diff --git a/Fraud_Detection_Machine_Learning_Project.ipynb b/Fraud_Detection_Machine_Learning_Project.ipynb
deleted file mode 100644
index 582383301..000000000
--- a/Fraud_Detection_Machine_Learning_Project.ipynb
+++ /dev/null
@@ -1,3565 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/PrasDev4/PyVerse/blob/main/Fraud_Detection_Machine_Learning_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "### **Fraud Detection Project**"
-      ],
-      "metadata": {
-        "id": "CJcZvXn4e3fY"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cgEbe5wPeMmg",
-        "outputId": "8d167fd1-c199-47c0-df29-0a6f391da9bd"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.17)\n",
-            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.17.0)\n",
-            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.12.14)\n",
-            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
-            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.32.3)\n",
-            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.67.1)\n",
-            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
-            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.3.0)\n",
-            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.2.0)\n",
-            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
-            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
-            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4.1)\n",
-            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.10)\n"
-          ]
-        }
-      ],
-      "source": [
-        "pip install kaggle"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!mkdir -p ~/.kaggle\n",
-        "!cp kaggle.json~/.kaggle/\n",
-        "!chmod 600 ~/.kaggle/kaggle.json"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "3NBZssp0fJsk",
-        "outputId": "6bde38c8-1b8b-4ff4-c90e-8527a9e4803a"
-      },
-      "execution_count": 2,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "cp: missing destination file operand after 'kaggle.json~/.kaggle/'\n",
-            "Try 'cp --help' for more information.\n",
-            "chmod: cannot access '/root/.kaggle/kaggle.json': No such file or directory\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# API to fetch  kaggle dataset\n",
-        "!kaggle datasets download -d mlg-ulb/creditcardfraud"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "d6OnTj1ygtta",
-        "outputId": "990b1470-f01e-4322-abbc-549d85cf3a1d"
-      },
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Dataset URL: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud\n",
-            "License(s): DbCL-1.0\n",
-            "Downloading creditcardfraud.zip to /content\n",
-            " 94% 62.0M/66.0M [00:02<00:00, 43.9MB/s]\n",
-            "100% 66.0M/66.0M [00:02<00:00, 29.3MB/s]\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# extracting zip file\n",
-        "from zipfile import ZipFile\n",
-        "file_name = \"creditcardfraud.zip\"\n",
-        "\n",
-        "with ZipFile(file_name,'r') as zip:\n",
-        "  zip.extractall()\n",
-        "  print(\"Done\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "smmqhDZwg9fE",
-        "outputId": "49af5a9a-2431-456d-b4e2-7caaac7b4d1c"
-      },
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Done\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.linear_model import LogisticRegression\n",
-        "from sklearn.metrics import accuracy_score\n"
-      ],
-      "metadata": {
-        "id": "u8XAie13g9kI"
-      },
-      "execution_count": 5,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Importing data\n",
-        "data=pd.read_csv(\"/content/creditcard.csv\")"
-      ],
-      "metadata": {
-        "id": "Zu7kS1mKg9sK"
-      },
-      "execution_count": 6,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "data.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 255
-        },
-        "id": "g0fLI_V5iQ8t",
-        "outputId": "b83f3655-79eb-4b96-900b-f2526934ac6e"
-      },
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
-              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
-              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
-              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
-              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
-              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
-              "\n",
-              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
-              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
-              "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
-              "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
-              "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
-              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
-              "\n",
-              "        V26       V27       V28  Amount  Class  \n",
-              "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
-              "1  0.125895 -0.008983  0.014724    2.69      0  \n",
-              "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
-              "3 -0.221929  0.062723  0.061458  123.50      0  \n",
-              "4  0.502292  0.219422  0.215153   69.99      0  \n",
-              "\n",
-              "[5 rows x 31 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-ff4ebe7d-d887-4e8a-bcd8-5d85271cef71\" 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>...</th>\n",
-              "      <th>V21</th>\n",
-              "      <th>V22</th>\n",
-              "      <th>V23</th>\n",
-              "      <th>V24</th>\n",
-              "      <th>V25</th>\n",
-              "      <th>V26</th>\n",
-              "      <th>V27</th>\n",
-              "      <th>V28</th>\n",
-              "      <th>Amount</th>\n",
-              "      <th>Class</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.359807</td>\n",
-              "      <td>-0.072781</td>\n",
-              "      <td>2.536347</td>\n",
-              "      <td>1.378155</td>\n",
-              "      <td>-0.338321</td>\n",
-              "      <td>0.462388</td>\n",
-              "      <td>0.239599</td>\n",
-              "      <td>0.098698</td>\n",
-              "      <td>0.363787</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-0.018307</td>\n",
-              "      <td>0.277838</td>\n",
-              "      <td>-0.110474</td>\n",
-              "      <td>0.066928</td>\n",
-              "      <td>0.128539</td>\n",
-              "      <td>-0.189115</td>\n",
-              "      <td>0.133558</td>\n",
-              "      <td>-0.021053</td>\n",
-              "      <td>149.62</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.191857</td>\n",
-              "      <td>0.266151</td>\n",
-              "      <td>0.166480</td>\n",
-              "      <td>0.448154</td>\n",
-              "      <td>0.060018</td>\n",
-              "      <td>-0.082361</td>\n",
-              "      <td>-0.078803</td>\n",
-              "      <td>0.085102</td>\n",
-              "      <td>-0.255425</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-0.225775</td>\n",
-              "      <td>-0.638672</td>\n",
-              "      <td>0.101288</td>\n",
-              "      <td>-0.339846</td>\n",
-              "      <td>0.167170</td>\n",
-              "      <td>0.125895</td>\n",
-              "      <td>-0.008983</td>\n",
-              "      <td>0.014724</td>\n",
-              "      <td>2.69</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>-1.358354</td>\n",
-              "      <td>-1.340163</td>\n",
-              "      <td>1.773209</td>\n",
-              "      <td>0.379780</td>\n",
-              "      <td>-0.503198</td>\n",
-              "      <td>1.800499</td>\n",
-              "      <td>0.791461</td>\n",
-              "      <td>0.247676</td>\n",
-              "      <td>-1.514654</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.247998</td>\n",
