0.732789  -0.055080  ... -0.053527   24.79      0\n",
              "284804  172788.0   1.919565  -0.301254  ... -0.026561   67.88      0\n",
              "284805  172788.0  -0.240440   0.530483  ...  0.104533   10.00      0\n",
              "284806  172792.0  -0.533413  -0.189733  ...  0.013649  217.00      0\n",
              "\n",
              "[284807 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# creditcard.csv을 읽어 df에 저장하세요\n",
        "df = pd.read_csv('creditcard.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 프레임의 shape을 조회하세요\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJVUK1UjOog7",
        "outputId": "4f47836c-db32-4169-81be-6c64462bda7d"
      },
      "id": "zJVUK1UjOog7",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 프레임의 결측치를 조회하세요\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wXHBwkqkOvQs",
        "outputId": "141d8616-dd46-453e-f729-f5dc76aea4b8"
      },
      "id": "wXHBwkqkOvQs",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      0\n",
              "V1        0\n",
              "V2        0\n",
              "V3        0\n",
              "V4        0\n",
              "V5        0\n",
              "V6        0\n",
              "V7        0\n",
              "V8        0\n",
              "V9        0\n",
              "V10       0\n",
              "V11       0\n",
              "V12       0\n",
              "V13       0\n",
              "V14       0\n",
              "V15       0\n",
              "V16       0\n",
              "V17       0\n",
              "V18       0\n",
              "V19       0\n",
              "V20       0\n",
              "V21       0\n",
              "V22       0\n",
              "V23       0\n",
              "V24       0\n",
              "V25       0\n",
              "V26       0\n",
              "V27       0\n",
              "V28       0\n",
              "Amount    0\n",
              "Class     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 프레임의 정보를 출력하세요\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dnYV2LT_O0i-",
        "outputId": "3d278b82-71d7-477f-bd50-7b03dec787ee"
      },
      "id": "dnYV2LT_O0i-",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284807 entries, 0 to 284806\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   Time    284807 non-null  float64\n",
            " 1   V1      284807 non-null  float64\n",
            " 2   V2      284807 non-null  float64\n",
            " 3   V3      284807 non-null  float64\n",
            " 4   V4      284807 non-null  float64\n",
            " 5   V5      284807 non-null  float64\n",
            " 6   V6      284807 non-null  float64\n",
            " 7   V7      284807 non-null  float64\n",
            " 8   V8      284807 non-null  float64\n",
            " 9   V9      284807 non-null  float64\n",
            " 10  V10     284807 non-null  float64\n",
            " 11  V11     284807 non-null  float64\n",
            " 12  V12     284807 non-null  float64\n",
            " 13  V13     284807 non-null  float64\n",
            " 14  V14     284807 non-null  float64\n",
            " 15  V15     284807 non-null  float64\n",
            " 16  V16     284807 non-null  float64\n",
            " 17  V17     284807 non-null  float64\n",
            " 18  V18     284807 non-null  float64\n",
            " 19  V19     284807 non-null  float64\n",
            " 20  V20     284807 non-null  float64\n",
            " 21  V21     284807 non-null  float64\n",
            " 22  V22     284807 non-null  float64\n",
            " 23  V23     284807 non-null  float64\n",
            " 24  V24     284807 non-null  float64\n",
            " 25  V25     284807 non-null  float64\n",
            " 26  V26     284807 non-null  float64\n",
            " 27  V27     284807 non-null  float64\n",
            " 28  V28     284807 non-null  float64\n",
            " 29  Amount  284807 non-null  float64\n",
            " 30  Class   284807 non-null  int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 67.4 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 프레임의 기초 통계 분석을 진행하세요\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "c1CtN1AVPFc_",
        "outputId": "2b197c14-3ac4-423d-cd15-559976aa7956"
      },
      "id": "c1CtN1AVPFc_",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b58fae75-3385-41f6-bc89-8ca40785e712\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "    }\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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b58fae75-3385-41f6-bc89-8ca40785e712')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b58fae75-3385-41f6-bc89-8ca40785e712 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b58fae75-3385-41f6-bc89-8ca40785e712');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "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": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# var() 함수를 이용하여 데이터 프레임의 컬럼별 분산값을 조회하세요\n",
        "df.var()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nat5Xt20PLLJ",
        "outputId": "6f77dc74-d0bc-4542-e5cb-15d6a70be9ec"
      },
      "id": "Nat5Xt20PLLJ",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      2.255124e+09\n",
              "V1        3.836489e+00\n",
              "V2        2.726820e+00\n",
              "V3        2.299029e+00\n",
              "V4        2.004684e+00\n",
              "V5        1.905081e+00\n",
              "V6        1.774946e+00\n",
              "V7        1.530401e+00\n",
              "V8        1.426479e+00\n",
              "V9        1.206992e+00\n",
              "V10       1.185594e+00\n",
              "V11       1.041855e+00\n",
              "V12       9.984034e-01\n",
              "V13       9.905708e-01\n",
              "V14       9.189055e-01\n",
              "V15       8.378034e-01\n",
              "V16       7.678191e-01\n",
              "V17       7.213734e-01\n",
              "V18       7.025394e-01\n",
              "V19       6.626619e-01\n",
              "V20       5.943254e-01\n",
              "V21       5.395255e-01\n",
              "V22       5.266428e-01\n",
              "V23       3.899507e-01\n",
              "V24       3.668084e-01\n",
              "V25       2.717308e-01\n",
              "V26       2.325429e-01\n",
              "V27       1.629192e-01\n",
              "V28       1.089550e-01\n",
              "Amount    6.256007e+04\n",
              "Class     1.724507e-03\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "dDbRHCZnPRez",
        "outputId": "467c84e3-bf86-4daf-dee1-18b8d63356b6"
      },
      "id": "dDbRHCZnPRez",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-308159ec-fa69-4977-af92-1d49a53f413f\">\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284802</th>\n",
              "      <td>172786.0</td>\n",
              "      <td>-11.881118</td>\n",
              "      <td>10.071785</td>\n",
              "      <td>-9.834783</td>\n",
              "      <td>-2.066656</td>\n",
              "      <td>-5.364473</td>\n",
              "      <td>-2.606837</td>\n",
              "      <td>-4.918215</td>\n",
              "      <td>7.305334</td>\n",
              "      <td>1.914428</td>\n",
              "      <td>4.356170</td>\n",
              "      <td>-1.593105</td>\n",
              "      <td>2.711941</td>\n",
              "      <td>-0.689256</td>\n",
              "      <td>4.626942</td>\n",
              "      <td>-0.924459</td>\n",
              "      <td>1.107641</td>\n",
              "      <td>1.991691</td>\n",
              "      <td>0.510632</td>\n",
              "      <td>-0.682920</td>\n",
              "      <td>1.475829</td>\n",
              "      <td>0.213454</td>\n",
              "      <td>0.111864</td>\n",
              "      <td>1.014480</td>\n",
              "      <td>-0.509348</td>\n",
              "      <td>1.436807</td>\n",
              "      <td>0.250034</td>\n",
              "      <td>0.943651</td>\n",
              "      <td>0.823731</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284803</th>\n",
              "      <td>172787.0</td>\n",
              "      <td>-0.732789</td>\n",
              "      <td>-0.055080</td>\n",
              "      <td>2.035030</td>\n",
              "      <td>-0.738589</td>\n",
              "      <td>0.868229</td>\n",
              "      <td>1.058415</td>\n",
              "      <td>0.024330</td>\n",
              "      <td>0.294869</td>\n",
              "      <td>0.584800</td>\n",
              "      <td>-0.975926</td>\n",
              "      <td>-0.150189</td>\n",
              "      <td>0.915802</td>\n",
              "      <td>1.214756</td>\n",
              "      <td>-0.675143</td>\n",
              "      <td>1.164931</td>\n",
              "      <td>-0.711757</td>\n",
              "      <td>-0.025693</td>\n",
              "      <td>-1.221179</td>\n",
              "      <td>-1.545556</td>\n",
              "      <td>0.059616</td>\n",
              "      <td>0.214205</td>\n",
              "      <td>0.924384</td>\n",
              "      <td>0.012463</td>\n",
              "      <td>-1.016226</td>\n",
              "      <td>-0.606624</td>\n",
              "      <td>-0.395255</td>\n",
              "      <td>0.068472</td>\n",
              "      <td>-0.053527</td>\n",
              "      <td>24.79</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284804</th>\n",
              "      <td>172788.0</td>\n",
              "      <td>1.919565</td>\n",
              "      <td>-0.301254</td>\n",
              "      <td>-3.249640</td>\n",
              "      <td>-0.557828</td>\n",
              "      <td>2.630515</td>\n",
              "      <td>3.031260</td>\n",
              "      <td>-0.296827</td>\n",
              "      <td>0.708417</td>\n",
              "      <td>0.432454</td>\n",
              "      <td>-0.484782</td>\n",
              "      <td>0.411614</td>\n",
              "      <td>0.063119</td>\n",
              "      <td>-0.183699</td>\n",
              "      <td>-0.510602</td>\n",
              "      <td>1.329284</td>\n",
              "      <td>0.140716</td>\n",
              "      <td>0.313502</td>\n",
              "      <td>0.395652</td>\n",
              "      <td>-0.577252</td>\n",
              "      <td>0.001396</td>\n",
              "      <td>0.232045</td>\n",
              "      <td>0.578229</td>\n",
              "      <td>-0.037501</td>\n",
              "      <td>0.640134</td>\n",
              "      <td>0.265745</td>\n",
              "      <td>-0.087371</td>\n",
              "      <td>0.004455</td>\n",
              "      <td>-0.026561</td>\n",
              "      <td>67.88</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284805</th>\n",
              "      <td>172788.0</td>\n",
              "      <td>-0.240440</td>\n",
              "      <td>0.530483</td>\n",
              "      <td>0.702510</td>\n",
              "      <td>0.689799</td>\n",
              "      <td>-0.377961</td>\n",
              "      <td>0.623708</td>\n",
              "      <td>-0.686180</td>\n",
              "      <td>0.679145</td>\n",
              "      <td>0.392087</td>\n",
              "      <td>-0.399126</td>\n",
              "      <td>-1.933849</td>\n",
              "      <td>-0.962886</td>\n",
              "      <td>-1.042082</td>\n",
              "      <td>0.449624</td>\n",
              "      <td>1.962563</td>\n",
              "      <td>-0.608577</td>\n",
              "      <td>0.509928</td>\n",
              "      <td>1.113981</td>\n",
              "      <td>2.897849</td>\n",
              "      <td>0.127434</td>\n",
              "      <td>0.265245</td>\n",
              "      <td>0.800049</td>\n",
              "      <td>-0.163298</td>\n",
              "      <td>0.123205</td>\n",
              "      <td>-0.569159</td>\n",
              "      <td>0.546668</td>\n",
              "      <td>0.108821</td>\n",
              "      <td>0.104533</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284806</th>\n",
              "      <td>172792.0</td>\n",
              "      <td>-0.533413</td>\n",
              "      <td>-0.189733</td>\n",
              "      <td>0.703337</td>\n",
              "      <td>-0.506271</td>\n",
              "      <td>-0.012546</td>\n",
              "      <td>-0.649617</td>\n",
              "      <td>1.577006</td>\n",
              "      <td>-0.414650</td>\n",
              "      <td>0.486180</td>\n",
              "      <td>-0.915427</td>\n",
              "      <td>-1.040458</td>\n",
              "      <td>-0.031513</td>\n",
              "      <td>-0.188093</td>\n",
              "      <td>-0.084316</td>\n",
              "      <td>0.041333</td>\n",
              "      <td>-0.302620</td>\n",
              "      <td>-0.660377</td>\n",
              "      <td>0.167430</td>\n",
              "      <td>-0.256117</td>\n",
              "      <td>0.382948</td>\n",
              "      <td>0.261057</td>\n",
              "      <td>0.643078</td>\n",
              "      <td>0.376777</td>\n",
              "      <td>0.008797</td>\n",
              "      <td>-0.473649</td>\n",
              "      <td>-0.818267</td>\n",
              "      <td>-0.002415</td>\n",
              "      <td>0.013649</td>\n",
              "      <td>217.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>284807 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-308159ec-fa69-4977-af92-1d49a53f413f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-308159ec-fa69-4977-af92-1d49a53f413f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-308159ec-fa69-4977-af92-1d49a53f413f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            Time         V1         V2  ...       V28  Amount  Class\n",
              "0            0.0  -1.359807  -0.072781  ... -0.021053  149.62      0\n",
              "1            0.0   1.191857   0.266151  ...  0.014724    2.69      0\n",
              "2            1.0  -1.358354  -1.340163  ... -0.059752  378.66      0\n",
              "3            1.0  -0.966272  -0.185226  ...  0.061458  123.50      0\n",
              "4            2.0  -1.158233   0.877737  ...  0.215153   69.99      0\n",
              "...          ...        ...        ...  ...       ...     ...    ...\n",
              "284802  172786.0 -11.881118  10.071785  ...  0.823731    0.77      0\n",
              "284803  172787.0  -0.732789  -0.055080  ... -0.053527   24.79      0\n",
              "284804  172788.0   1.919565  -0.301254  ... -0.026561   67.88      0\n",
              "284805  172788.0  -0.240440   0.530483  ...  0.104533   10.00      0\n",
              "284806  172792.0  -0.533413  -0.189733  ...  0.013649  217.00      0\n",
              "\n",
              "[284807 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Class 컬럼의 값별 개수를 계산하세요\n",
        "df['Class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9XF-lKGPdg2",
        "outputId": "68d65b03-df26-44ed-8d0f-ea73fce89e74"
      },
      "id": "q9XF-lKGPdg2",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    284315\n",
              "1       492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Class 컬럼의 값별 비율을 계산하세요\n",
        "df['Class'].value_counts(normalize = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqJgs7iFPuGC",
        "outputId": "a16fea65-1c45-482a-f7d9-f3d027b17ae7"
      },
      "id": "dqJgs7iFPuGC",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.998273\n",
              "1    0.001727\n",
              "Name: Class, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 해당 결과를 시각화하세요\n",
        "df['Class'].value_counts(normalize = True).plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Epun2ZayP3f3",
        "outputId": "79a0dcaa-7ab8-4745-d79c-3f7ac7f7da67"
      },
      "id": "Epun2ZayP3f3",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80af605e10>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "8aec1afe",
      "metadata": {
        "id": "8aec1afe",
        "outputId": "4d153c36-e7ae-4bb8-f34d-7075593ac5e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f80af4deb90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80af03e2d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80af0748d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80af02bed0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aefee510>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aefa2b10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80af4ccf50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aef19050>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aef224d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aeed7bd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aee47290>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aedfc890>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80aee34e90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aedf64d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aedadad0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aed70110>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aed27710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aecdcd10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80aec9f350>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aec57950>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aec0df50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aebcf590>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aeb85b90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aeb481d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80aeb007d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aeb36dd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aeaf8410>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aeaaea10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aea72050>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aea27650>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80ae9dec50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ae9ac710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ae958890>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ae926350>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ae8d24d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ae888ad0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 36 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# 데이터 프레임의 컬럼별 분포를 확인하세요\n",
        "# hist(figsize=(15, 15), bins=20) 함수를 사용하세요\n",
        "df.hist(figsize=(15, 15), bins=20)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 정상 데이터(Class 0)만 추출 후 컬럼별 분포를 확인하세요\n",
        "# hist(figsize=(15, 15), bins=20) 함수를 사용하세요\n",
        "df.loc[df['Class'] ==0].hist(figsize=(15, 15), bins=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "i4SbHqUwSD6T",
        "outputId": "50a4aed4-015e-4a8c-ceae-c1f32a670e78"
      },
      "id": "i4SbHqUwSD6T",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f80addecdd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80adf33150>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac542550>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac555a50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac569f50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac504490>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac4b8990>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac4eee90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac484350>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac43d950>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac41ddd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac3e0310>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac394810>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac34cd10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac30f250>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac2c4750>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac27bc50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac23f190>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac274690>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac22ab90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac1ed0d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac1a25d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ae04af90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aded9310>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80ae792210>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ae03f590>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac093290>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ac045890>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80abff9e90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80abfb84d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80abfeead0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80abfb3110>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80abf69710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80abf22d10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80abee2350>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80abe9a950>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 36 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 비정상 데이터(Class 1)만 추출 후 컬럼별 분포를 확인하세요\n",
        "# hist(figsize=(15, 15), bins=20) 함수를 사용하세요\n",
        "df.loc[df['Class'] == 1].hist(figsize=(15, 15), bins=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pkwzTjVaS58X",
        "outputId": "60d84e47-f648-4d14-db5c-2f782e04dd4b"
      },
      "id": "pkwzTjVaS58X",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab3c2910>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab3f9c50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab351a50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab35fad0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab2f8050>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab30f510>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab2bfa10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab2f4f10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab28c3d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab2419d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab223e50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab1e5390>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab19b890>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab151d90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab1142d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab0ca7d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab07fcd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab04d690>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80aaff9710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80ab02fc10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aaff3150>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aafa8650>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aaf69fd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aaf22090>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80aaed6590>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aae8ea90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aae41f90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aae064d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aadbb9d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aadf3ed0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f80aadb4410>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aad6c910>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aad22e10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aace1350>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aac9b850>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80aac67210>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 36 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 각 컬럼별 BoxPlot을 그려보세요\n",
        "# subplots=True, layout=(7, 5), figsize=(15,21) 사용\n",
        "k=1\n",
        "plt.figure(figsize=(15,15))\n",
        "for i in df.columns:\n",
        "\n",
        "  plt.subplot(10,5,k)\n",
        "  plt.boxplot(df[i])\n",
        "  k+=1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "P6TObzF_TVmZ",
        "outputId": "0fa732eb-00d5-44fd-b10e-a3c9034b4f61"
      },
      "id": "P6TObzF_TVmZ",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 31 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Amount 컬럼의 분포를 시각화하세요\n",
        "# hist 함수 이용\n",
        "df['Amount'].plot(kind='hist')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "qW45CZPIbGud",
        "outputId": "d0701528-98bb-4e92-d77e-568d4a59f8f8"
      },
      "id": "qW45CZPIbGud",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80a7d8be10>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# np.log1p 함수를 이용하여 Amount_log 컬럼을 생성하세요\n",
        "df['Amount_log'] = np.log1p(df['Amount'])\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "TreRf3MLbMBx",
        "outputId": "3715f18a-54f7-43ca-8b6b-d137f44e8681"
      },
      "id": "TreRf3MLbMBx",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b4294841-42e9-4119-a891-8b9a47eb2444\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "      <th>Amount_log</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",
              "      <td>5.014760</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",
              "      <td>1.305626</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",
              "      <td>5.939276</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",
              "      <td>4.824306</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",
              "      <td>4.262539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284802</th>\n",
              "      <td>172786.0</td>\n",
              "      <td>-11.881118</td>\n",
              "      <td>10.071785</td>\n",
              "      <td>-9.834783</td>\n",
              "      <td>-2.066656</td>\n",
              "      <td>-5.364473</td>\n",
              "      <td>-2.606837</td>\n",
              "      <td>-4.918215</td>\n",
              "      <td>7.305334</td>\n",
              "      <td>1.914428</td>\n",
              "      <td>4.356170</td>\n",
              "      <td>-1.593105</td>\n",
              "      <td>2.711941</td>\n",
              "      <td>-0.689256</td>\n",
              "      <td>4.626942</td>\n",
              "      <td>-0.924459</td>\n",
              "      <td>1.107641</td>\n",
              "      <td>1.991691</td>\n",
              "      <td>0.510632</td>\n",
              "      <td>-0.682920</td>\n",
              "      <td>1.475829</td>\n",
              "      <td>0.213454</td>\n",
              "      <td>0.111864</td>\n",
              "      <td>1.014480</td>\n",
              "      <td>-0.509348</td>\n",
              "      <td>1.436807</td>\n",
              "      <td>0.250034</td>\n",
              "      <td>0.943651</td>\n",
              "      <td>0.823731</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0</td>\n",
              "      <td>0.570980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284803</th>\n",
              "      <td>172787.0</td>\n",
              "      <td>-0.732789</td>\n",
              "      <td>-0.055080</td>\n",
              "      <td>2.035030</td>\n",
              "      <td>-0.738589</td>\n",
              "      <td>0.868229</td>\n",
              "      <td>1.058415</td>\n",
              "      <td>0.024330</td>\n",
              "      <td>0.294869</td>\n",
              "      <td>0.584800</td>\n",
              "      <td>-0.975926</td>\n",
              "      <td>-0.150189</td>\n",
              "      <td>0.915802</td>\n",
              "      <td>1.214756</td>\n",
              "      <td>-0.675143</td>\n",
              "      <td>1.164931</td>\n",
              "      <td>-0.711757</td>\n",
              "      <td>-0.025693</td>\n",
              "      <td>-1.221179</td>\n",
              "      <td>-1.545556</td>\n",
              "      <td>0.059616</td>\n",
              "      <td>0.214205</td>\n",
              "      <td>0.924384</td>\n",
              "      <td>0.012463</td>\n",
              "      <td>-1.016226</td>\n",
              "      <td>-0.606624</td>\n",
              "      <td>-0.395255</td>\n",
              "      <td>0.068472</td>\n",
              "      <td>-0.053527</td>\n",
              "      <td>24.79</td>\n",
              "      <td>0</td>\n",
              "      <td>3.249987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284804</th>\n",
              "      <td>172788.0</td>\n",
              "      <td>1.919565</td>\n",
              "      <td>-0.301254</td>\n",
              "      <td>-3.249640</td>\n",
              "      <td>-0.557828</td>\n",
              "      <td>2.630515</td>\n",
              "      <td>3.031260</td>\n",
              "      <td>-0.296827</td>\n",
              "      <td>0.708417</td>\n",
              "      <td>0.432454</td>\n",
              "      <td>-0.484782</td>\n",
              "      <td>0.411614</td>\n",
              "      <td>0.063119</td>\n",
              "      <td>-0.183699</td>\n",
              "      <td>-0.510602</td>\n",
              "      <td>1.329284</td>\n",
              "      <td>0.140716</td>\n",
              "      <td>0.313502</td>\n",
              "      <td>0.395652</td>\n",
              "      <td>-0.577252</td>\n",
              "      <td>0.001396</td>\n",
              "      <td>0.232045</td>\n",
              "      <td>0.578229</td>\n",
              "      <td>-0.037501</td>\n",
              "      <td>0.640134</td>\n",
              "      <td>0.265745</td>\n",
              "      <td>-0.087371</td>\n",
              "      <td>0.004455</td>\n",
              "      <td>-0.026561</td>\n",
              "      <td>67.88</td>\n",
              "      <td>0</td>\n",
              "      <td>4.232366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284805</th>\n",
              "      <td>172788.0</td>\n",
              "      <td>-0.240440</td>\n",
              "      <td>0.530483</td>\n",
              "      <td>0.702510</td>\n",
              "      <td>0.689799</td>\n",
              "      <td>-0.377961</td>\n",
              "      <td>0.623708</td>\n",
              "      <td>-0.686180</td>\n",