-              "      <td>0.771679</td>\n",
-              "      <td>0.909412</td>\n",
-              "      <td>-0.689281</td>\n",
-              "      <td>-0.327642</td>\n",
-              "      <td>-0.139097</td>\n",
-              "      <td>-0.055353</td>\n",
-              "      <td>-0.059752</td>\n",
-              "      <td>378.66</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>-0.966272</td>\n",
-              "      <td>-0.185226</td>\n",
-              "      <td>1.792993</td>\n",
-              "      <td>-0.863291</td>\n",
-              "      <td>-0.010309</td>\n",
-              "      <td>1.247203</td>\n",
-              "      <td>0.237609</td>\n",
-              "      <td>0.377436</td>\n",
-              "      <td>-1.387024</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-0.108300</td>\n",
-              "      <td>0.005274</td>\n",
-              "      <td>-0.190321</td>\n",
-              "      <td>-1.175575</td>\n",
-              "      <td>0.647376</td>\n",
-              "      <td>-0.221929</td>\n",
-              "      <td>0.062723</td>\n",
-              "      <td>0.061458</td>\n",
-              "      <td>123.50</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2.0</td>\n",
-              "      <td>-1.158233</td>\n",
-              "      <td>0.877737</td>\n",
-              "      <td>1.548718</td>\n",
-              "      <td>0.403034</td>\n",
-              "      <td>-0.407193</td>\n",
-              "      <td>0.095921</td>\n",
-              "      <td>0.592941</td>\n",
-              "      <td>-0.270533</td>\n",
-              "      <td>0.817739</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-0.009431</td>\n",
-              "      <td>0.798278</td>\n",
-              "      <td>-0.137458</td>\n",
-              "      <td>0.141267</td>\n",
-              "      <td>-0.206010</td>\n",
-              "      <td>0.502292</td>\n",
-              "      <td>0.219422</td>\n",
-              "      <td>0.215153</td>\n",
-              "      <td>69.99</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>5 rows × 31 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff4ebe7d-d887-4e8a-bcd8-5d85271cef71')\"\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 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\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",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\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-ff4ebe7d-d887-4e8a-bcd8-5d85271cef71 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-ff4ebe7d-d887-4e8a-bcd8-5d85271cef71');\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",
-              "\n",
-              "\n",
-              "<div id=\"df-8b59245b-e534-408b-9c35-dea3c4bcc837\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8b59245b-e534-408b-9c35-dea3c4bcc837')\"\n",
-              "            title=\"Suggest charts\"\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",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-8b59245b-e534-408b-9c35-dea3c4bcc837 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "data"
-            }
-          },
-          "metadata": {},
-          "execution_count": 7
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "data.shape"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "HAo_1IP4iQ_6",
-        "outputId": "d8f47e26-1f18-44af-f927-28869b64c308"
-      },
-      "execution_count": 8,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(284807, 31)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "data.info()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "MUA5pxcDiRCk",
-        "outputId": "3ea91468-77ac-4a6c-c1ae-3bd612ac8b74"
-      },
-      "execution_count": 9,
-      "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": [
-        "data['Class'].value_counts()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 178
-        },
-        "id": "BaC0Ct1Rg9wL",
-        "outputId": "afa8272a-5403-44b9-c386-59e96df6ce64"
-      },
-      "execution_count": 10,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Class\n",
-              "0    284315\n",
-              "1       492\n",
-              "Name: count, dtype: int64"
-            ],
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>count</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Class</th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>284315</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>492</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div><br><label><b>dtype:</b> int64</label>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 10
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# statastics\n",
-        "data.describe()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 349
-        },
-        "id": "Z9i_dZ0gijg3",
-        "outputId": "47f87fb2-9ea4-4e86-f553-1d542e34d970"
-      },
-      "execution_count": 11,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                Time            V1            V2            V3            V4  \\\n",
-              "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
-              "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
-              "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
-              "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
-              "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
-              "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
-              "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
-              "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
-              "\n",
-              "                 V5            V6            V7            V8            V9  \\\n",
-              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
-              "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
-              "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
-              "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
-              "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
-              "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
-              "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
-              "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
-              "\n",
-              "       ...           V21           V22           V23           V24  \\\n",
-              "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
-              "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
-              "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
-              "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
-              "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
-              "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
-              "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
-              "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
-              "\n",
-              "                V25           V26           V27           V28         Amount  \\\n",
-              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
-              "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
-              "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
-              "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
-              "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
-              "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
-              "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
-              "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
-              "\n",
-              "               Class  \n",
-              "count  284807.000000  \n",
-              "mean        0.001727  \n",
-              "std         0.041527  \n",
-              "min         0.000000  \n",
-              "25%         0.000000  \n",
-              "50%         0.000000  \n",
-              "75%         0.000000  \n",
-              "max         1.000000  \n",
-              "\n",
-              "[8 rows x 31 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-abd74a01-0868-41f2-aaf8-9b825d82c25d\" 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>...</th>\n",
-              "      <th>V21</th>\n",
-              "      <th>V22</th>\n",
-              "      <th>V23</th>\n",
-              "      <th>V24</th>\n",
-              "      <th>V25</th>\n",
-              "      <th>V26</th>\n",
-              "      <th>V27</th>\n",
-              "      <th>V28</th>\n",
-              "      <th>Amount</th>\n",
-              "      <th>Class</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>284807.000000</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>...</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>2.848070e+05</td>\n",
-              "      <td>284807.000000</td>\n",
-              "      <td>284807.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>94813.859575</td>\n",
-              "      <td>1.168375e-15</td>\n",
-              "      <td>3.416908e-16</td>\n",
-              "      <td>-1.379537e-15</td>\n",
-              "      <td>2.074095e-15</td>\n",
-              "      <td>9.604066e-16</td>\n",
-              "      <td>1.487313e-15</td>\n",
-              "      <td>-5.556467e-16</td>\n",
-              "      <td>1.213481e-16</td>\n",
-              "      <td>-2.406331e-15</td>\n",
-              "      <td>...</td>\n",
-              "      <td>1.654067e-16</td>\n",
-              "      <td>-3.568593e-16</td>\n",
-              "      <td>2.578648e-16</td>\n",
-              "      <td>4.473266e-15</td>\n",
-              "      <td>5.340915e-16</td>\n",
-              "      <td>1.683437e-15</td>\n",
-              "      <td>-3.660091e-16</td>\n",
-              "      <td>-1.227390e-16</td>\n",
-              "      <td>88.349619</td>\n",
-              "      <td>0.001727</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>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>...</td>\n",
-              "      <td>7.345240e-01</td>\n",
-              "      <td>7.257016e-01</td>\n",
-              "      <td>6.244603e-01</td>\n",
-              "      <td>6.056471e-01</td>\n",
-              "      <td>5.212781e-01</td>\n",
-              "      <td>4.822270e-01</td>\n",
-              "      <td>4.036325e-01</td>\n",
-              "      <td>3.300833e-01</td>\n",
-              "      <td>250.120109</td>\n",
-              "      <td>0.041527</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>-5.640751e+01</td>\n",
-              "      <td>-7.271573e+01</td>\n",
-              "      <td>-4.832559e+01</td>\n",
-              "      <td>-5.683171e+00</td>\n",
-              "      <td>-1.137433e+02</td>\n",
-              "      <td>-2.616051e+01</td>\n",
-              "      <td>-4.355724e+01</td>\n",
-              "      <td>-7.321672e+01</td>\n",
-              "      <td>-1.343407e+01</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-3.483038e+01</td>\n",
-              "      <td>-1.093314e+01</td>\n",
-              "      <td>-4.480774e+01</td>\n",
-              "      <td>-2.836627e+00</td>\n",
-              "      <td>-1.029540e+01</td>\n",
-              "      <td>-2.604551e+00</td>\n",
-              "      <td>-2.256568e+01</td>\n",
-              "      <td>-1.543008e+01</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>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>...</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>...</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>...</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>...</td>\n",
-              "      <td>2.720284e+01</td>\n",
-              "      <td>1.050309e+01</td>\n",
-              "      <td>2.252841e+01</td>\n",
-              "      <td>4.584549e+00</td>\n",
-              "      <td>7.519589e+00</td>\n",
-              "      <td>3.517346e+00</td>\n",
-              "      <td>3.161220e+01</td>\n",
-              "      <td>3.384781e+01</td>\n",
-              "      <td>25691.160000</td>\n",
-              "      <td>1.000000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>8 rows × 31 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abd74a01-0868-41f2-aaf8-9b825d82c25d')\"\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 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\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",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\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-abd74a01-0868-41f2-aaf8-9b825d82c25d 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-abd74a01-0868-41f2-aaf8-9b825d82c25d');\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",
-              "\n",
-              "\n",
-              "<div id=\"df-e984cc6b-5705-411d-9063-728fc50e45cc\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e984cc6b-5705-411d-9063-728fc50e45cc')\"\n",
-              "            title=\"Suggest charts\"\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",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-e984cc6b-5705-411d-9063-728fc50e45cc button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe"
-            }
-          },
-          "metadata": {},
-          "execution_count": 11
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "legit=data[data.Class==0]\n",
-        "fraud=data[data['Class']==1]"
-      ],
-      "metadata": {
-        "id": "TZTQSHysijk-"
-      },
-      "execution_count": 12,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "legit\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 443
-        },
-        "id": "kSv5ZTvqijpf",
-        "outputId": "970c09b0-2b93-425a-c2e9-4b0deeb1b58f"
-      },
-      "execution_count": 13,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "            Time         V1         V2        V3        V4        V5  \\\n",
-              "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
-              "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
-              "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
-              "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
-              "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
-              "...          ...        ...        ...       ...       ...       ...   \n",
-              "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
-              "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
-              "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
-              "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
-              "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
-              "\n",
-              "              V6        V7        V8        V9  ...       V21       V22  \\\n",
-              "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
-              "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
-              "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
-              "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
-              "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
-              "...          ...       ...       ...       ...  ...       ...       ...   \n",
-              "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
-              "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
-              "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
-              "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
-              "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
-              "\n",
-              "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
-              "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
-              "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
-              "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
-              "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