              "      <td>0.679145</td>\n",
              "      <td>0.392087</td>\n",
              "      <td>-0.399126</td>\n",
              "      <td>-1.933849</td>\n",
              "      <td>-0.962886</td>\n",
              "      <td>-1.042082</td>\n",
              "      <td>0.449624</td>\n",
              "      <td>1.962563</td>\n",
              "      <td>-0.608577</td>\n",
              "      <td>0.509928</td>\n",
              "      <td>1.113981</td>\n",
              "      <td>2.897849</td>\n",
              "      <td>0.127434</td>\n",
              "      <td>0.265245</td>\n",
              "      <td>0.800049</td>\n",
              "      <td>-0.163298</td>\n",
              "      <td>0.123205</td>\n",
              "      <td>-0.569159</td>\n",
              "      <td>0.546668</td>\n",
              "      <td>0.108821</td>\n",
              "      <td>0.104533</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0</td>\n",
              "      <td>2.397895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284806</th>\n",
              "      <td>172792.0</td>\n",
              "      <td>-0.533413</td>\n",
              "      <td>-0.189733</td>\n",
              "      <td>0.703337</td>\n",
              "      <td>-0.506271</td>\n",
              "      <td>-0.012546</td>\n",
              "      <td>-0.649617</td>\n",
              "      <td>1.577006</td>\n",
              "      <td>-0.414650</td>\n",
              "      <td>0.486180</td>\n",
              "      <td>-0.915427</td>\n",
              "      <td>-1.040458</td>\n",
              "      <td>-0.031513</td>\n",
              "      <td>-0.188093</td>\n",
              "      <td>-0.084316</td>\n",
              "      <td>0.041333</td>\n",
              "      <td>-0.302620</td>\n",
              "      <td>-0.660377</td>\n",
              "      <td>0.167430</td>\n",
              "      <td>-0.256117</td>\n",
              "      <td>0.382948</td>\n",
              "      <td>0.261057</td>\n",
              "      <td>0.643078</td>\n",
              "      <td>0.376777</td>\n",
              "      <td>0.008797</td>\n",
              "      <td>-0.473649</td>\n",
              "      <td>-0.818267</td>\n",
              "      <td>-0.002415</td>\n",
              "      <td>0.013649</td>\n",
              "      <td>217.00</td>\n",
              "      <td>0</td>\n",
              "      <td>5.384495</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>284807 rows × 32 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4294841-42e9-4119-a891-8b9a47eb2444')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b4294841-42e9-4119-a891-8b9a47eb2444 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b4294841-42e9-4119-a891-8b9a47eb2444');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            Time         V1         V2  ...  Amount  Class  Amount_log\n",
              "0            0.0  -1.359807  -0.072781  ...  149.62      0    5.014760\n",
              "1            0.0   1.191857   0.266151  ...    2.69      0    1.305626\n",
              "2            1.0  -1.358354  -1.340163  ...  378.66      0    5.939276\n",
              "3            1.0  -0.966272  -0.185226  ...  123.50      0    4.824306\n",
              "4            2.0  -1.158233   0.877737  ...   69.99      0    4.262539\n",
              "...          ...        ...        ...  ...     ...    ...         ...\n",
              "284802  172786.0 -11.881118  10.071785  ...    0.77      0    0.570980\n",
              "284803  172787.0  -0.732789  -0.055080  ...   24.79      0    3.249987\n",
              "284804  172788.0   1.919565  -0.301254  ...   67.88      0    4.232366\n",
              "284805  172788.0  -0.240440   0.530483  ...   10.00      0    2.397895\n",
              "284806  172792.0  -0.533413  -0.189733  ...  217.00      0    5.384495\n",
              "\n",
              "[284807 rows x 32 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "3bda1061",
      "metadata": {
        "id": "3bda1061",
        "outputId": "da007503-07fa-4d9c-d159-e537fda7f816",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80a9a099d0>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Amount_log 컬럼의 분포를 시각화하세요\n",
        "# hist 함수 이용\n",
        "df['Amount_log'].plot(kind='hist', color='red')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df['Class'] == 1, 'Time'].plot(kind='hist', color='red', bins = 50, figsize=(10,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "6Ut35Wn8fVa8",
        "outputId": "628e5906-ee95-49e5-9b94-15d21781140f"
      },
      "id": "6Ut35Wn8fVa8",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80a9a0ee10>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wkmUcvjw5yftqNiR5fJK3dff/WEIdAADD2PZQ1t2fT/Ko7d4uAMDITIkBADAAoQwAYADLnNGfZVuzS40BYGRGygAABiCUAQAMQCgDABiAUAYAMAChDABgAEIZAMAAhDIAgAGYp4zlMEcaW2G/WV3z/tt1L7YOGJiRMgCAAQhlAAADEMoAAAYglAEADEAoAwAYgFAGADAAoQwAYADmKTvA/EcAy+d3MWvMSBkAwACEMgCAAQhlAAADEMoAAAYglAEADEAoAwAYgFAGADAA85QBsHXmFYNjxkgZAMAAhDIAgAEIZQAAA1hKKKuqc6rqs1V1U1VdsIwaAABGsu2hrKqOS/L7SZ6R5MwkL6yqM7e7DgCAkSxjpOyxSW7q7s9393eSvCPJuUuoAwBgGMsIZacm+T8b7n9xagMAWFvDzlNWVecnOX+6+82q+uwCN3dSkq8s8PV3En01H/00P301v++/r9ZjXjH71PzWr6+29hk42n76wa1sZBmh7JYkD9tw/6FT291090VJLtqOgqpqb3fv2Y5trTp9NR/9ND99NT99NR/9ND99NZ/t6qdlHL78sySPqKrTq+peSV6Q5LIl1AEAMIxtHynr7jur6qVJPpDkuCQXd/f1210HAMBIlnJOWXdfnuTyZWx7E9tymHSH0Ffz0U/z01fz01fz0U/z01fz2Z7Tqbp7O7YDAMBh+JolAIABrH0oW8evfKqqh1XVR6rq01V1fVW9bGq/sKpuqap90+2ZG9b5jamPPltVT9/Qfsj+my7kuHJqv3S6qGPlVNXNVXXt1B97p7YTq+qKqrpx+nnC1F5V9bvTe/5UVZ294XXOm55/Y1Wdt6H9x6bXv2ladyXnK6iqR27Yb/ZV1R1V9XL71ExVXVxVt1fVdRvaFr4fbbaNUW3ST/+hqj4z9cX7quohU/vuqvrWhn3r9RvWOar+OFyfj2qTvlr4562q7j3dv2l6fPf2vOOt26SvLt3QTzdX1b6pfbn7VXev7S2zCw0+l+SMJPdK8skkZy67rm1436ckOXtafmCSP8/sK68uTPKvDvH8M6e+uXeS06c+O+5w/ZfknUleMC2/Psm/XPb73mJf3ZzkpIPa/n2SC6blC5K8alp+ZpI/SVJJHpfkyqn9xCSfn36eMC2fMD121fTcmtZ9xrLf8zHos+OSfDmzeXrsU7N6fzzJ2Umu2879aLNtjHrbpJ+eluT4aflVG/pp98bnHfQ6R9Ufm/X5yLdN+mrhn7ckv5Lk9dPyC5Jcuuy+2EpfHfT4q5P81gj71bqPlK3lVz51963dfc20/I0kN+Tw36pwbpJ3dPe3u/svktyUWd8dsv+mvx6elOTd0/qXJHnOYt7NUpyb2XtK7v7ezk3y5p75eJKHVNUpSZ6e5Iru/lp3/2WSK5KcMz32oO7+eM8+wW/OzuinJyf5XHd/4TDPWat9qrs/luRrBzVvx3602TaGdKh+6u4Pdved092PZza35aa22B+b9fmwNtmnNnMsP28b+/DdSZ58YMRoVIfrq6n25yd5++FeY7v2q3UPZWv/lU/T0POjk1w5Nb10Gma9eMOhjs36abP2v5Pk6xt+ka5yv3aSD1bV1TX7lokkObm7b52Wv5zk5Gn5aPvp1Gn54PZV94Lc/RecferQtmM/2mwbq+qXMht5OOD0qvpEVX20qp44tW2lP3bS/wWL/rx9b53p8b+anr+qnpjktu6+cUPb0vardQ9la62qHpDkPUle3t13JHldkocnOSvJrZkN6a67J3T32UmekeQlVfXjGx+c/mJyCfNkOu/k2UneNTXZp+awHfvRqu+rVfXKJHcmeevUdGuS07r70Ul+NcnbqupB877eqvfHJnzejt4Lc/c/Ipe6X617KJvrK592oqq6Z2aB7K3d/d4k6e7buvuu7v5ukjdkNrSdbN5Pm7V/NbNh2uMPal853X3L9PP2JO/LrE9uOzAEPf28fXr60fbTLbn7oZiV7acNnpHkmu6+LbFPHcF27EebbWOlVNU/T/JPkvzs9J9epkNxX52Wr87s3Ki/l631x474v2CbPm/fW2d6/MHT81fOVP9PJ7n0QNuy96t1D2Vr+ZVP0zH0Nya5obtfs6F947Hu5yY5cKXKZUleMF11c3qSR2R2wuMh+2/6pfmRJM+b1j8vyfsX+Z4WoaruX1UPPLCc2QnH12XWHweufNv43i5L8gvTFTePS/JX05D2B5I8rapOmA4nPC3JB6bH7qiqx03/Jr+QFeyng9ztr0771GFtx3602TZWRlWdk+RfJ3l2d//NhvZdVXXctHxGZvvQ57fYH5v1+UrZps/bxj58XpL/eSAor6CnJPlMd3/vsOTS96uDz/xft1tmV0f8eWZp+JXLrmeb3vMTMhte/VSSfdPtmUnekuTaqf2yJKdsWOeVUx99NhuuENys/zK7mueqzE4ofVeSey/7fW+hn87I7GqkTya5/sD7y+z8iQ8nuTHJh5KcOLVXkt+f+uLaJHs2vNYvTX1xU5Jf3NC+J7NfnJ9L8l8yTei8irck98/sL+YHb2izT81qf3tmh0X+NrPzSl60HfvRZtsY9bZJP92U2Xk5B35XHbjy72emz+W+JNckedZW++NwfT7qbZO+WvjnLcl9pvs3TY+fsey+2EpfTe1vSvIvDnruUvcrM/oDAAxg3Q9fAgAMQSgDABiAUAYAMAChDABgAEIZAMAAhDIAgAEIZQAAAxDKAAAG8H8BtesMC5G2X4YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df['Class'] == 0, 'Time'].plot(kind='hist', color='blue', bins = 50, figsize=(10,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "gmoqPsCBgHyT",
        "outputId": "6b5978bd-aa09-4b26-f1b7-cfe57068b934"
      },
      "id": "gmoqPsCBgHyT",
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80a8b3a6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "13272bf2",
      "metadata": {
        "id": "13272bf2",
        "outputId": "b035e7c9-c538-4feb-9d60-f9ac01fcd7a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80a90d8890>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# class 컬럼의 값인 1인 데이터 프레임을 추출 후 Time 컬럼을 scatter 함수를 이용하여 시각화 하세요.\n",
        "df.loc[df['Class'] == 1, 'Time'].plot(kind='hist', color='red')\n",
        "df.loc[df['Class'] == 0, 'Time'].plot(kind='hist', color='blue')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sc_df_one = pd.DataFrame(df.loc[df['Class'] == 1, ['Time', 'Amount']])\n",
        "sc_df_one"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "R9tavxlIABQd",
        "outputId": "62a60857-7da6-410c-82a2-96b0164193e1"
      },
      "id": "R9tavxlIABQd",
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6e79e5e2-c939-45f9-9619-773f56d313da\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>541</th>\n",
              "      <td>406.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>623</th>\n",
              "      <td>472.0</td>\n",
              "      <td>529.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4920</th>\n",
              "      <td>4462.0</td>\n",
              "      <td>239.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6108</th>\n",
              "      <td>6986.0</td>\n",
              "      <td>59.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6329</th>\n",
              "      <td>7519.0</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279863</th>\n",
              "      <td>169142.0</td>\n",
              "      <td>390.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280143</th>\n",
              "      <td>169347.0</td>\n",
              "      <td>0.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280149</th>\n",
              "      <td>169351.0</td>\n",
              "      <td>77.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281144</th>\n",
              "      <td>169966.0</td>\n",
              "      <td>245.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281674</th>\n",
              "      <td>170348.0</td>\n",
              "      <td>42.53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>492 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e79e5e2-c939-45f9-9619-773f56d313da')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6e79e5e2-c939-45f9-9619-773f56d313da button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6e79e5e2-c939-45f9-9619-773f56d313da');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            Time  Amount\n",
              "541        406.0    0.00\n",
              "623        472.0  529.00\n",
              "4920      4462.0  239.93\n",
              "6108      6986.0   59.00\n",
              "6329      7519.0    1.00\n",
              "...          ...     ...\n",
              "279863  169142.0  390.00\n",
              "280143  169347.0    0.76\n",
              "280149  169351.0   77.89\n",
              "281144  169966.0  245.00\n",
              "281674  170348.0   42.53\n",
              "\n",
              "[492 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(sc_df_one['Time'], sc_df_one['Amount'], color = 'red')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "RFFpBq2w_5C_",
        "outputId": "add69761-a276-4881-8e4f-2b30cb75798a"
      },
      "id": "RFFpBq2w_5C_",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f80a8ae49d0>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sc_df_zero = pd.DataFrame(df.loc[df['Class'] == 0, ['Time', 'Amount']])\n",
        "sc_df_zero"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "B32zePMFBCXq",
        "outputId": "349f4710-b62e-4f5f-f0f3-42624c1c27aa"
      },
      "id": "B32zePMFBCXq",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1a1045ad-37d2-461f-b939-95aaeaab841c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>149.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>378.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>123.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>69.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284802</th>\n",
              "      <td>172786.0</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284803</th>\n",
              "      <td>172787.0</td>\n",
              "      <td>24.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284804</th>\n",
              "      <td>172788.0</td>\n",
              "      <td>67.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284805</th>\n",
              "      <td>172788.0</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284806</th>\n",
              "      <td>172792.0</td>\n",
              "      <td>217.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>284315 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a1045ad-37d2-461f-b939-95aaeaab841c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1a1045ad-37d2-461f-b939-95aaeaab841c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1a1045ad-37d2-461f-b939-95aaeaab841c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            Time  Amount\n",
              "0            0.0  149.62\n",
              "1            0.0    2.69\n",
              "2            1.0  378.66\n",
              "3            1.0  123.50\n",
              "4            2.0   69.99\n",
              "...          ...     ...\n",
              "284802  172786.0    0.77\n",
              "284803  172787.0   24.79\n",
              "284804  172788.0   67.88\n",
              "284805  172788.0   10.00\n",
              "284806  172792.0  217.00\n",
              "\n",
              "[284315 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(sc_df_zero['Time'], sc_df_zero['Amount'], color = 'blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "MAHPBOsaBBF4",
        "outputId": "a19d3c32-bb58-44db-89ec-6ac215971aa4"
      },
      "id": "MAHPBOsaBBF4",
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f80a9414fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "id": "b20f9d7f",
      "metadata": {
        "id": "b20f9d7f",
        "outputId": "447e7a63-84fe-4bc3-e410-d71985a03cc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d883b3c4-a841-4025-963c-a2818b53b6c3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "      <th>Amount_log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Time</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.173963e-01</td>\n",
              "      <td>-1.059333e-02</td>\n",
              "      <td>-4.196182e-01</td>\n",
              "      <td>-1.052602e-01</td>\n",
              "      <td>1.730721e-01</td>\n",
              "      <td>-6.301647e-02</td>\n",
              "      <td>8.471437e-02</td>\n",
              "      <td>-3.694943e-02</td>\n",
              "      <td>-8.660434e-03</td>\n",
              "      <td>3.061663e-02</td>\n",
              "      <td>-2.476894e-01</td>\n",
              "      <td>1.243481e-01</td>\n",
              "      <td>-6.590202e-02</td>\n",
              "      <td>-9.875682e-02</td>\n",
              "      <td>-1.834533e-01</td>\n",
              "      <td>1.190287e-02</td>\n",
              "      <td>-7.329721e-02</td>\n",
              "      <td>9.043813e-02</td>\n",
              "      <td>2.897530e-02</td>\n",
              "      <td>-5.086602e-02</td>\n",