-              "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
-              "...          ...       ...       ...       ...       ...       ...     ...   \n",
-              "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
-              "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
-              "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
-              "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
-              "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
-              "\n",
-              "        Class  \n",
-              "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",
-              "\n",
-              "[284315 rows x 31 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-29f975d2-8ba0-49eb-a37b-842142163871\" 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>...</th>\n",
-              "      <th>V21</th>\n",
-              "      <th>V22</th>\n",
-              "      <th>V23</th>\n",
-              "      <th>V24</th>\n",
-              "      <th>V25</th>\n",
-              "      <th>V26</th>\n",
-              "      <th>V27</th>\n",
-              "      <th>V28</th>\n",
-              "      <th>Amount</th>\n",
-              "      <th>Class</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>-1.359807</td>\n",
-              "      <td>-0.072781</td>\n",
-              "      <td>2.536347</td>\n",
-              "      <td>1.378155</td>\n",
-              "      <td>-0.338321</td>\n",
-              "      <td>0.462388</td>\n",
-              "      <td>0.239599</td>\n",
-              "      <td>0.098698</td>\n",
-              "      <td>0.363787</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-0.018307</td>\n",
-              "      <td>0.277838</td>\n",
-              "      <td>-0.110474</td>\n",
-              "      <td>0.066928</td>\n",
-              "      <td>0.128539</td>\n",
-              "      <td>-0.189115</td>\n",
-              "      <td>0.133558</td>\n",
-              "      <td>-0.021053</td>\n",
-              "      <td>149.62</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.191857</td>\n",
-              "      <td>0.266151</td>\n",
-              "      <td>0.166480</td>\n",
-              "      <td>0.448154</td>\n",
-              "      <td>0.060018</td>\n",
-              "      <td>-0.082361</td>\n",
-              "      <td>-0.078803</td>\n",
-              "      <td>0.085102</td>\n",
-              "      <td>-0.255425</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-0.225775</td>\n",
-              "      <td>-0.638672</td>\n",
-              "      <td>0.101288</td>\n",
-              "      <td>-0.339846</td>\n",
-              "      <td>0.167170</td>\n",
-              "      <td>0.125895</td>\n",
-              "      <td>-0.008983</td>\n",
-              "      <td>0.014724</td>\n",
-              "      <td>2.69</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>-1.358354</td>\n",
-              "      <td>-1.340163</td>\n",
-              "      <td>1.773209</td>\n",
-              "      <td>0.379780</td>\n",
-              "      <td>-0.503198</td>\n",
-              "      <td>1.800499</td>\n",
-              "      <td>0.791461</td>\n",
-              "      <td>0.247676</td>\n",
-              "      <td>-1.514654</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.247998</td>\n",
-              "      <td>0.771679</td>\n",
-              "      <td>0.909412</td>\n",
-              "      <td>-0.689281</td>\n",
-              "      <td>-0.327642</td>\n",
-              "      <td>-0.139097</td>\n",
-              "      <td>-0.055353</td>\n",
-              "      <td>-0.059752</td>\n",
-              "      <td>378.66</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>-0.966272</td>\n",
-              "      <td>-0.185226</td>\n",
-              "      <td>1.792993</td>\n",
-              "      <td>-0.863291</td>\n",
-              "      <td>-0.010309</td>\n",
-              "      <td>1.247203</td>\n",
-              "      <td>0.237609</td>\n",
-              "      <td>0.377436</td>\n",
-              "      <td>-1.387024</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-0.108300</td>\n",
-              "      <td>0.005274</td>\n",
-              "      <td>-0.190321</td>\n",
-              "      <td>-1.175575</td>\n",
-              "      <td>0.647376</td>\n",
-              "      <td>-0.221929</td>\n",
-              "      <td>0.062723</td>\n",
-              "      <td>0.061458</td>\n",
-              "      <td>123.50</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2.0</td>\n",
-              "      <td>-1.158233</td>\n",
-              "      <td>0.877737</td>\n",
-              "      <td>1.548718</td>\n",
-              "      <td>0.403034</td>\n",
-              "      <td>-0.407193</td>\n",
-              "      <td>0.095921</td>\n",
-              "      <td>0.592941</td>\n",
-              "      <td>-0.270533</td>\n",
-              "      <td>0.817739</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-0.009431</td>\n",
-              "      <td>0.798278</td>\n",
-              "      <td>-0.137458</td>\n",
-              "      <td>0.141267</td>\n",
-              "      <td>-0.206010</td>\n",
-              "      <td>0.502292</td>\n",
-              "      <td>0.219422</td>\n",
-              "      <td>0.215153</td>\n",
-              "      <td>69.99</td>\n",
-              "      <td>0</td>\n",
-              "    </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",
-              "    </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>...</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>...</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>...</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>...</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>...</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>284315 rows × 31 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29f975d2-8ba0-49eb-a37b-842142163871')\"\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 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\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",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\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-29f975d2-8ba0-49eb-a37b-842142163871 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-29f975d2-8ba0-49eb-a37b-842142163871');\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",
-              "\n",
-              "\n",
-              "<div id=\"df-4b28ddad-1618-4178-9925-ae277c8f1dab\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4b28ddad-1618-4178-9925-ae277c8f1dab')\"\n",
-              "            title=\"Suggest charts\"\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",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-4b28ddad-1618-4178-9925-ae277c8f1dab button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "  <div id=\"id_c181a51b-6270-4dbb-b4b6-9031525c8e5d\">\n",
-              "    <style>\n",
-              "      .colab-df-generate {\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-generate: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-generate {\n",
-              "        background-color: #3B4455;\n",
-              "        fill: #D2E3FC;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate: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",
-              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('legit')\"\n",
-              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "    <script>\n",
-              "      (() => {\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#id_c181a51b-6270-4dbb-b4b6-9031525c8e5d button.colab-df-generate');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      buttonEl.onclick = () => {\n",
-              "        google.colab.notebook.generateWithVariable('legit');\n",
-              "      }\n",
-              "      })();\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "legit"
-            }
-          },
-          "metadata": {},
-          "execution_count": 13
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "fraud"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 443
-        },
-        "id": "NWPiqg01ijtY",
-        "outputId": "5a02a532-0fc5-44bf-c940-399111636f96"
-      },
-      "execution_count": 14,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "            Time        V1        V2        V3        V4        V5        V6  \\\n",
-              "541        406.0 -2.312227  1.951992 -1.609851  3.997906 -0.522188 -1.426545   \n",
-              "623        472.0 -3.043541 -3.157307  1.088463  2.288644  1.359805 -1.064823   \n",
-              "4920      4462.0 -2.303350  1.759247 -0.359745  2.330243 -0.821628 -0.075788   \n",
-              "6108      6986.0 -4.397974  1.358367 -2.592844  2.679787 -1.128131 -1.706536   \n",
-              "6329      7519.0  1.234235  3.019740 -4.304597  4.732795  3.624201 -1.357746   \n",
-              "...          ...       ...       ...       ...       ...       ...       ...   \n",
-              "279863  169142.0 -1.927883  1.125653 -4.518331  1.749293 -1.566487 -2.010494   \n",
-              "280143  169347.0  1.378559  1.289381 -5.004247  1.411850  0.442581 -1.326536   \n",
-              "280149  169351.0 -0.676143  1.126366 -2.213700  0.468308 -1.120541 -0.003346   \n",
-              "281144  169966.0 -3.113832  0.585864 -5.399730  1.817092 -0.840618 -2.943548   \n",
-              "281674  170348.0  1.991976  0.158476 -2.583441  0.408670  1.151147 -0.096695   \n",
-              "\n",
-              "              V7        V8        V9  ...       V21       V22       V23  \\\n",
-              "541    -2.537387  1.391657 -2.770089  ...  0.517232 -0.035049 -0.465211   \n",
-              "623     0.325574 -0.067794 -0.270953  ...  0.661696  0.435477  1.375966   \n",
-              "4920    0.562320 -0.399147 -0.238253  ... -0.294166 -0.932391  0.172726   \n",
-              "6108   -3.496197 -0.248778 -0.247768  ...  0.573574  0.176968 -0.436207   \n",
-              "6329    1.713445 -0.496358 -1.282858  ... -0.379068 -0.704181 -0.656805   \n",
-              "...          ...       ...       ...  ...       ...       ...       ...   \n",
-              "279863 -0.882850  0.697211 -2.064945  ...  0.778584 -0.319189  0.639419   \n",
-              "280143 -1.413170  0.248525 -1.127396  ...  0.370612  0.028234 -0.145640   \n",
-              "280149 -2.234739  1.210158 -0.652250  ...  0.751826  0.834108  0.190944   \n",
-              "281144 -2.208002  1.058733 -1.632333  ...  0.583276 -0.269209 -0.456108   \n",
-              "281674  0.223050 -0.068384  0.577829  ... -0.164350 -0.295135 -0.072173   \n",
-              "\n",
-              "             V24       V25       V26       V27       V28  Amount  Class  \n",
-              "541     0.320198  0.044519  0.177840  0.261145 -0.143276    0.00      1  \n",
-              "623    -0.293803  0.279798 -0.145362 -0.252773  0.035764  529.00      1  \n",
-              "4920   -0.087330 -0.156114 -0.542628  0.039566 -0.153029  239.93      1  \n",
-              "6108   -0.053502  0.252405 -0.657488 -0.827136  0.849573   59.00      1  \n",
-              "6329   -1.632653  1.488901  0.566797 -0.010016  0.146793    1.00      1  \n",
-              "...          ...       ...       ...       ...       ...     ...    ...  \n",
-              "279863 -0.294885  0.537503  0.788395  0.292680  0.147968  390.00      1  \n",
-              "280143 -0.081049  0.521875  0.739467  0.389152  0.186637    0.76      1  \n",
-              "280149  0.032070 -0.739695  0.471111  0.385107  0.194361   77.89      1  \n",
-              "281144 -0.183659 -0.328168  0.606116  0.884876 -0.253700  245.00      1  \n",
-              "281674 -0.450261  0.313267 -0.289617  0.002988 -0.015309   42.53      1  \n",
-              "\n",
-              "[492 rows x 31 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-b59ee2f1-bdc3-426e-abda-230559a44db3\" 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>...</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>541</th>\n",
-              "      <td>406.0</td>\n",
-              "      <td>-2.312227</td>\n",
-              "      <td>1.951992</td>\n",
-              "      <td>-1.609851</td>\n",
-              "      <td>3.997906</td>\n",
-              "      <td>-0.522188</td>\n",
-              "      <td>-1.426545</td>\n",
-              "      <td>-2.537387</td>\n",
-              "      <td>1.391657</td>\n",
-              "      <td>-2.770089</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.517232</td>\n",
-              "      <td>-0.035049</td>\n",
-              "      <td>-0.465211</td>\n",
-              "      <td>0.320198</td>\n",
-              "      <td>0.044519</td>\n",
-              "      <td>0.177840</td>\n",
-              "      <td>0.261145</td>\n",
-              "      <td>-0.143276</td>\n",
-              "      <td>0.00</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>623</th>\n",
-              "      <td>472.0</td>\n",
-              "      <td>-3.043541</td>\n",
-              "      <td>-3.157307</td>\n",
-              "      <td>1.088463</td>\n",
-              "      <td>2.288644</td>\n",
-              "      <td>1.359805</td>\n",
-              "      <td>-1.064823</td>\n",
-              "      <td>0.325574</td>\n",
-              "      <td>-0.067794</td>\n",
-              "      <td>-0.270953</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.661696</td>\n",
-              "      <td>0.435477</td>\n",
-              "      <td>1.375966</td>\n",
-              "      <td>-0.293803</td>\n",
-              "      <td>0.279798</td>\n",
-              "      <td>-0.145362</td>\n",
-              "      <td>-0.252773</td>\n",
-              "      <td>0.035764</td>\n",
-              "      <td>529.00</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4920</th>\n",
-              "      <td>4462.0</td>\n",
-              "      <td>-2.303350</td>\n",
-              "      <td>1.759247</td>\n",
-              "      <td>-0.359745</td>\n",
-              "      <td>2.330243</td>\n",
-              "      <td>-0.821628</td>\n",
-              "      <td>-0.075788</td>\n",
-              "      <td>0.562320</td>\n",
-              "      <td>-0.399147</td>\n",
-              "      <td>-0.238253</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-0.294166</td>\n",
-              "      <td>-0.932391</td>\n",
-              "      <td>0.172726</td>\n",
-              "      <td>-0.087330</td>\n",
-              "      <td>-0.156114</td>\n",
-              "      <td>-0.542628</td>\n",
-              "      <td>0.039566</td>\n",
-              "      <td>-0.153029</td>\n",
-              "      <td>239.93</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6108</th>\n",
-              "      <td>6986.0</td>\n",
-              "      <td>-4.397974</td>\n",
-              "      <td>1.358367</td>\n",
-              "      <td>-2.592844</td>\n",
-              "      <td>2.679787</td>\n",
-              "      <td>-1.128131</td>\n",
-              "      <td>-1.706536</td>\n",
-              "      <td>-3.496197</td>\n",
-              "      <td>-0.248778</td>\n",
-              "      <td>-0.247768</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.573574</td>\n",
-              "      <td>0.176968</td>\n",
-              "      <td>-0.436207</td>\n",
-              "      <td>-0.053502</td>\n",
-              "      <td>0.252405</td>\n",
-              "      <td>-0.657488</td>\n",
-              "      <td>-0.827136</td>\n",
-              "      <td>0.849573</td>\n",
-              "      <td>59.00</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6329</th>\n",
-              "      <td>7519.0</td>\n",
-              "      <td>1.234235</td>\n",
-              "      <td>3.019740</td>\n",
-              "      <td>-4.304597</td>\n",