              "      <td>4.473573e-02</td>\n",
              "      <td>1.440591e-01</td>\n",
              "      <td>5.114236e-02</td>\n",
              "      <td>-1.618187e-02</td>\n",
              "      <td>-2.330828e-01</td>\n",
              "      <td>-4.140710e-02</td>\n",
              "      <td>-5.134591e-03</td>\n",
              "      <td>-9.412688e-03</td>\n",
              "      <td>-0.010596</td>\n",
              "      <td>-0.012323</td>\n",
              "      <td>-0.028515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V1</th>\n",
              "      <td>0.117396</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>4.697350e-17</td>\n",
              "      <td>-1.424390e-15</td>\n",
              "      <td>1.755316e-17</td>\n",
              "      <td>6.391162e-17</td>\n",
              "      <td>2.398071e-16</td>\n",
              "      <td>1.991550e-15</td>\n",
              "      <td>-9.490675e-17</td>\n",
              "      <td>2.169581e-16</td>\n",
              "      <td>7.433820e-17</td>\n",
              "      <td>2.438580e-16</td>\n",
              "      <td>2.422086e-16</td>\n",
              "      <td>-2.115458e-16</td>\n",
              "      <td>9.352582e-16</td>\n",
              "      <td>-3.252451e-16</td>\n",
              "      <td>6.308789e-16</td>\n",
              "      <td>-5.011524e-16</td>\n",
              "      <td>2.870125e-16</td>\n",
              "      <td>1.818128e-16</td>\n",
              "      <td>1.036959e-16</td>\n",
              "      <td>-1.755072e-16</td>\n",
              "      <td>7.477367e-17</td>\n",
              "      <td>9.808705e-16</td>\n",
              "      <td>7.354269e-17</td>\n",
              "      <td>-9.805358e-16</td>\n",
              "      <td>-8.621897e-17</td>\n",
              "      <td>3.208233e-17</td>\n",
              "      <td>9.820892e-16</td>\n",
              "      <td>-0.227709</td>\n",
              "      <td>-0.101347</td>\n",
              "      <td>-0.096375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V2</th>\n",
              "      <td>-0.010593</td>\n",
              "      <td>4.697350e-17</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.512175e-16</td>\n",
              "      <td>-1.126388e-16</td>\n",
              "      <td>-2.039868e-16</td>\n",
              "      <td>5.024680e-16</td>\n",
              "      <td>3.966486e-16</td>\n",
              "      <td>-4.413984e-17</td>\n",
              "      <td>-5.728718e-17</td>\n",
              "      <td>-4.782388e-16</td>\n",
              "      <td>9.468995e-16</td>\n",
              "      <td>-6.588252e-16</td>\n",
              "      <td>3.854521e-16</td>\n",
              "      <td>-2.541036e-16</td>\n",
              "      <td>2.831060e-16</td>\n",
              "      <td>4.934097e-17</td>\n",
              "      <td>-9.883008e-16</td>\n",
              "      <td>2.636654e-16</td>\n",
              "      <td>9.528280e-17</td>\n",
              "      <td>-9.309954e-16</td>\n",
              "      <td>8.444409e-17</td>\n",
              "      <td>2.500830e-16</td>\n",
              "      <td>1.059562e-16</td>\n",
              "      <td>-8.142354e-18</td>\n",
              "      <td>-4.261894e-17</td>\n",
              "      <td>2.601622e-16</td>\n",
              "      <td>-4.478472e-16</td>\n",
              "      <td>-3.676415e-16</td>\n",
              "      <td>-0.531409</td>\n",
              "      <td>0.091289</td>\n",
              "      <td>-0.450317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V3</th>\n",
              "      <td>-0.419618</td>\n",
              "      <td>-1.424390e-15</td>\n",
              "      <td>2.512175e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-3.416910e-16</td>\n",
              "      <td>-1.436514e-15</td>\n",
              "      <td>1.431581e-15</td>\n",
              "      <td>2.168574e-15</td>\n",
              "      <td>3.433113e-16</td>\n",
              "      <td>-4.233770e-16</td>\n",
              "      <td>6.289267e-16</td>\n",
              "      <td>-5.501758e-17</td>\n",
              "      <td>2.206522e-16</td>\n",
              "      <td>-6.883375e-16</td>\n",
              "      <td>4.271336e-16</td>\n",
              "      <td>1.122756e-16</td>\n",
              "      <td>1.183364e-15</td>\n",
              "      <td>4.576619e-17</td>\n",
              "      <td>5.427965e-16</td>\n",
              "      <td>2.576773e-16</td>\n",
              "      <td>-9.429297e-16</td>\n",
              "      <td>-2.971969e-17</td>\n",
              "      <td>4.648259e-16</td>\n",
              "      <td>2.115206e-17</td>\n",
              "      <td>-9.351637e-17</td>\n",
              "      <td>4.771164e-16</td>\n",
              "      <td>6.521501e-16</td>\n",
              "      <td>6.239832e-16</td>\n",
              "      <td>7.726948e-16</td>\n",
              "      <td>-0.210880</td>\n",
              "      <td>-0.192961</td>\n",
              "      <td>-0.033913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V4</th>\n",
              "      <td>-0.105260</td>\n",
              "      <td>1.755316e-17</td>\n",
              "      <td>-1.126388e-16</td>\n",
              "      <td>-3.416910e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-1.940929e-15</td>\n",
              "      <td>-2.712659e-16</td>\n",
              "      <td>1.556330e-16</td>\n",
              "      <td>5.195643e-16</td>\n",
              "      <td>3.859585e-16</td>\n",
              "      <td>6.055490e-16</td>\n",
              "      <td>-2.083600e-16</td>\n",
              "      <td>-5.657963e-16</td>\n",
              "      <td>-1.506129e-16</td>\n",
              "      <td>-8.522435e-17</td>\n",
              "      <td>-1.507718e-16</td>\n",
              "      <td>-6.939204e-16</td>\n",
              "      <td>-4.397925e-16</td>\n",
              "      <td>1.493667e-16</td>\n",
              "      <td>-2.656938e-16</td>\n",
              "      <td>-3.223123e-16</td>\n",
              "      <td>-9.976950e-17</td>\n",
              "      <td>2.099922e-16</td>\n",
              "      <td>6.002528e-17</td>\n",
              "      <td>2.229738e-16</td>\n",
              "      <td>5.394585e-16</td>\n",
              "      <td>-6.179751e-16</td>\n",
              "      <td>-6.403423e-17</td>\n",
              "      <td>-5.863664e-17</td>\n",
              "      <td>0.098732</td>\n",
              "      <td>0.133447</td>\n",
              "      <td>-0.004677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V5</th>\n",
              "      <td>0.173072</td>\n",
              "      <td>6.391162e-17</td>\n",
              "      <td>-2.039868e-16</td>\n",
              "      <td>-1.436514e-15</td>\n",
              "      <td>-1.940929e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>7.926364e-16</td>\n",
              "      <td>-4.209851e-16</td>\n",
              "      <td>7.589187e-16</td>\n",
              "      <td>4.205206e-16</td>\n",
              "      <td>-6.601716e-16</td>\n",
              "      <td>7.342759e-16</td>\n",
              "      <td>3.761033e-16</td>\n",
              "      <td>-9.578659e-16</td>\n",
              "      <td>-3.634803e-16</td>\n",
              "      <td>-5.132620e-16</td>\n",
              "      <td>-3.517076e-16</td>\n",
              "      <td>1.425729e-16</td>\n",
              "      <td>1.109525e-15</td>\n",
              "      <td>-3.138234e-16</td>\n",
              "      <td>2.076048e-16</td>\n",
              "      <td>-1.368701e-16</td>\n",
              "      <td>5.060029e-16</td>\n",
              "      <td>1.637596e-16</td>\n",
              "      <td>-9.286095e-16</td>\n",
              "      <td>5.625102e-16</td>\n",
              "      <td>9.144690e-16</td>\n",
              "      <td>4.465960e-16</td>\n",
              "      <td>-3.299167e-16</td>\n",
              "      <td>-0.386356</td>\n",
              "      <td>-0.094974</td>\n",
              "      <td>-0.286189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V6</th>\n",
              "      <td>-0.063016</td>\n",
              "      <td>2.398071e-16</td>\n",
              "      <td>5.024680e-16</td>\n",
              "      <td>1.431581e-15</td>\n",
              "      <td>-2.712659e-16</td>\n",
              "      <td>7.926364e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.429426e-16</td>\n",
              "      <td>-1.707421e-16</td>\n",
              "      <td>1.114447e-16</td>\n",
              "      <td>2.850776e-16</td>\n",
              "      <td>4.865799e-16</td>\n",
              "      <td>2.140589e-16</td>\n",
              "      <td>-2.268061e-16</td>\n",
              "      <td>3.452801e-16</td>\n",
              "      <td>-6.368111e-18</td>\n",
              "      <td>-2.477917e-16</td>\n",
              "      <td>3.567582e-16</td>\n",
              "      <td>2.811474e-16</td>\n",
              "      <td>2.717167e-16</td>\n",
              "      <td>1.898638e-16</td>\n",
              "      <td>-1.575903e-16</td>\n",
              "      <td>-3.362902e-16</td>\n",
              "      <td>-7.232186e-17</td>\n",
              "      <td>-1.261867e-15</td>\n",
              "      <td>1.081933e-15</td>\n",
              "      <td>-2.378414e-16</td>\n",
              "      <td>-2.623818e-16</td>\n",
              "      <td>4.813155e-16</td>\n",
              "      <td>0.215981</td>\n",
              "      <td>-0.043643</td>\n",
              "      <td>0.163822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V7</th>\n",
              "      <td>0.084714</td>\n",
              "      <td>1.991550e-15</td>\n",
              "      <td>3.966486e-16</td>\n",
              "      <td>2.168574e-15</td>\n",
              "      <td>1.556330e-16</td>\n",
              "      <td>-4.209851e-16</td>\n",
              "      <td>1.429426e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-8.691834e-17</td>\n",
              "      <td>7.933251e-16</td>\n",
              "      <td>3.043333e-17</td>\n",
              "      <td>-1.084105e-15</td>\n",
              "      <td>1.510045e-15</td>\n",
              "      <td>-9.892325e-17</td>\n",
              "      <td>-1.729462e-16</td>\n",
              "      <td>1.936832e-17</td>\n",
              "      <td>2.893672e-16</td>\n",
              "      <td>1.149692e-15</td>\n",
              "      <td>-1.116789e-16</td>\n",
              "      <td>-2.874017e-16</td>\n",
              "      <td>1.744242e-16</td>\n",
              "      <td>1.938604e-16</td>\n",
              "      <td>-1.058131e-15</td>\n",
              "      <td>2.327911e-16</td>\n",
              "      <td>-2.589727e-17</td>\n",
              "      <td>1.174169e-15</td>\n",
              "      <td>-7.334507e-16</td>\n",
              "      <td>-5.886825e-16</td>\n",
              "      <td>-6.836764e-17</td>\n",
              "      <td>0.397311</td>\n",
              "      <td>-0.187257</td>\n",
              "      <td>0.095758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V8</th>\n",
              "      <td>-0.036949</td>\n",
              "      <td>-9.490675e-17</td>\n",
              "      <td>-4.413984e-17</td>\n",
              "      <td>3.433113e-16</td>\n",
              "      <td>5.195643e-16</td>\n",
              "      <td>7.589187e-16</td>\n",
              "      <td>-1.707421e-16</td>\n",
              "      <td>-8.691834e-17</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.900829e-16</td>\n",
              "      <td>9.051847e-17</td>\n",
              "      <td>1.954747e-16</td>\n",
              "      <td>-6.266057e-17</td>\n",
              "      <td>-2.382948e-16</td>\n",
              "      <td>-1.131098e-16</td>\n",
              "      <td>2.021491e-16</td>\n",
              "      <td>5.027192e-16</td>\n",
              "      <td>-3.508777e-16</td>\n",
              "      <td>-4.093852e-16</td>\n",
              "      <td>-5.339821e-16</td>\n",
              "      <td>-1.095534e-16</td>\n",
              "      <td>-2.412439e-16</td>\n",
              "      <td>5.475559e-16</td>\n",
              "      <td>3.897104e-16</td>\n",
              "      <td>-1.802967e-16</td>\n",
              "      <td>-1.390791e-16</td>\n",
              "      <td>-1.209975e-16</td>\n",
              "      <td>1.733633e-16</td>\n",
              "      <td>-4.484325e-16</td>\n",
              "      <td>-0.103079</td>\n",
              "      <td>0.019875</td>\n",
              "      <td>-0.020690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V9</th>\n",
              "      <td>-0.008660</td>\n",
              "      <td>2.169581e-16</td>\n",
              "      <td>-5.728718e-17</td>\n",
              "      <td>-4.233770e-16</td>\n",
              "      <td>3.859585e-16</td>\n",
              "      <td>4.205206e-16</td>\n",
              "      <td>1.114447e-16</td>\n",
              "      <td>7.933251e-16</td>\n",
              "      <td>2.900829e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-2.771761e-16</td>\n",
              "      <td>4.682341e-16</td>\n",
              "      <td>-2.445230e-15</td>\n",
              "      <td>-2.650351e-16</td>\n",
              "      <td>2.343317e-16</td>\n",
              "      <td>-1.588105e-15</td>\n",
              "      <td>-3.251906e-16</td>\n",
              "      <td>6.535992e-16</td>\n",
              "      <td>1.203843e-16</td>\n",
              "      <td>1.120752e-16</td>\n",
              "      <td>-4.340941e-16</td>\n",
              "      <td>4.578389e-17</td>\n",
              "      <td>2.871855e-17</td>\n",
              "      <td>5.929286e-16</td>\n",
              "      <td>-2.346385e-16</td>\n",
              "      <td>1.099645e-15</td>\n",
              "      <td>-1.388725e-15</td>\n",
              "      <td>-2.287414e-16</td>\n",
              "      <td>9.146779e-16</td>\n",
              "      <td>-0.044246</td>\n",
              "      <td>-0.097733</td>\n",
              "      <td>-0.080498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V10</th>\n",
              "      <td>0.030617</td>\n",
              "      <td>7.433820e-17</td>\n",
              "      <td>-4.782388e-16</td>\n",
              "      <td>6.289267e-16</td>\n",
              "      <td>6.055490e-16</td>\n",
              "      <td>-6.601716e-16</td>\n",
              "      <td>2.850776e-16</td>\n",
              "      <td>3.043333e-17</td>\n",
              "      <td>9.051847e-17</td>\n",
              "      <td>-2.771761e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.624448e-16</td>\n",
              "      <td>1.439907e-15</td>\n",
              "      <td>-8.853582e-16</td>\n",
              "      <td>2.622513e-16</td>\n",
              "      <td>7.615272e-16</td>\n",
              "      <td>-1.705923e-15</td>\n",
              "      <td>3.674182e-15</td>\n",
              "      <td>3.986710e-16</td>\n",
              "      <td>2.663038e-17</td>\n",
              "      <td>-1.141888e-15</td>\n",