-              "      <td>4.732795</td>\n",
-              "      <td>3.624201</td>\n",
-              "      <td>-1.357746</td>\n",
-              "      <td>1.713445</td>\n",
-              "      <td>-0.496358</td>\n",
-              "      <td>-1.282858</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-0.379068</td>\n",
-              "      <td>-0.704181</td>\n",
-              "      <td>-0.656805</td>\n",
-              "      <td>-1.632653</td>\n",
-              "      <td>1.488901</td>\n",
-              "      <td>0.566797</td>\n",
-              "      <td>-0.010016</td>\n",
-              "      <td>0.146793</td>\n",
-              "      <td>1.00</td>\n",
-              "      <td>1</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",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>279863</th>\n",
-              "      <td>169142.0</td>\n",
-              "      <td>-1.927883</td>\n",
-              "      <td>1.125653</td>\n",
-              "      <td>-4.518331</td>\n",
-              "      <td>1.749293</td>\n",
-              "      <td>-1.566487</td>\n",
-              "      <td>-2.010494</td>\n",
-              "      <td>-0.882850</td>\n",
-              "      <td>0.697211</td>\n",
-              "      <td>-2.064945</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.778584</td>\n",
-              "      <td>-0.319189</td>\n",
-              "      <td>0.639419</td>\n",
-              "      <td>-0.294885</td>\n",
-              "      <td>0.537503</td>\n",
-              "      <td>0.788395</td>\n",
-              "      <td>0.292680</td>\n",
-              "      <td>0.147968</td>\n",
-              "      <td>390.00</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>280143</th>\n",
-              "      <td>169347.0</td>\n",
-              "      <td>1.378559</td>\n",
-              "      <td>1.289381</td>\n",
-              "      <td>-5.004247</td>\n",
-              "      <td>1.411850</td>\n",
-              "      <td>0.442581</td>\n",
-              "      <td>-1.326536</td>\n",
-              "      <td>-1.413170</td>\n",
-              "      <td>0.248525</td>\n",
-              "      <td>-1.127396</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.370612</td>\n",
-              "      <td>0.028234</td>\n",
-              "      <td>-0.145640</td>\n",
-              "      <td>-0.081049</td>\n",
-              "      <td>0.521875</td>\n",
-              "      <td>0.739467</td>\n",
-              "      <td>0.389152</td>\n",
-              "      <td>0.186637</td>\n",
-              "      <td>0.76</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>280149</th>\n",
-              "      <td>169351.0</td>\n",
-              "      <td>-0.676143</td>\n",
-              "      <td>1.126366</td>\n",
-              "      <td>-2.213700</td>\n",
-              "      <td>0.468308</td>\n",
-              "      <td>-1.120541</td>\n",
-              "      <td>-0.003346</td>\n",
-              "      <td>-2.234739</td>\n",
-              "      <td>1.210158</td>\n",
-              "      <td>-0.652250</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.751826</td>\n",
-              "      <td>0.834108</td>\n",
-              "      <td>0.190944</td>\n",
-              "      <td>0.032070</td>\n",
-              "      <td>-0.739695</td>\n",
-              "      <td>0.471111</td>\n",
-              "      <td>0.385107</td>\n",
-              "      <td>0.194361</td>\n",
-              "      <td>77.89</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>281144</th>\n",
-              "      <td>169966.0</td>\n",
-              "      <td>-3.113832</td>\n",
-              "      <td>0.585864</td>\n",
-              "      <td>-5.399730</td>\n",
-              "      <td>1.817092</td>\n",
-              "      <td>-0.840618</td>\n",
-              "      <td>-2.943548</td>\n",
-              "      <td>-2.208002</td>\n",
-              "      <td>1.058733</td>\n",
-              "      <td>-1.632333</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.583276</td>\n",
-              "      <td>-0.269209</td>\n",
-              "      <td>-0.456108</td>\n",
-              "      <td>-0.183659</td>\n",
-              "      <td>-0.328168</td>\n",
-              "      <td>0.606116</td>\n",
-              "      <td>0.884876</td>\n",
-              "      <td>-0.253700</td>\n",
-              "      <td>245.00</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>281674</th>\n",
-              "      <td>170348.0</td>\n",
-              "      <td>1.991976</td>\n",
-              "      <td>0.158476</td>\n",
-              "      <td>-2.583441</td>\n",
-              "      <td>0.408670</td>\n",
-              "      <td>1.151147</td>\n",
-              "      <td>-0.096695</td>\n",
-              "      <td>0.223050</td>\n",
-              "      <td>-0.068384</td>\n",
-              "      <td>0.577829</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-0.164350</td>\n",
-              "      <td>-0.295135</td>\n",
-              "      <td>-0.072173</td>\n",
-              "      <td>-0.450261</td>\n",
-              "      <td>0.313267</td>\n",
-              "      <td>-0.289617</td>\n",
-              "      <td>0.002988</td>\n",
-              "      <td>-0.015309</td>\n",
-              "      <td>42.53</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>492 rows × 31 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b59ee2f1-bdc3-426e-abda-230559a44db3')\"\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 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\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",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\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-b59ee2f1-bdc3-426e-abda-230559a44db3 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-b59ee2f1-bdc3-426e-abda-230559a44db3');\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",
-              "\n",
-              "\n",
-              "<div id=\"df-718e79f5-ff1d-4cc1-a964-5a75c8ffb874\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-718e79f5-ff1d-4cc1-a964-5a75c8ffb874')\"\n",
-              "            title=\"Suggest charts\"\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",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-718e79f5-ff1d-4cc1-a964-5a75c8ffb874 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "  <div id=\"id_6648e005-c39e-44e9-9c70-3292f10d32b9\">\n",
-              "    <style>\n",
-              "      .colab-df-generate {\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-generate: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-generate {\n",
-              "        background-color: #3B4455;\n",
-              "        fill: #D2E3FC;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate: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",
-              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('fraud')\"\n",
-              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "    <script>\n",
-              "      (() => {\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#id_6648e005-c39e-44e9-9c70-3292f10d32b9 button.colab-df-generate');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      buttonEl.onclick = () => {\n",
-              "        google.colab.notebook.generateWithVariable('fraud');\n",
-              "      }\n",
-              "      })();\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "fraud"
-            }
-          },
-          "metadata": {},
-          "execution_count": 14
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "legit.shape"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "uUeLjBzJij16",
-        "outputId": "3d6804aa-21ee-448e-b2ae-4e7cc87a9805"
-      },
-      "execution_count": 15,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(284315, 31)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 15
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "fraud.shape"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "b3ZBMilnj4O0",
-        "outputId": "55551a02-9769-4c48-e08e-b7838164d2b5"
-      },
-      "execution_count": 16,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(492, 31)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 16
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "legit_s=legit.sample(n=492)\n",
-        "data=pd.concat([legit_s,fraud],axis=0)\n"
-      ],
-      "metadata": {
-        "id": "s0lx5ll0j4Tn"
-      },
-      "execution_count": 17,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "data.shape"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "UkxKUtyYj4aY",
-        "outputId": "9b87f275-4926-4e54-857f-dccb0945da11"
-      },
-      "execution_count": 18,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(984, 31)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 18
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "data['Class'].value_counts()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 178
-        },
-        "id": "BJ5HcAJCj4eG",
-        "outputId": "46a8d581-a79c-446e-a869-122c5e7cefa6"
-      },
-      "execution_count": 19,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Class\n",
-              "0    492\n",
-              "1    492\n",
-              "Name: count, dtype: int64"
-            ],
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>count</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Class</th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>492</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>492</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div><br><label><b>dtype:</b> int64</label>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 19
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "data.groupby('Class').mean()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 193
-        },
-        "id": "LXYEdIsAk-N1",
-        "outputId": "2959e942-e217-488c-b3df-c55034385fc9"
-      },
-      "execution_count": 20,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "               Time        V1        V2        V3        V4        V5  \\\n",
-              "Class                                                                   \n",
-              "0      96841.701220  0.047145 -0.045274  0.018299 -0.028750 -0.028220   \n",
-              "1      80746.806911 -4.771948  3.623778 -7.033281  4.542029 -3.151225   \n",
-              "\n",
-              "             V6        V7        V8        V9  ...       V20       V21  \\\n",
-              "Class                                          ...                       \n",
-              "0      0.061239  0.124701  0.012802  0.037197  ...  0.015074 -0.013212   \n",
-              "1     -1.397737 -5.568731  0.570636 -2.581123  ...  0.372319  0.713588   \n",
-              "\n",
-              "            V22       V23      V24       V25       V26       V27       V28  \\\n",
-              "Class                                                                        \n",
-              "0     -0.034433  0.048324 -0.00221  0.004126  0.006488 -0.022973 -0.016320   \n",
-              "1      0.014049 -0.040308 -0.10513  0.041449  0.051648  0.170575  0.075667   \n",
-              "\n",
-              "           Amount  \n",
-              "Class              \n",
-              "0      105.675793  \n",
-              "1      122.211321  \n",
-              "\n",
-              "[2 rows x 30 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-60f3df54-9f9e-4718-b2bf-d19fb5056d56\" 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>...</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",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Class</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>96841.701220</td>\n",
-              "      <td>0.047145</td>\n",
-              "      <td>-0.045274</td>\n",
-              "      <td>0.018299</td>\n",
-              "      <td>-0.028750</td>\n",
-              "      <td>-0.028220</td>\n",
-              "      <td>0.061239</td>\n",
-              "      <td>0.124701</td>\n",
-              "      <td>0.012802</td>\n",
-              "      <td>0.037197</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.015074</td>\n",
-              "      <td>-0.013212</td>\n",
-              "      <td>-0.034433</td>\n",
-              "      <td>0.048324</td>\n",
-              "      <td>-0.00221</td>\n",
-              "      <td>0.004126</td>\n",
-              "      <td>0.006488</td>\n",
-              "      <td>-0.022973</td>\n",
-              "      <td>-0.016320</td>\n",
-              "      <td>105.675793</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>80746.806911</td>\n",
-              "      <td>-4.771948</td>\n",
-              "      <td>3.623778</td>\n",
-              "      <td>-7.033281</td>\n",
-              "      <td>4.542029</td>\n",
-              "      <td>-3.151225</td>\n",
-              "      <td>-1.397737</td>\n",
-              "      <td>-5.568731</td>\n",
-              "      <td>0.570636</td>\n",
-              "      <td>-2.581123</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.372319</td>\n",
-              "      <td>0.713588</td>\n",
-              "      <td>0.014049</td>\n",
-              "      <td>-0.040308</td>\n",
-              "      <td>-0.10513</td>\n",
-              "      <td>0.041449</td>\n",
-              "      <td>0.051648</td>\n",
-              "      <td>0.170575</td>\n",
-              "      <td>0.075667</td>\n",
-              "      <td>122.211321</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>2 rows × 30 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60f3df54-9f9e-4718-b2bf-d19fb5056d56')\"\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 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\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",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\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-60f3df54-9f9e-4718-b2bf-d19fb5056d56 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-60f3df54-9f9e-4718-b2bf-d19fb5056d56');\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",
-              "\n",
-              "\n",
-              "<div id=\"df-8acc8022-65a3-410b-9478-fdf5c6375e4f\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8acc8022-65a3-410b-9478-fdf5c6375e4f')\"\n",
-              "            title=\"Suggest charts\"\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",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-8acc8022-65a3-410b-9478-fdf5c6375e4f button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe"
-            }
-          },
-          "metadata": {},
-          "execution_count": 20
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "x=data.drop('Class',axis=1)\n",
-        "y=data['Class']"
-      ],
-      "metadata": {
-        "id": "oS9EL437k-RD"
-      },
-      "execution_count": 21,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "x.shape"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "173lxqaYk-T0",
-        "outputId": "7129c9f5-a4f9-47c7-dacc-32e96c2959d2"
-      },
-      "execution_count": 22,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(984, 30)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 22
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "y.shape"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "eLd3clgFk-Xh",
-        "outputId": "ea5f1d59-1fc8-44a8-bf61-473af6bdf226"
-      },
-      "execution_count": 23,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(984,)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 23
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,stratify=y,random_state=2)"
-      ],
-      "metadata": {
-        "id": "-ns-kvj0lfsF"
-      },
-      "execution_count": 24,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model=LogisticRegression()\n",
-        "model.fit(x_train,y_train)\n",
-        "ypred=model.predict(x_test)\n",
-        "accuracy_score(y_test,ypred)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "wn2fxiwplfuv",