              "      <td>8.089504e-16</td>\n",
              "      <td>-6.707598e-16</td>\n",
              "      <td>3.809732e-16</td>\n",
              "      <td>-4.032806e-17</td>\n",
              "      <td>-2.863813e-16</td>\n",
              "      <td>-2.554293e-16</td>\n",
              "      <td>-3.103239e-16</td>\n",
              "      <td>-1.515934e-16</td>\n",
              "      <td>-0.101502</td>\n",
              "      <td>-0.216883</td>\n",
              "      <td>-0.009621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V11</th>\n",
              "      <td>-0.247689</td>\n",
              "      <td>2.438580e-16</td>\n",
              "      <td>9.468995e-16</td>\n",
              "      <td>-5.501758e-17</td>\n",
              "      <td>-2.083600e-16</td>\n",
              "      <td>7.342759e-16</td>\n",
              "      <td>4.865799e-16</td>\n",
              "      <td>-1.084105e-15</td>\n",
              "      <td>1.954747e-16</td>\n",
              "      <td>4.682341e-16</td>\n",
              "      <td>2.624448e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.176029e-15</td>\n",
              "      <td>1.945810e-15</td>\n",
              "      <td>3.637160e-17</td>\n",
              "      <td>4.801345e-16</td>\n",
              "      <td>-6.173867e-16</td>\n",
              "      <td>8.719713e-16</td>\n",
              "      <td>5.978229e-16</td>\n",
              "      <td>3.216338e-16</td>\n",
              "      <td>-2.310189e-16</td>\n",
              "      <td>-3.911893e-16</td>\n",
              "      <td>-3.811640e-17</td>\n",
              "      <td>2.232007e-16</td>\n",
              "      <td>1.219849e-15</td>\n",
              "      <td>-4.567635e-16</td>\n",
              "      <td>-1.110976e-16</td>\n",
              "      <td>-2.635827e-16</td>\n",
              "      <td>-3.091914e-16</td>\n",
              "      <td>0.000104</td>\n",
              "      <td>0.154876</td>\n",
              "      <td>-0.049182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V12</th>\n",
              "      <td>0.124348</td>\n",
              "      <td>2.422086e-16</td>\n",
              "      <td>-6.588252e-16</td>\n",
              "      <td>2.206522e-16</td>\n",
              "      <td>-5.657963e-16</td>\n",
              "      <td>3.761033e-16</td>\n",
              "      <td>2.140589e-16</td>\n",
              "      <td>1.510045e-15</td>\n",
              "      <td>-6.266057e-17</td>\n",
              "      <td>-2.445230e-15</td>\n",
              "      <td>1.439907e-15</td>\n",
              "      <td>3.176029e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-2.306149e-14</td>\n",
              "      <td>1.761652e-16</td>\n",
              "      <td>8.858186e-16</td>\n",
              "      <td>3.530418e-16</td>\n",
              "      <td>-9.869814e-16</td>\n",
              "      <td>-5.881814e-16</td>\n",
              "      <td>9.282566e-17</td>\n",
              "      <td>1.276976e-16</td>\n",
              "      <td>3.229576e-16</td>\n",
              "      <td>-5.903992e-16</td>\n",
              "      <td>1.392162e-16</td>\n",
              "      <td>4.901644e-16</td>\n",
              "      <td>5.053736e-16</td>\n",
              "      <td>-5.759321e-16</td>\n",
              "      <td>-2.312619e-16</td>\n",
              "      <td>7.327446e-16</td>\n",
              "      <td>-0.009542</td>\n",
              "      <td>-0.260593</td>\n",
              "      <td>-0.018930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V13</th>\n",
              "      <td>-0.065902</td>\n",
              "      <td>-2.115458e-16</td>\n",
              "      <td>3.854521e-16</td>\n",
              "      <td>-6.883375e-16</td>\n",
              "      <td>-1.506129e-16</td>\n",
              "      <td>-9.578659e-16</td>\n",
              "      <td>-2.268061e-16</td>\n",
              "      <td>-9.892325e-17</td>\n",
              "      <td>-2.382948e-16</td>\n",
              "      <td>-2.650351e-16</td>\n",
              "      <td>-8.853582e-16</td>\n",
              "      <td>1.945810e-15</td>\n",
              "      <td>-2.306149e-14</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.841428e-15</td>\n",
              "      <td>-4.728048e-16</td>\n",
              "      <td>3.361767e-16</td>\n",
              "      <td>-3.187905e-16</td>\n",
              "      <td>3.192535e-16</td>\n",
              "      <td>-2.594988e-16</td>\n",
              "      <td>2.345665e-18</td>\n",
              "      <td>9.499130e-17</td>\n",
              "      <td>-2.659710e-17</td>\n",
              "      <td>-5.884304e-16</td>\n",
              "      <td>-5.470547e-16</td>\n",
              "      <td>8.066738e-17</td>\n",
              "      <td>-2.121518e-16</td>\n",
              "      <td>-4.520414e-16</td>\n",
              "      <td>1.049541e-15</td>\n",
              "      <td>0.005293</td>\n",
              "      <td>-0.004570</td>\n",
              "      <td>-0.002660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V14</th>\n",
              "      <td>-0.098757</td>\n",
              "      <td>9.352582e-16</td>\n",
              "      <td>-2.541036e-16</td>\n",
              "      <td>4.271336e-16</td>\n",
              "      <td>-8.522435e-17</td>\n",
              "      <td>-3.634803e-16</td>\n",
              "      <td>3.452801e-16</td>\n",
              "      <td>-1.729462e-16</td>\n",
              "      <td>-1.131098e-16</td>\n",
              "      <td>2.343317e-16</td>\n",
              "      <td>2.622513e-16</td>\n",
              "      <td>3.637160e-17</td>\n",
              "      <td>1.761652e-16</td>\n",
              "      <td>2.841428e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>4.165738e-17</td>\n",
              "      <td>7.910367e-17</td>\n",
              "      <td>4.626140e-15</td>\n",
              "      <td>8.980955e-17</td>\n",
              "      <td>-1.061486e-16</td>\n",
              "      <td>-1.789433e-16</td>\n",
              "      <td>1.634141e-17</td>\n",
              "      <td>3.439699e-16</td>\n",
              "      <td>7.620728e-17</td>\n",
              "      <td>2.335749e-16</td>\n",
              "      <td>-2.606783e-16</td>\n",
              "      <td>-6.580254e-18</td>\n",
              "      <td>1.285770e-16</td>\n",
              "      <td>2.503271e-15</td>\n",
              "      <td>0.033751</td>\n",
              "      <td>-0.302544</td>\n",
              "      <td>0.024120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V15</th>\n",
              "      <td>-0.183453</td>\n",
              "      <td>-3.252451e-16</td>\n",
              "      <td>2.831060e-16</td>\n",
              "      <td>1.122756e-16</td>\n",
              "      <td>-1.507718e-16</td>\n",
              "      <td>-5.132620e-16</td>\n",
              "      <td>-6.368111e-18</td>\n",
              "      <td>1.936832e-17</td>\n",
              "      <td>2.021491e-16</td>\n",
              "      <td>-1.588105e-15</td>\n",
              "      <td>7.615272e-16</td>\n",
              "      <td>4.801345e-16</td>\n",
              "      <td>8.858186e-16</td>\n",
              "      <td>-4.728048e-16</td>\n",
              "      <td>4.165738e-17</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.335904e-15</td>\n",
              "      <td>3.208310e-16</td>\n",
              "      <td>7.538156e-16</td>\n",
              "      <td>-8.556257e-16</td>\n",
              "      <td>3.240475e-16</td>\n",
              "      <td>1.947458e-17</td>\n",
              "      <td>-8.936817e-16</td>\n",
              "      <td>1.119827e-16</td>\n",
              "      <td>-4.589689e-16</td>\n",
              "      <td>3.869740e-16</td>\n",
              "      <td>3.761094e-16</td>\n",
              "      <td>-1.265235e-15</td>\n",
              "      <td>-1.063286e-15</td>\n",
              "      <td>-0.002986</td>\n",
              "      <td>-0.004223</td>\n",
              "      <td>-0.066270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V16</th>\n",
              "      <td>0.011903</td>\n",
              "      <td>6.308789e-16</td>\n",
              "      <td>4.934097e-17</td>\n",
              "      <td>1.183364e-15</td>\n",
              "      <td>-6.939204e-16</td>\n",
              "      <td>-3.517076e-16</td>\n",
              "      <td>-2.477917e-16</td>\n",
              "      <td>2.893672e-16</td>\n",
              "      <td>5.027192e-16</td>\n",
              "      <td>-3.251906e-16</td>\n",
              "      <td>-1.705923e-15</td>\n",
              "      <td>-6.173867e-16</td>\n",
              "      <td>3.530418e-16</td>\n",
              "      <td>3.361767e-16</td>\n",
              "      <td>7.910367e-17</td>\n",
              "      <td>1.335904e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.854297e-15</td>\n",
              "      <td>-3.015119e-15</td>\n",
              "      <td>1.041659e-15</td>\n",
              "      <td>3.903456e-16</td>\n",
              "      <td>-3.927401e-16</td>\n",
              "      <td>3.878384e-17</td>\n",
              "      <td>8.519670e-16</td>\n",
              "      <td>-4.289239e-16</td>\n",
              "      <td>-6.644104e-16</td>\n",
              "      <td>-5.186503e-16</td>\n",
              "      <td>7.820038e-16</td>\n",
              "      <td>8.637186e-16</td>\n",
              "      <td>-0.003910</td>\n",
              "      <td>-0.196539</td>\n",
              "      <td>-0.099295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V17</th>\n",
              "      <td>-0.073297</td>\n",
              "      <td>-5.011524e-16</td>\n",
              "      <td>-9.883008e-16</td>\n",
              "      <td>4.576619e-17</td>\n",
              "      <td>-4.397925e-16</td>\n",
              "      <td>1.425729e-16</td>\n",
              "      <td>3.567582e-16</td>\n",
              "      <td>1.149692e-15</td>\n",
              "      <td>-3.508777e-16</td>\n",
              "      <td>6.535992e-16</td>\n",
              "      <td>3.674182e-15</td>\n",
              "      <td>8.719713e-16</td>\n",
              "      <td>-9.869814e-16</td>\n",
              "      <td>-3.187905e-16</td>\n",
              "      <td>4.626140e-15</td>\n",
              "      <td>3.208310e-16</td>\n",
              "      <td>1.854297e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-5.584714e-15</td>\n",
              "      <td>-3.916791e-16</td>\n",
              "      <td>-9.887431e-16</td>\n",
              "      <td>-7.753967e-16</td>\n",
              "      <td>-8.389703e-16</td>\n",
              "      <td>5.367784e-16</td>\n",
              "      <td>-5.543631e-17</td>\n",
              "      <td>4.822068e-16</td>\n",
              "      <td>4.870302e-16</td>\n",
              "      <td>8.844373e-16</td>\n",
              "      <td>-2.182692e-16</td>\n",
              "      <td>0.007309</td>\n",
              "      <td>-0.326481</td>\n",
              "      <td>0.017123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V18</th>\n",
              "      <td>0.090438</td>\n",
              "      <td>2.870125e-16</td>\n",
              "      <td>2.636654e-16</td>\n",
              "      <td>5.427965e-16</td>\n",
              "      <td>1.493667e-16</td>\n",
              "      <td>1.109525e-15</td>\n",
              "      <td>2.811474e-16</td>\n",
              "      <td>-1.116789e-16</td>\n",
              "      <td>-4.093852e-16</td>\n",
              "      <td>1.203843e-16</td>\n",
              "      <td>3.986710e-16</td>\n",
              "      <td>5.978229e-16</td>\n",
              "      <td>-5.881814e-16</td>\n",
              "      <td>3.192535e-16</td>\n",
              "      <td>8.980955e-17</td>\n",
              "      <td>7.538156e-16</td>\n",
              "      <td>-3.015119e-15</td>\n",
              "      <td>-5.584714e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-2.424185e-15</td>\n",
              "      <td>-4.942764e-16</td>\n",
              "      <td>-1.140973e-15</td>\n",
              "      <td>-8.662635e-17</td>\n",
              "      <td>-3.624236e-16</td>\n",
              "      <td>-1.126043e-16</td>\n",
              "      <td>-2.310856e-16</td>\n",
              "      <td>3.183964e-16</td>\n",
              "      <td>2.435170e-16</td>\n",
              "      <td>8.844995e-16</td>\n",
              "      <td>0.035650</td>\n",
              "      <td>-0.111485</td>\n",
              "      <td>0.042916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V19</th>\n",
              "      <td>0.028975</td>\n",
              "      <td>1.818128e-16</td>\n",
              "      <td>9.528280e-17</td>\n",
              "      <td>2.576773e-16</td>\n",
              "      <td>-2.656938e-16</td>\n",
              "      <td>-3.138234e-16</td>\n",
              "      <td>2.717167e-16</td>\n",
              "      <td>-2.874017e-16</td>\n",
              "      <td>-5.339821e-16</td>\n",
              "      <td>1.120752e-16</td>\n",
              "      <td>2.663038e-17</td>\n",
              "      <td>3.216338e-16</td>\n",
              "      <td>9.282566e-17</td>\n",
              "      <td>-2.594988e-16</td>\n",
              "      <td>-1.061486e-16</td>\n",
              "      <td>-8.556257e-16</td>\n",
              "      <td>1.041659e-15</td>\n",
              "      <td>-3.916791e-16</td>\n",
              "      <td>-2.424185e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.938450e-16</td>\n",
              "      <td>4.032541e-16</td>\n",
              "      <td>-9.690436e-16</td>\n",
              "      <td>5.733798e-16</td>\n",
              "      <td>3.126716e-17</td>\n",
              "      <td>7.415355e-16</td>\n",
              "      <td>5.614354e-16</td>\n",
              "      <td>-1.113035e-16</td>\n",
              "      <td>-1.375843e-15</td>\n",
              "      <td>-0.056151</td>\n",
              "      <td>0.034783</td>\n",
              "      <td>-0.016127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V20</th>\n",
              "      <td>-0.050866</td>\n",
              "      <td>1.036959e-16</td>\n",
              "      <td>-9.309954e-16</td>\n",
              "      <td>-9.429297e-16</td>\n",
              "      <td>-3.223123e-16</td>\n",
              "      <td>2.076048e-16</td>\n",
              "      <td>1.898638e-16</td>\n",
              "      <td>1.744242e-16</td>\n",
              "      <td>-1.095534e-16</td>\n",
              "      <td>-4.340941e-16</td>\n",
              "      <td>-1.141888e-15</td>\n",
              "      <td>-2.310189e-16</td>\n",
              "      <td>1.276976e-16</td>\n",
              "      <td>2.345665e-18</td>\n",
              "      <td>-1.789433e-16</td>\n",
              "      <td>3.240475e-16</td>\n",
              "      <td>3.903456e-16</td>\n",
              "      <td>-9.887431e-16</td>\n",
              "      <td>-4.942764e-16</td>\n",
              "      <td>2.938450e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-1.120828e-15</td>\n",
              "      <td>1.105842e-15</td>\n",
              "      <td>4.986739e-16</td>\n",
              "      <td>1.637488e-16</td>\n",
              "      <td>-1.518242e-16</td>\n",
              "      <td>-2.975081e-16</td>\n",
              "      <td>-1.446069e-15</td>\n",
              "      <td>-1.133579e-16</td>\n",
              "      <td>0.339403</td>\n",
              "      <td>0.020090</td>\n",
              "      <td>0.144731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V21</th>\n",
              "      <td>0.044736</td>\n",
              "      <td>-1.755072e-16</td>\n",
              "      <td>8.444409e-17</td>\n",
              "      <td>-2.971969e-17</td>\n",
              "      <td>-9.976950e-17</td>\n",
              "      <td>-1.368701e-16</td>\n",
              "      <td>-1.575903e-16</td>\n",
              "      <td>1.938604e-16</td>\n",
              "      <td>-2.412439e-16</td>\n",
              "      <td>4.578389e-17</td>\n",
              "      <td>8.089504e-16</td>\n",
              "      <td>-3.911893e-16</td>\n",
              "      <td>3.229576e-16</td>\n",
              "      <td>9.499130e-17</td>\n",
              "      <td>1.634141e-17</td>\n",
              "      <td>1.947458e-17</td>\n",
              "      <td>-3.927401e-16</td>\n",
              "      <td>-7.753967e-16</td>\n",
              "      <td>-1.140973e-15</td>\n",
              "      <td>4.032541e-16</td>\n",
              "      <td>-1.120828e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.905948e-15</td>\n",
              "      <td>6.127323e-16</td>\n",
              "      <td>1.298254e-16</td>\n",
              "      <td>-2.826293e-16</td>\n",
              "      <td>-4.907301e-16</td>\n",
              "      <td>-1.033403e-15</td>\n",
              "      <td>5.132234e-16</td>\n",
              "      <td>0.105999</td>\n",
              "      <td>0.040413</td>\n",
              "      <td>0.084379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V22</th>\n",
              "      <td>0.144059</td>\n",
              "      <td>7.477367e-17</td>\n",
              "      <td>2.500830e-16</td>\n",
              "      <td>4.648259e-16</td>\n",
              "      <td>2.099922e-16</td>\n",
              "      <td>5.060029e-16</td>\n",
              "      <td>-3.362902e-16</td>\n",
              "      <td>-1.058131e-15</td>\n",
              "      <td>5.475559e-16</td>\n",
              "      <td>2.871855e-17</td>\n",
              "      <td>-6.707598e-16</td>\n",
              "      <td>-3.811640e-17</td>\n",
              "      <td>-5.903992e-16</td>\n",
              "      <td>-2.659710e-17</td>\n",
              "      <td>3.439699e-16</td>\n",
              "      <td>-8.936817e-16</td>\n",
              "      <td>3.878384e-17</td>\n",
              "      <td>-8.389703e-16</td>\n",
              "      <td>-8.662635e-17</td>\n",
              "      <td>-9.690436e-16</td>\n",
              "      <td>1.105842e-15</td>\n",
              "      <td>3.905948e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.130812e-16</td>\n",
              "      <td>1.150829e-17</td>\n",
              "      <td>-6.078986e-16</td>\n",
              "      <td>-8.477050e-16</td>\n",
              "      <td>-1.294910e-16</td>\n",
              "      <td>-3.021376e-16</td>\n",
              "      <td>-0.064801</td>\n",
              "      <td>0.000805</td>\n",
              "      <td>0.044661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V23</th>\n",
              "      <td>0.051142</td>\n",
              "      <td>9.808705e-16</td>\n",
              "      <td>1.059562e-16</td>\n",
              "      <td>2.115206e-17</td>\n",
              "      <td>6.002528e-17</td>\n",
              "      <td>1.637596e-16</td>\n",
              "      <td>-7.232186e-17</td>\n",
              "      <td>2.327911e-16</td>\n",
              "      <td>3.897104e-16</td>\n",
              "      <td>5.929286e-16</td>\n",
              "      <td>3.809732e-16</td>\n",
              "      <td>2.232007e-16</td>\n",
              "      <td>1.392162e-16</td>\n",
              "      <td>-5.884304e-16</td>\n",
              "      <td>7.620728e-17</td>\n",
              "      <td>1.119827e-16</td>\n",
              "      <td>8.519670e-16</td>\n",
              "      <td>5.367784e-16</td>\n",
              "      <td>-3.624236e-16</td>\n",
              "      <td>5.733798e-16</td>\n",
              "      <td>4.986739e-16</td>\n",
              "      <td>6.127323e-16</td>\n",
              "      <td>3.130812e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-4.411271e-17</td>\n",
              "      <td>-9.938362e-16</td>\n",
              "      <td>8.848700e-16</td>\n",
              "      <td>5.524044e-16</td>\n",
              "      <td>9.029821e-16</td>\n",
              "      <td>-0.112633</td>\n",
              "      <td>-0.002685</td>\n",
              "      <td>-0.029892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V24</th>\n",
              "      <td>-0.016182</td>\n",
              "      <td>7.354269e-17</td>\n",
              "      <td>-8.142354e-18</td>\n",
              "      <td>-9.351637e-17</td>\n",
              "      <td>2.229738e-16</td>\n",
              "      <td>-9.286095e-16</td>\n",
              "      <td>-1.261867e-15</td>\n",
              "      <td>-2.589727e-17</td>\n",
              "      <td>-1.802967e-16</td>\n",
              "      <td>-2.346385e-16</td>\n",
              "      <td>-4.032806e-17</td>\n",
              "      <td>1.219849e-15</td>\n",
              "      <td>4.901644e-16</td>\n",
              "      <td>-5.470547e-16</td>\n",
              "      <td>2.335749e-16</td>\n",
              "      <td>-4.589689e-16</td>\n",
              "      <td>-4.289239e-16</td>\n",
              "      <td>-5.543631e-17</td>\n",
              "      <td>-1.126043e-16</td>\n",
              "      <td>3.126716e-17</td>\n",
              "      <td>1.637488e-16</td>\n",
              "      <td>1.298254e-16</td>\n",
              "      <td>1.150829e-17</td>\n",
              "      <td>-4.411271e-17</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.557318e-15</td>\n",
              "      <td>3.129195e-16</td>\n",
              "      <td>-3.736529e-16</td>\n",
              "      <td>-2.259275e-16</td>\n",
              "      <td>0.005146</td>\n",
              "      <td>-0.007221</td>\n",
              "      <td>-0.015484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V25</th>\n",
              "      <td>-0.233083</td>\n",
              "      <td>-9.805358e-16</td>\n",
              "      <td>-4.261894e-17</td>\n",
              "      <td>4.771164e-16</td>\n",
              "      <td>5.394585e-16</td>\n",
              "      <td>5.625102e-16</td>\n",
              "      <td>1.081933e-15</td>\n",
              "      <td>1.174169e-15</td>\n",
              "      <td>-1.390791e-16</td>\n",
              "      <td>1.099645e-15</td>\n",
              "      <td>-2.863813e-16</td>\n",
              "      <td>-4.567635e-16</td>\n",
              "      <td>5.053736e-16</td>\n",
              "      <td>8.066738e-17</td>\n",
              "      <td>-2.606783e-16</td>\n",
              "      <td>3.869740e-16</td>\n",
              "      <td>-6.644104e-16</td>\n",
              "      <td>4.822068e-16</td>\n",
              "      <td>-2.310856e-16</td>\n",
              "      <td>7.415355e-16</td>\n",
              "      <td>-1.518242e-16</td>\n",
              "      <td>-2.826293e-16</td>\n",
              "      <td>-6.078986e-16</td>\n",
              "      <td>-9.938362e-16</td>\n",
              "      <td>1.557318e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.810884e-15</td>\n",
              "      <td>-6.107118e-16</td>\n",
              "      <td>3.399375e-16</td>\n",
              "      <td>-0.047837</td>\n",
              "      <td>0.003308</td>\n",
              "      <td>-0.003326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V26</th>\n",
              "      <td>-0.041407</td>\n",
              "      <td>-8.621897e-17</td>\n",
              "      <td>2.601622e-16</td>\n",
              "      <td>6.521501e-16</td>\n",
              "      <td>-6.179751e-16</td>\n",
              "      <td>9.144690e-16</td>\n",
              "      <td>-2.378414e-16</td>\n",
              "      <td>-7.334507e-16</td>\n",
              "      <td>-1.209975e-16</td>\n",
              "      <td>-1.388725e-15</td>\n",
              "      <td>-2.554293e-16</td>\n",
              "      <td>-1.110976e-16</td>\n",
              "      <td>-5.759321e-16</td>\n",
              "      <td>-2.121518e-16</td>\n",
              "      <td>-6.580254e-18</td>\n",
              "      <td>3.761094e-16</td>\n",
              "      <td>-5.186503e-16</td>\n",
              "      <td>4.870302e-16</td>\n",
              "      <td>3.183964e-16</td>\n",
              "      <td>5.614354e-16</td>\n",
              "      <td>-2.975081e-16</td>\n",
              "      <td>-4.907301e-16</td>\n",
              "      <td>-8.477050e-16</td>\n",
              "      <td>8.848700e-16</td>\n",
              "      <td>3.129195e-16</td>\n",
              "      <td>2.810884e-15</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-3.383861e-16</td>\n",
              "      <td>-3.751403e-16</td>\n",
              "      <td>-0.003208</td>\n",
              "      <td>0.004455</td>\n",
              "      <td>-0.015035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V27</th>\n",
              "      <td>-0.005135</td>\n",
              "      <td>3.208233e-17</td>\n",
              "      <td>-4.478472e-16</td>\n",
              "      <td>6.239832e-16</td>\n",
              "      <td>-6.403423e-17</td>\n",
              "      <td>4.465960e-16</td>\n",
              "      <td>-2.623818e-16</td>\n",
              "      <td>-5.886825e-16</td>\n",
              "      <td>1.733633e-16</td>\n",
              "      <td>-2.287414e-16</td>\n",
              "      <td>-3.103239e-16</td>\n",
              "      <td>-2.635827e-16</td>\n",
              "      <td>-2.312619e-16</td>\n",
              "      <td>-4.520414e-16</td>\n",
              "      <td>1.285770e-16</td>\n",
              "      <td>-1.265235e-15</td>\n",
              "      <td>7.820038e-16</td>\n",
              "      <td>8.844373e-16</td>\n",
              "      <td>2.435170e-16</td>\n",
              "      <td>-1.113035e-16</td>\n",
              "      <td>-1.446069e-15</td>\n",
              "      <td>-1.033403e-15</td>\n",
              "      <td>-1.294910e-16</td>\n",
              "      <td>5.524044e-16</td>\n",
              "      <td>-3.736529e-16</td>\n",
              "      <td>-6.107118e-16</td>\n",
              "      <td>-3.383861e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-3.770124e-16</td>\n",
              "      <td>0.028825</td>\n",
              "      <td>0.017580</td>\n",
              "      <td>-0.044270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V28</th>\n",
              "      <td>-0.009413</td>\n",
              "      <td>9.820892e-16</td>\n",
              "      <td>-3.676415e-16</td>\n",
              "      <td>7.726948e-16</td>\n",
              "      <td>-5.863664e-17</td>\n",
              "      <td>-3.299167e-16</td>\n",
              "      <td>4.813155e-16</td>\n",
              "      <td>-6.836764e-17</td>\n",
              "      <td>-4.484325e-16</td>\n",
              "      <td>9.146779e-16</td>\n",
              "      <td>-1.515934e-16</td>\n",
              "      <td>-3.091914e-16</td>\n",
              "      <td>7.327446e-16</td>\n",
              "      <td>1.049541e-15</td>\n",
              "      <td>2.503271e-15</td>\n",
              "      <td>-1.063286e-15</td>\n",
              "      <td>8.637186e-16</td>\n",
              "      <td>-2.182692e-16</td>\n",
              "      <td>8.844995e-16</td>\n",
              "      <td>-1.375843e-15</td>\n",
              "      <td>-1.133579e-16</td>\n",
              "      <td>5.132234e-16</td>\n",
              "      <td>-3.021376e-16</td>\n",
              "      <td>9.029821e-16</td>\n",
              "      <td>-2.259275e-16</td>\n",
              "      <td>3.399375e-16</td>\n",
              "      <td>-3.751403e-16</td>\n",
              "      <td>-3.770124e-16</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.010258</td>\n",
              "      <td>0.009536</td>\n",
              "      <td>-0.001771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Amount</th>\n",
              "      <td>-0.010596</td>\n",
              "      <td>-2.277087e-01</td>\n",
              "      <td>-5.314089e-01</td>\n",
              "      <td>-2.108805e-01</td>\n",
              "      <td>9.873167e-02</td>\n",
              "      <td>-3.863563e-01</td>\n",
              "      <td>2.159812e-01</td>\n",
              "      <td>3.973113e-01</td>\n",
              "      <td>-1.030791e-01</td>\n",
              "      <td>-4.424560e-02</td>\n",
              "      <td>-1.015021e-01</td>\n",
              "      <td>1.039770e-04</td>\n",
              "      <td>-9.541802e-03</td>\n",
              "      <td>5.293409e-03</td>\n",
              "      <td>3.375117e-02</td>\n",
              "      <td>-2.985848e-03</td>\n",
              "      <td>-3.909527e-03</td>\n",
              "      <td>7.309042e-03</td>\n",
              "      <td>3.565034e-02</td>\n",
              "      <td>-5.615079e-02</td>\n",
              "      <td>3.394034e-01</td>\n",
              "      <td>1.059989e-01</td>\n",
              "      <td>-6.480065e-02</td>\n",
              "      <td>-1.126326e-01</td>\n",
              "      <td>5.146217e-03</td>\n",
              "      <td>-4.783686e-02</td>\n",
              "      <td>-3.208037e-03</td>\n",
              "      <td>2.882546e-02</td>\n",
              "      <td>1.025822e-02</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.005632</td>\n",
              "      <td>0.552005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <td>-0.012323</td>\n",
              "      <td>-1.013473e-01</td>\n",
              "      <td>9.128865e-02</td>\n",
              "      <td>-1.929608e-01</td>\n",
              "      <td>1.334475e-01</td>\n",
              "      <td>-9.497430e-02</td>\n",
              "      <td>-4.364316e-02</td>\n",
              "      <td>-1.872566e-01</td>\n",
              "      <td>1.987512e-02</td>\n",
              "      <td>-9.773269e-02</td>\n",
              "      <td>-2.168829e-01</td>\n",
              "      <td>1.548756e-01</td>\n",
              "      <td>-2.605929e-01</td>\n",
              "      <td>-4.569779e-03</td>\n",
              "      <td>-3.025437e-01</td>\n",
              "      <td>-4.223402e-03</td>\n",
              "      <td>-1.965389e-01</td>\n",
              "      <td>-3.264811e-01</td>\n",
              "      <td>-1.114853e-01</td>\n",
              "      <td>3.478301e-02</td>\n",
              "      <td>2.009032e-02</td>\n",
              "      <td>4.041338e-02</td>\n",
              "      <td>8.053175e-04</td>\n",
              "      <td>-2.685156e-03</td>\n",
              "      <td>-7.220907e-03</td>\n",
              "      <td>3.307706e-03</td>\n",
              "      <td>4.455398e-03</td>\n",
              "      <td>1.757973e-02</td>\n",
              "      <td>9.536041e-03</td>\n",
              "      <td>0.005632</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.008326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Amount_log</th>\n",
              "      <td>-0.028515</td>\n",
              "      <td>-9.637529e-02</td>\n",
              "      <td>-4.503166e-01</td>\n",
              "      <td>-3.391303e-02</td>\n",
              "      <td>-4.677013e-03</td>\n",
              "      <td>-2.861889e-01</td>\n",
              "      <td>1.638222e-01</td>\n",
              "      <td>9.575759e-02</td>\n",
              "      <td>-2.068987e-02</td>\n",
              "      <td>-8.049824e-02</td>\n",
              "      <td>-9.620577e-03</td>\n",
              "      <td>-4.918239e-02</td>\n",
              "      <td>-1.892987e-02</td>\n",
              "      <td>-2.659918e-03</td>\n",
              "      <td>2.412015e-02</td>\n",
              "      <td>-6.626960e-02</td>\n",
              "      <td>-9.929519e-02</td>\n",
              "      <td>1.712343e-02</td>\n",
              "      <td>4.291602e-02</td>\n",
              "      <td>-1.612698e-02</td>\n",
              "      <td>1.447309e-01</td>\n",
              "      <td>8.437884e-02</td>\n",
              "      <td>4.466067e-02</td>\n",
              "      <td>-2.989225e-02</td>\n",
              "      <td>-1.548424e-02</td>\n",
              "      <td>-3.326404e-03</td>\n",
              "      <td>-1.503530e-02</td>\n",
              "      <td>-4.426980e-02</td>\n",
              "      <td>-1.771442e-03</td>\n",
              "      <td>0.552005</td>\n",
              "      <td>-0.008326</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d883b3c4-a841-4025-963c-a2818b53b6c3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d883b3c4-a841-4025-963c-a2818b53b6c3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d883b3c4-a841-4025-963c-a2818b53b6c3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                Time            V1  ...     Class  Amount_log\n",
              "Time        1.000000  1.173963e-01  ... -0.012323   -0.028515\n",
              "V1          0.117396  1.000000e+00  ... -0.101347   -0.096375\n",
              "V2         -0.010593  4.697350e-17  ...  0.091289   -0.450317\n",
              "V3         -0.419618 -1.424390e-15  ... -0.192961   -0.033913\n",
              "V4         -0.105260  1.755316e-17  ...  0.133447   -0.004677\n",
              "V5          0.173072  6.391162e-17  ... -0.094974   -0.286189\n",
              "V6         -0.063016  2.398071e-16  ... -0.043643    0.163822\n",
              "V7          0.084714  1.991550e-15  ... -0.187257    0.095758\n",
              "V8         -0.036949 -9.490675e-17  ...  0.019875   -0.020690\n",
              "V9         -0.008660  2.169581e-16  ... -0.097733   -0.080498\n",
              "V10         0.030617  7.433820e-17  ... -0.216883   -0.009621\n",
              "V11        -0.247689  2.438580e-16  ...  0.154876   -0.049182\n",
              "V12         0.124348  2.422086e-16  ... -0.260593   -0.018930\n",
              "V13        -0.065902 -2.115458e-16  ... -0.004570   -0.002660\n",
              "V14        -0.098757  9.352582e-16  ... -0.302544    0.024120\n",
              "V15        -0.183453 -3.252451e-16  ... -0.004223   -0.066270\n",
              "V16         0.011903  6.308789e-16  ... -0.196539   -0.099295\n",
              "V17        -0.073297 -5.011524e-16  ... -0.326481    0.017123\n",
              "V18         0.090438  2.870125e-16  ... -0.111485    0.042916\n",
              "V19         0.028975  1.818128e-16  ...  0.034783   -0.016127\n",
              "V20        -0.050866  1.036959e-16  ...  0.020090    0.144731\n",
              "V21         0.044736 -1.755072e-16  ...  0.040413    0.084379\n",
              "V22         0.144059  7.477367e-17  ...  0.000805    0.044661\n",
              "V23         0.051142  9.808705e-16  ... -0.002685   -0.029892\n",
              "V24        -0.016182  7.354269e-17  ... -0.007221   -0.015484\n",
              "V25        -0.233083 -9.805358e-16  ...  0.003308   -0.003326\n",
              "V26        -0.041407 -8.621897e-17  ...  0.004455   -0.015035\n",
              "V27        -0.005135  3.208233e-17  ...  0.017580   -0.044270\n",
              "V28        -0.009413  9.820892e-16  ...  0.009536   -0.001771\n",
              "Amount     -0.010596 -2.277087e-01  ...  0.005632    0.552005\n",
              "Class      -0.012323 -1.013473e-01  ...  1.000000   -0.008326\n",
              "Amount_log -0.028515 -9.637529e-02  ... -0.008326    1.000000\n",
              "\n",
              "[32 rows x 32 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "# 데이터 프레임의 상관관계를 계산하세요\n",
        "df_map = df.corr()\n",
        "df_map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "id": "a4f7265b",
      "metadata": {
        "id": "a4f7265b",
        "outputId": "cda9e4c4-1a81-4300-eefe-3042711452ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80a86aa490>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# 계산된 상관 관계를 heatmap을 이용하여 시각화 하세요\n",
        "sns.heatmap(df_map)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "id": "3ec37a3a",
      "metadata": {
        "id": "3ec37a3a",
        "outputId": "f93dc547-4ffc-4904-ed5d-936a4d2fd69b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80a8664ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# class 컬럼의 값인 1인 데이터 프레임을 추출 후 상관 관계를 heatmap을 이용하여 시각화 하세요\n",
        "class_one = df.loc[df['Class'] ==1, :]\n",
        "one_map = class_one.corr()\n",
        "sns.heatmap(one_map)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "id": "cc089aff",
      "metadata": {
        "id": "cc089aff",
        "outputId": "263b6633-e90f-41c0-ac13-89a0bf87a7bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80a9b59cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# class 컬럼의 값인 0인 데이터 프레임을 추출 후 상관 관계를 heatmap을 이용하여 시각화 하세요\n",
        "class_zero = df.loc[df['Class'] ==0, :]\n",
        "zero_map = class_zero.corr()\n",
        "sns.heatmap(zero_map)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "eecfd59c",
      "metadata": {
        "id": "eecfd59c",
        "outputId": "e50f1cd6-e146-413f-e3f2-030b7bf4bac2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['V1',\n",
              " 'V2',\n",
              " 'V3',\n",
              " 'V4',\n",
              " 'V5',\n",
              " 'V6',\n",
              " 'V7',\n",
              " 'V8',\n",
              " 'V9',\n",
              " 'V10',\n",
              " 'V11',\n",
              " 'V12',\n",
              " 'V13',\n",
              " 'V14',\n",
              " 'V15',\n",
              " 'V16',\n",
              " 'V17',\n",
              " 'V18',\n",
              " 'V19',\n",
              " 'V20',\n",
              " 'V21',\n",
              " 'V22',\n",
              " 'V23',\n",
              " 'V24',\n",
              " 'V25',\n",
              " 'V26',\n",
              " 'V27',\n",
              " 'V28',\n",
              " 'Amount_log']"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "# 학습에 사용할 특성을 선택하세요. (학습에서 제외하기 위해선 주석처리 합니다.)\n",
        "features = [\n",
        "     'V1',\n",
        "     'V2',\n",
        "     'V3',\n",
        "     'V4',\n",
        "     'V5',\n",
        "     'V6',\n",
        "     'V7',\n",
        "     'V8',\n",
        "     'V9',\n",
        "     'V10',\n",
        "     'V11',\n",
        "     'V12',\n",
        "     'V13',\n",
        "     'V14',\n",
        "     'V15',\n",
        "     'V16',\n",
        "     'V17',\n",
        "     'V18',\n",
        "     'V19',\n",
        "     'V20',\n",
        "     'V21',\n",
        "     'V22',\n",
        "     'V23',\n",
        "     'V24',\n",
        "     'V25',\n",
        "     'V26',\n",
        "     'V27',\n",
        "     'V28',\n",
        "     'Amount_log'\n",
        "]\n",
        "features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "id": "6efdbb8b",
      "metadata": {
        "id": "6efdbb8b",
        "outputId": "12147559-a434-48e8-86d8-685b00a165f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f0fadde5-8f00-4c84-94a3-4e57cec9ea46\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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_log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\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>5.014760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>1.305626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>5.939276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>4.824306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>4.262539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284802</th>\n",
              "      <td>-11.881118</td>\n",
              "      <td>10.071785</td>\n",
              "      <td>-9.834783</td>\n",
              "      <td>-2.066656</td>\n",
              "      <td>-5.364473</td>\n",
              "      <td>-2.606837</td>\n",
              "      <td>-4.918215</td>\n",
              "      <td>7.305334</td>\n",
              "      <td>1.914428</td>\n",
              "      <td>4.356170</td>\n",
              "      <td>-1.593105</td>\n",
              "      <td>2.711941</td>\n",
              "      <td>-0.689256</td>\n",
              "      <td>4.626942</td>\n",
              "      <td>-0.924459</td>\n",
              "      <td>1.107641</td>\n",
              "      <td>1.991691</td>\n",
              "      <td>0.510632</td>\n",
              "      <td>-0.682920</td>\n",
              "      <td>1.475829</td>\n",
              "      <td>0.213454</td>\n",
              "      <td>0.111864</td>\n",
              "      <td>1.014480</td>\n",
              "      <td>-0.509348</td>\n",
              "      <td>1.436807</td>\n",
              "      <td>0.250034</td>\n",
              "      <td>0.943651</td>\n",
              "      <td>0.823731</td>\n",
              "      <td>0.570980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284803</th>\n",
              "      <td>-0.732789</td>\n",
              "      <td>-0.055080</td>\n",
              "      <td>2.035030</td>\n",
              "      <td>-0.738589</td>\n",
              "      <td>0.868229</td>\n",
              "      <td>1.058415</td>\n",
              "      <td>0.024330</td>\n",
              "      <td>0.294869</td>\n",
              "      <td>0.584800</td>\n",
              "      <td>-0.975926</td>\n",
              "      <td>-0.150189</td>\n",
              "      <td>0.915802</td>\n",
              "      <td>1.214756</td>\n",
              "      <td>-0.675143</td>\n",
              "      <td>1.164931</td>\n",
              "      <td>-0.711757</td>\n",
              "      <td>-0.025693</td>\n",
              "      <td>-1.221179</td>\n",
              "      <td>-1.545556</td>\n",
              "      <td>0.059616</td>\n",
              "      <td>0.214205</td>\n",
              "      <td>0.924384</td>\n",
              "      <td>0.012463</td>\n",
              "      <td>-1.016226</td>\n",
              "      <td>-0.606624</td>\n",
              "      <td>-0.395255</td>\n",
              "      <td>0.068472</td>\n",
              "      <td>-0.053527</td>\n",
              "      <td>3.249987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284804</th>\n",
              "      <td>1.919565</td>\n",
              "      <td>-0.301254</td>\n",
              "      <td>-3.249640</td>\n",
              "      <td>-0.557828</td>\n",
              "      <td>2.630515</td>\n",
              "      <td>3.031260</td>\n",
              "      <td>-0.296827</td>\n",
              "      <td>0.708417</td>\n",
              "      <td>0.432454</td>\n",
              "      <td>-0.484782</td>\n",
              "      <td>0.411614</td>\n",
              "      <td>0.063119</td>\n",
              "      <td>-0.183699</td>\n",
              "      <td>-0.510602</td>\n",
              "      <td>1.329284</td>\n",
              "      <td>0.140716</td>\n",
              "      <td>0.313502</td>\n",
              "      <td>0.395652</td>\n",
              "      <td>-0.577252</td>\n",
              "      <td>0.001396</td>\n",
              "      <td>0.232045</td>\n",
              "      <td>0.578229</td>\n",
              "      <td>-0.037501</td>\n",
              "      <td>0.640134</td>\n",
              "      <td>0.265745</td>\n",
              "      <td>-0.087371</td>\n",
              "      <td>0.004455</td>\n",
              "      <td>-0.026561</td>\n",
              "      <td>4.232366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284805</th>\n",
              "      <td>-0.240440</td>\n",
              "      <td>0.530483</td>\n",
              "      <td>0.702510</td>\n",
              "      <td>0.689799</td>\n",
              "      <td>-0.377961</td>\n",
              "      <td>0.623708</td>\n",
              "      <td>-0.686180</td>\n",
              "      <td>0.679145</td>\n",
              "      <td>0.392087</td>\n",
              "      <td>-0.399126</td>\n",
              "      <td>-1.933849</td>\n",
              "      <td>-0.962886</td>\n",
              "      <td>-1.042082</td>\n",
              "      <td>0.449624</td>\n",
              "      <td>1.962563</td>\n",
              "      <td>-0.608577</td>\n",
              "      <td>0.509928</td>\n",
              "      <td>1.113981</td>\n",
              "      <td>2.897849</td>\n",
              "      <td>0.127434</td>\n",
              "      <td>0.265245</td>\n",
              "      <td>0.800049</td>\n",
              "      <td>-0.163298</td>\n",
              "      <td>0.123205</td>\n",
              "      <td>-0.569159</td>\n",
              "      <td>0.546668</td>\n",
              "      <td>0.108821</td>\n",
              "      <td>0.104533</td>\n",
              "      <td>2.397895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284806</th>\n",
              "      <td>-0.533413</td>\n",
              "      <td>-0.189733</td>\n",
              "      <td>0.703337</td>\n",
              "      <td>-0.506271</td>\n",
              "      <td>-0.012546</td>\n",
              "      <td>-0.649617</td>\n",
              "      <td>1.577006</td>\n",
              "      <td>-0.414650</td>\n",
              "      <td>0.486180</td>\n",
              "      <td>-0.915427</td>\n",
              "      <td>-1.040458</td>\n",
              "      <td>-0.031513</td>\n",
              "      <td>-0.188093</td>\n",
              "      <td>-0.084316</td>\n",
              "      <td>0.041333</td>\n",
              "      <td>-0.302620</td>\n",
              "      <td>-0.660377</td>\n",
              "      <td>0.167430</td>\n",
              "      <td>-0.256117</td>\n",
              "      <td>0.382948</td>\n",
              "      <td>0.261057</td>\n",
              "      <td>0.643078</td>\n",
              "      <td>0.376777</td>\n",
              "      <td>0.008797</td>\n",
              "      <td>-0.473649</td>\n",
              "      <td>-0.818267</td>\n",
              "      <td>-0.002415</td>\n",
              "      <td>0.013649</td>\n",
              "      <td>5.384495</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>284807 rows × 29 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0fadde5-8f00-4c84-94a3-4e57cec9ea46')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f0fadde5-8f00-4c84-94a3-4e57cec9ea46 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f0fadde5-8f00-4c84-94a3-4e57cec9ea46');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "               V1         V2        V3  ...       V27       V28  Amount_log\n",
              "0       -1.359807  -0.072781  2.536347  ...  0.133558 -0.021053    5.014760\n",
              "1        1.191857   0.266151  0.166480  ... -0.008983  0.014724    1.305626\n",
              "2       -1.358354  -1.340163  1.773209  ... -0.055353 -0.059752    5.939276\n",
              "3       -0.966272  -0.185226  1.792993  ...  0.062723  0.061458    4.824306\n",
              "4       -1.158233   0.877737  1.548718  ...  0.219422  0.215153    4.262539\n",
              "...           ...        ...       ...  ...       ...       ...         ...\n",
              "284802 -11.881118  10.071785 -9.834783  ...  0.943651  0.823731    0.570980\n",
              "284803  -0.732789  -0.055080  2.035030  ...  0.068472 -0.053527    3.249987\n",
              "284804   1.919565  -0.301254 -3.249640  ...  0.004455 -0.026561    4.232366\n",
              "284805  -0.240440   0.530483  0.702510  ...  0.108821  0.104533    2.397895\n",
              "284806  -0.533413  -0.189733  0.703337  ... -0.002415  0.013649    5.384495\n",
              "\n",
              "[284807 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "# features에 저장된 컬럼명만 추출하여 X에 대입하세요.\n",
        "X = df[features]\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "id": "5c742d81",
      "metadata": {
        "id": "5c742d81",
        "outputId": "a3276b52-06a2-4069-99f3-94b3a5d6621e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         0\n",
              "1         0\n",
              "2         0\n",
              "3         0\n",
              "4         0\n",
              "         ..\n",
              "284802    0\n",
              "284803    0\n",
              "284804    0\n",
              "284805    0\n",
              "284806    0\n",
              "Name: Class, Length: 284807, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "# Class 컬럼을 추출하여 Y에 대입하세요\n",
        "Y = df['Class']\n",
        "Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "id": "50b5be74",
      "metadata": {
        "id": "50b5be74"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "id": "cf554971",
      "metadata": {
        "scrolled": true,
        "id": "cf554971",
        "outputId": "b58147ec-5975-4f1d-a7c9-13aaec194395",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.61623732, -0.09860181,  1.22890545, ...,  0.81673112,\n",
              "        -0.24609073,  0.76029701],\n",
              "       [ 0.52492862,  0.14309984, -0.00697044, ..., -0.0637815 ,\n",
              "         0.02651893, -0.74031194],\n",
              "       [-0.61558747, -1.0024067 ,  0.83093237, ..., -0.35021814,\n",
              "        -0.54096166,  1.13432963],\n",
              "       ...,\n",
              "       [ 0.85037719, -0.26153195, -1.78846319, ...,  0.01922743,\n",
              "        -0.28805801,  0.44376276],\n",
              "       [-0.1156293 ,  0.33160212,  0.27256711, ...,  0.66392082,\n",
              "         0.71082878, -0.29841135],\n",
              "       [-0.24665367, -0.18200361,  0.27299846, ..., -0.02321069,\n",
              "         0.01832587,  0.90988115]])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "# 추출된 X데이터를 RobustSclaer를 이용하여 정규화하세요.\n",
        "scaler = RobustScaler()\n",
        "x_scaled = scaler.fit_transform(X)\n",
        "x_scaled"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "id": "854d8641",
      "metadata": {
        "id": "854d8641"
      },
      "outputs": [],
      "source": [
        "# X_Sclaed와 Y를 이용하여 데이터를 분할하세요. \n",
        "# 층화추출을 적용하세요, random_state는 0으로 지정하세요.\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_scaled, Y, \n",
        "                                                    stratify=Y,\n",
        "                                                    test_size=0.01,\n",
        "                                                    random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# KNN 모델을 적용 후 성능을 평가하세요. \n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "model = KNeighborsClassifier().fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "f0C3NGEhmu05"
      },
      "id": "f0C3NGEhmu05",
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape , x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDy1K4GGnbD0",
        "outputId": "45aacf36-e16d-46a8-c2e9-c77dd7aa5a43"
      },
      "id": "UDy1K4GGnbD0",
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2849, 29), (281958, 29))"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7iGj24S0nYMx",
        "outputId": "9eb1003c-84fb-49ae-e4aa-a94cda9a66a8"
      },
      "id": "7iGj24S0nYMx",
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9996489996489997"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#너무 오래 걸려서 주석처리를 하였습니다.\n",
        "#odel.score(x_train, y_train)"
      ],
      "metadata": {
        "id": "w_PUBlgHoL06"
      },
      "id": "w_PUBlgHoL06",
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 범주의 비율이 비대칭인 경우 정확도는 높게 나올 수 있습니다. \n",
        "# 정확한 성능 평가를 위해 classification_report 함수를 이용해서 test 데이터를 평가하세요.\n",
        "P = model.predict(x_test)\n",
        "print(classification_report(y_test, P))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFSeAC1IolGI",
        "outputId": "e34824fb-2822-40c4-e3f0-d2c11d67a89f"
      },
      "id": "NFSeAC1IolGI",
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      2844\n",
            "           1       0.83      1.00      0.91         5\n",
            "\n",
            "    accuracy                           1.00      2849\n",
            "   macro avg       0.92      1.00      0.95      2849\n",
            "weighted avg       1.00      1.00      1.00      2849\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X_Sclaed와 Y를 이용하여 데이터를 분할하세요. \n",
        "# 층화추출을 적용하세요, random_state는 0으로 지정하세요.\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_scaled, Y, \n",
        "                                                    stratify=Y,\n",
        "                                                    test_size=0.2,\n",
        "                                                    random_state=0)"
      ],
      "metadata": {
        "id": "hfVvJ6rfqrFF"
      },
      "id": "hfVvJ6rfqrFF",
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression().fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "DSxPtzM5qBPf"
      },
      "id": "DSxPtzM5qBPf",
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "id": "9693af7d",
      "metadata": {
        "id": "9693af7d",
        "outputId": "91479316-8cb7-4294-fea3-fb7e8494ea3f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9992407118874673, 0.9991924440855307)"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ],
      "source": [
        "# LogisticRegreesion 모델을 적용 후 성능을 평가하세요. \n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "id": "671b9961",
      "metadata": {
        "id": "671b9961",
        "outputId": "c64c54a6-2464-45b4-c29f-45c6389efc97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     56864\n",
            "           1       0.86      0.63      0.73        98\n",
            "\n",
            "    accuracy                           1.00     56962\n",
            "   macro avg       0.93      0.82      0.86     56962\n",
            "weighted avg       1.00      1.00      1.00     56962\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 범주의 비율이 비대칭인 경우 정확도는 높게 나올 수 있습니다. \n",
        "# 정확한 성능 평가를 위해 classification_report 함수를 이용해서 test 데이터를 평가하세요.\n",
        "P = model.predict(x_test)\n",
        "print(classification_report(y_test, P))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "id": "ac937540",
      "metadata": {
        "id": "ac937540"
      },
      "outputs": [],
      "source": [
        "# DecisionTree 모델을 적용 후 성능을 평가하세요. \n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "model = DecisionTreeClassifier(random_state=23).fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# DecisionTree 모델을 적용 후 성능을 평가하세요. \n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mALBWRCyJW0M",
        "outputId": "0d4dda3f-ee1d-43ab-9c04-02ab1b3b74fe"
      },
      "id": "mALBWRCyJW0M",
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.0, 0.9990519995786665)"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "id": "91c496a6",
      "metadata": {
        "id": "91c496a6",
        "outputId": "f4f05987-2de3-439d-da35-e673e3615938",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     56864\n",
            "           1       0.72      0.74      0.73        98\n",
            "\n",
            "    accuracy                           1.00     56962\n",
            "   macro avg       0.86      0.87      0.86     56962\n",
            "weighted avg       1.00      1.00      1.00     56962\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 범주의 비율이 비대칭인 경우 정확도는 높게 나올 수 있습니다. \n",
        "# 정확한 성능 평가를 위해 classification_report 함수를 이용해서 test 데이터를 평가하세요.\n",
        "P = model.predict(x_test)\n",
        "print(classification_report(y_test, P))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "id": "d4ab3158",
      "metadata": {
        "id": "d4ab3158",
        "outputId": "cb7f2951-dbfa-4a36-f885-f480eeaaae34",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9999956110513727, 0.9994557775359011)"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ],
      "source": [
        "# RandomForest 모델을 적용 후 성능을 평가하세요. \n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model = RandomForestClassifier(random_state=23).fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "id": "958825ff",
      "metadata": {
        "id": "958825ff",
        "outputId": "e465bba2-0c67-43e3-9dae-bc3e0a456938",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     56864\n",
            "           1       0.95      0.72      0.82        98\n",
            "\n",
            "    accuracy                           1.00     56962\n",
            "   macro avg       0.97      0.86      0.91     56962\n",
            "weighted avg       1.00      1.00      1.00     56962\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 범주의 비율이 비대칭인 경우 정확도는 높게 나올 수 있습니다. \n",
        "# 정확한 성능 평가를 위해 classification_report 함수를 이용해서 test 데이터를 평가하세요.\n",
        "P = model.predict(x_test)\n",
        "print(classification_report(y_test, P))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "id": "60fa6382",
      "metadata": {
        "id": "60fa6382",
        "outputId": "89f04c03-7588-42c7-c99c-93858195c8b5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9994513814215804, 0.9992275552122467)"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ],
      "source": [
        "# GradientBoosting 모델을 적용 후 성능을 평가하세요. \n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "model = GradientBoostingClassifier().fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "id": "f9ad5ab4",
      "metadata": {
        "id": "f9ad5ab4",
        "outputId": "4921497f-edbf-4488-9fa4-dfcfa1973f4d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     56864\n",
            "           1       0.79      0.76      0.77        98\n",
            "\n",
            "    accuracy                           1.00     56962\n",
            "   macro avg       0.89      0.88      0.89     56962\n",
            "weighted avg       1.00      1.00      1.00     56962\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 범주의 비율이 비대칭인 경우 정확도는 높게 나올 수 있습니다. \n",
        "# 정확한 성능 평가를 위해 classification_report 함수를 이용해서 test 데이터를 평가하세요.\n",
        "P = model.predict(x_test)\n",
        "print(classification_report(y_test, P))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "id": "a5d2a818",
      "metadata": {
        "id": "a5d2a818",
        "outputId": "b8b59c69-5c70-4060-9afb-1d73736e1be8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9996752178015756, 0.999420666409185)"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ],
      "source": [
        "# xgboost 모델을 적용 후 성능을 평가하세요. \n",
        "from xgboost import XGBClassifier\n",
        "model = XGBClassifier().fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "id": "3448fbf1",
      "metadata": {
        "id": "3448fbf1",
        "outputId": "7f691c65-0a79-472b-d959-b030e85d8690",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     56864\n",
            "           1       0.92      0.72      0.81        98\n",
            "\n",
            "    accuracy                           1.00     56962\n",
            "   macro avg       0.96      0.86      0.91     56962\n",
            "weighted avg       1.00      1.00      1.00     56962\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 범주의 비율이 비대칭인 경우 정확도는 높게 나올 수 있습니다. \n",
        "# 정확한 성능 평가를 위해 classification_report 함수를 이용해서 test 데이터를 평가하세요.\n",
        "P = model.predict(x_test)\n",
        "print(classification_report(y_test, P))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "id": "a48f2c79",
      "metadata": {
        "id": "a48f2c79",
        "outputId": "8e0deb46-df2a-4f89-a5f7-dcdf08e9971a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9972876297482938, 0.996875109722271)"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ],
      "source": [
        "# lightgbm 모델을 적용 후 성능을 평가하세요. \n",
        "from lightgbm import LGBMClassifier\n",
        "model = LGBMClassifier().fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "id": "fd2826f8",
      "metadata": {
        "id": "fd2826f8",
        "outputId": "2bb62da0-12c2-4605-caa7-11c6ef6c313f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     56864\n",
            "           1       0.30      0.59      0.39        98\n",
            "\n",
            "    accuracy                           1.00     56962\n",
            "   macro avg       0.65      0.79      0.70     56962\n",
            "weighted avg       1.00      1.00      1.00     56962\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 범주의 비율이 비대칭인 경우 정확도는 높게 나올 수 있습니다. \n",
        "# 정확한 성능 평가를 위해 classification_report 함수를 이용해서 test 데이터를 평가하세요.\n",
        "P = model.predict(x_test)\n",
        "print(classification_report(y_test, P))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "CL0CcxfrW2gx"
      },
      "id": "CL0CcxfrW2gx",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.2"
    },
    "colab": {
      "name": "데이터분석_7일차_워크샵_분류 실습.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}