-        "outputId": "5a6146c5-3ce2-44fd-fb17-56dba4f8323b"
-      },
-      "execution_count": 25,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
-            "\n",
-            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
-            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
-            "Please also refer to the documentation for alternative solver options:\n",
-            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-            "  n_iter_i = _check_optimize_result(\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.949238578680203"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 25
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Data Visualization"
-      ],
-      "metadata": {
-        "id": "FRnVqs60vUyp"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
-        "\n",
-        "# Set the style\n",
-        "sns.set(style=\"whitegrid\")\n",
-        "\n",
-        "# Plot the distribution of the 'Class' variable (fraudulent vs. non-fraudulent transactions)\n",
-        "plt.figure(figsize=(8, 5))\n",
-        "sns.countplot(x='Class', data=data)\n",
-        "plt.title('Distribution of Fraudulent and Non-Fraudulent Transactions')\n",
-        "plt.xlabel('Class (0: Non-Fraud, 1: Fraud)')\n",
-        "plt.ylabel('Number of Transactions')\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 496
-        },
-        "id": "xp0SxKoBvUjW",
-        "outputId": "e2377079-5ee7-4b77-8468-f5d0afd09ca2"
-      },
-      "execution_count": 26,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 800x500 with 1 Axes>"
-            ],
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsEAAAHfCAYAAACxhQUCAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAW11JREFUeJzt3Xl8TPf+x/H3JBIEiaWWiiCJJo09WiKioaglYm0tVUtb+1ru7b1UN7rQ62rtO6UURVFaKUrVTqulKLXGll6xVRYJ2eb3h0fmZ0zCZDJZmNfz8eijzjnf+Z7PTCYn7/nO95xjMBqNRgEAAAAOxCmvCwAAAAByGyEYAAAADocQDAAAAIdDCAYAAIDDIQQDAADA4RCCAQAA4HAIwQAAAHA4hGAAAAA4HEIwAAAAHA4hGLlm2rRp8vf3z5V99ejRQz169DAt79+/X/7+/tq4cWOu7H/UqFFq0qRJruzLVrdu3dLbb7+tkJAQ+fv76+OPP87rkrIl/We8f//+LD/20qVL8vf315o1a3KgsvwvN383HZ2/v7+mTZtm02ObNGmiUaNG2bki5ITs/JyRewrkdQF4NK1Zs0ZvvfWWadnV1VUeHh7y9/dXo0aN1LFjRxUtWjTb+4mOjtbKlSvVrFkzBQQEZLs/e8rPtVljzpw5Wrt2rQYNGiQvLy/5+vpm2rZJkyaKiorKcNvhw4dVsGDBnCrzsbJ06VIVLlxYHTt2zOtSbDJq1CitXbtWfn5+Wr9+vQwGg9l2f39/vfLKK3rvvffyrLaMzJs3T6Ghoblc0aNp+/btOnz4sIYOHfrAdvf/DciMp6enfvzxR3uVl69Y+1oh/yIEI1uGDRumChUqKCUlRdeuXdPPP/+scePGadGiRZo5c6aefvppU9uBAweqX79+Wer/ypUrmj59ujw9PbMUNBcsWJCl/djiQbV9+OGHMhqNOV5Dduzbt0+1atXSkCFDrGofEBCg1157zWK9i4uLvUt7bC1fvlwlSpR4ZENwupMnT2rz5s1q0aJFXpdixtXVVR999JHF+nuPQ3iw7du3a+nSpQ8NdnXr1tWECRPM1r3zzjuqWbOmOnfubFpXpEiRHKkzP3jQa3X48GE5OzvnQVXICkIwsiU0NFQ1atQwLffv31979+7VgAEDNGjQIEVERKhQoUKSpAIFCqhAgZx9yyUmJqpw4cJydXXN0f08zKMQDK9fv64qVapY3b5s2bJq166d1e3TfxZ4vBQqVEjlypXTjBkz1Lx5c4vR4LxUoECBLL1HExIS5ObmloMVPb68vLzk5eVltm7MmDHy8vJ64M8gJSVFaWlpeX6Mzml8O/ZoYE4w7C44OFiDBg1SVFSU1q9fb1qf0bzD3bt36+WXX9azzz6rwMBAtWjRQp999pmku3M8X3rpJUnSW2+9JX9/f7N5mz169FB4eLiOHj2qV155RbVq1TI99v45wenS0tL02WefKSQkRLVr19aAAQP0v//9z6xNZvPu7u3zYbVlNCc4ISFBn3zyiRo1aqTq1aurRYsWWrBggcWIsb+/vz744ANt2bJF4eHhql69ulq3bq0dO3Y86GU3uX79ukaPHq0GDRqoRo0aatu2rdnXxOlzZy9duqSffvrJVPulS5es6j8jD/pZbNmyRf369VPDhg1VvXp1NWvWTDNmzFBqaqpZH9a87ukuX76sQYMGqXbt2goODta4ceOUlJRk8dis9JmRM2fOaNiwYapXr55q1Kihjh07auvWrWZt1qxZI39/f/36668aP3686tevr9q1a2vw4MG6ceOGWS2nTp3Szz//bHrNH1bDggUL1LVrVwUFBalmzZrq2LFjhvPas/KeOXDggF588UXVqFFDzZo101dfffXQ1+FeTk5OGjhwoE6cOKEffvjhoe0f9n6U/n9O9oIFC7RixQo1a9ZM1atX14svvqjDhw9nqb7MpB9/Tp8+rX/+85+qW7euunXrJkn6888/NWrUKDVt2lQ1atRQSEiI3nrrLf39999mfWQ21z+jY1tSUpLGjRun+vXrKzAwUAMGDNDly5ctHpuVPjMSGxurjz/+2HRceeGFFzR37lylpaWZ2lj7+o4aNUpLly6VJNN7NDtzxe/d76JFi9SsWTPVqFFDZ86cUVJSkqZMmaKOHTvqmWeeUe3atdWtWzft27cv0z4e9t64evWq3nrrLYWGhqp69epq2LChBg4caHZss/Z4JEm///67+vbtq7p166p27dpq06aNvvjiC6teq4zmBB87dkx9+vRRnTp1FBgYqF69eunQoUNmbaw9nkjSkSNH1Lt3b9PxoUmTJlZNUcH/YyQYOaJdu3b67LPPtGvXLrOvxu516tQp9e/fX/7+/ho2bJhcXV11/vx5/fbbb5IkX19fDRs2TFOnTlWXLl30zDPPSJLq1Klj6uPmzZvq27evWrdurbZt26pUqVIPrGvWrFkyGAzq27evrl+/ri+++EKvvvqq1q1bZxqxtoY1td3LaDRq4MCBpvAcEBCgnTt3asKECYqOjtbo0aPN2v/666/avHmzunXrpiJFimjJkiUaNmyYtm3bphIlSmRa1+3bt9WjRw9duHBBr7zyiipUqKCNGzdq1KhRio2NVa9eveTr66sJEyZo/PjxKleunGmKQ8mSJR/4nFNSUiwOwoULFzaN9mb2s1i7dq3c3Nz02muvyc3NTfv27dPUqVMVHx+vkSNHPnCfmT3HXr166X//+5969OihMmXKaN26dRZ/PLPr1KlTevnll1W2bFn17dtXbm5u+v777zV48GBNmzZNL7zwgln7jz76SO7u7hoyZIiioqL0xRdf6IMPPtDkyZMlSaNHj9aHH34oNzc3DRgwQJL0xBNPPLCGxYsXq0mTJmrTpo2Sk5O1YcMGvfHGG5ozZ44aN25s1taa98yJEyfUu3dvlSxZUkOHDlVKSoqmTZv20N+b+7Vp00azZs3SjBkz9MILL2Q6GmzN+/Fe3333nW7duqUuXbrIYDBo/vz5Gjp0qLZs2WL1tyv3v0ddXFxUrFgx0/Ibb7yhSpUqacSIEaYPoHv27NHFixfVsWNHlS5dWqdOndLKlSt1+vRprVy50qbR7rffflvr169XeHi46tSpo3379mV5OtjDJCYmqnv37oqOjlbXrl315JNP6uDBg/rss8909epVvf3222btH/b6dunSRVeuXNHu3bstpjpkx5o1a3Tnzh117tzZdP5IfHy8Vq1apfDwcHXq1Em3bt3S119/rT59+mjVqlUWU8yseW8MHTpUp0+fVvfu3eXp6akbN25o9+7d+t///qcKFSpIsv54tHv3bvXv319lypRRz5499cQTT+jMmTP66aef1KtXryy/VqdOndIrr7yiIkWKqE+fPipQoIBWrFihHj166Msvv1StWrXM2j/seHL9+nX17t1bJUqUUL9+/eTu7q5Lly5Z9cEU9zACNli9erXRz8/PePjw4UzbPPPMM8b27dublqdOnWr08/MzLS9cuNDo5+dnvH79eqZ9HD582Ojn52dcvXq1xbbu3bsb/fz8jMuXL89wW/fu3U3L+/btM/r5+Rmfe+45Y1xcnGl9RESE0c/Pz/jFF1+Y1j3//PPGkSNHPrTPB9U2cuRI4/PPP29a/uGHH4x+fn7GmTNnmrUbOnSo0d/f33j+/HnTOj8/P2O1atXM1h0/ftzo5+dnXLJkicW+7rVo0SKjn5+fcd26daZ1SUlJxi5duhhr165t9tyff/55Y79+/R7Y371t/fz8LP6bOnWq0Wh88M8iMTHRYt27775rrFWrlvHOnTtm+7DmdU9/jhEREaZ1CQkJxhdeeMHo5+dn3LdvX5b7vHjxosXPslevXsbw8HCzGtPS0oxdunQxNm/e3LQu/Xfh1VdfNaalpZnWjxs3zhgQEGCMjY01rWvdurXZfh/m/tcuKSnJGB4ebuzZs6fZemvfM4MGDTLWqFHDGBUVZVp3+vRpY0BAgNnvZmZGjhxprF27ttFoNBrXrl1r9PPzM27evNmsjrFjx5qWrX0/pr/+9erVM968edPUdsuWLUY/Pz/jjz/+aFVtGb1H01/v9OPPP/7xD4vHZvQe/e6774x+fn7GX375xWwf9/5ep7v/2Jb+2o8ZM8as3T/+8Q+z35us9Gk0Wr6fZ8yYYaxdu7YxMjLSrN3EiRONAQEBxr/++stoNGbt9R07dqxV74WM1K5d26y+9P3WqVPH4jifkpJi9rtlNBqNMTExxgYNGhjfeustiz4eVntMTIzRz8/POH/+/AfWaM3xKCUlxdikSRPj888/b4yJiTFre+/v+INeq/t/zoMGDTJWq1bNeOHCBdO66OhoY2BgoPGVV14xrbP2eJL+N+VBf4PxcEyHQI5xc3PTrVu3Mt3u7u4uSdq6davZV3dZ4erqmqWTjNq3b2921YqWLVuqdOnS2r59u037t9aOHTvk7Oxs8fX366+/LqPRaPG1dYMGDVSxYkXT8tNPP62iRYvq4sWLD91P6dKlFR4eblrn4uKiHj16KCEhQb/88ovNz6FWrVpauHCh2X/t27c3bc/sZ3HvCHt8fLxu3LihZ599VomJiTp79myW60h/ji1btjStK1y4cKbfONji5s2b2rdvn1q1amWq+caNG/r777/VsGFDnTt3TtHR0WaP6dy5s9mI4bPPPqvU1NRMr6phjXtfu5iYGMXFxemZZ57RsWPHLNo+7D2TmpqqXbt2qVmzZipfvrypna+vrxo2bJjl2tq0aaPKlStrxowZmZ4EmtX3Y1hYmDw8PEzLzz77rCQ99H2frmDBghbv0fu/bejatavF4+59ne/cuaMbN26YRub++OMPq/Z9r/Tjyf2/7/ePfGfXxo0b9cwzz8jd3d30Hr1x44YaNGig1NRUu7++tmrevLnFN03Ozs6mecFpaWm6efOmUlJSVL169Qzf3w+rvVChQnJxcdHPP/+smJiYTGux5nh07NgxXbp0ST179jT9nUpny7cCqamp2r17t5o1a2Y2j7pMmTIKDw/Xr7/+qvj4eLPHPOx4kv7txk8//aTk5OQs14S7mA6BHJOQkPDAr1nDwsK0atUqvfPOO/r0008VHBysF154QS1btpSTk3Wfz8qWLZulEywqVapktmwwGFSpUqVsBRVrREVFqUyZMhaXjUu/LNn9+3/yySct+vDw8FBsbOxD91OpUiWL1y99P3/99VeWa09XokQJNWjQINPtmf0sTp06pcmTJ2vfvn0WB/q4uLgs15H+HO//Y+Tt7Z3lvjJz4cIFGY1GTZkyRVOmTMmwzfXr11W2bFnT8r3BUvr/D3kP+5k9yLZt2zRr1iwdP37cbM5zRn+IH/aeuXHjhm7fvm3xOyDdfe2y+kHQ2dlZAwcO1MiRI7VlyxaL6SFS1t+P9z+H9NCT/hxu375t8Z4pXbq0WU0Peo9KMn0tfq+bN29q+vTpioiI0PXr18222foedXJyMvtQIkk+Pj5Z7utBzp8/rxMnTig4ODjD7fdPDXnY65tTMnrNpbtTEz7//HNFRkaaBbmM2j+sdldXV7355pv6z3/+o5CQENWqVUuNGzdW+/btzd4j1hyP0oO1n59fVp9qhm7cuKHExMQMj1G+vr5KS0vT//73Pz311FOm9Q87ntSrV08tWrTQ9OnTtWjRItWrV0/NmjVTmzZtHvuTDu2JEIwccfnyZcXFxVn8EbhXoUKFtHTpUu3fv18//fSTdu7cqYiICK1YsUKff/65VZeXyco83uxKTU3NtUveZLafzEbc8oOMfhaxsbHq3r27ihYtqmHDhqlixYoqWLCg/vjjD02cONGqbwBy4nV/WJ/pdb3++ut67rnnMmxz/3s7sw9utv7MDhw4oIEDB6pu3bp6//33Vbp0abm4uGj16tX67rvvLNrnxXumTZs2mjlzpmbMmKFmzZplu7+HPYeIiAiLE39OnDiRpX1kdNb+8OHDdfDgQfXu3VsBAQFyc3NTWlqa+vTpY/b6ZTYKmNFJVdbKTp9paWkKCQlRnz59MtxeuXJls+W8Oq5kdGxYt26dRo0apWbNmql3794qVaqUnJ2dNWfOnAxHpq2p/dVXX1WTJk20ZcsW7dq1S1OmTNHcuXP1xRdfqGrVqnY5HuWWhx1PDAaDpk6dqkOHDmnbtm3auXOnRo8erYULF2rFihWP9aXp7IkQjByxbt06SXro16xOTk4KDg5WcHCw3nrrLc2ePVuTJk3S/v371aBBA7tffun8+fNmy0ajUefPnzc7qzezEde//vrL7KusrNTm6empvXv3Kj4+3mw0OP3rN09PT6v7eth+Tpw4obS0NLODaPp+7h9dyGk///yzaZStbt26pvUZXYnC2tfd09NTJ0+elNFoNPsZREZG2tzn/dK3ubi4PHRkMSuy8p7ZtGmTChYsqAULFpiN7KxevdqmfZcsWVKFChWy+B2QMn7trJE+Gjxq1CiLq2ZI9n8/NmzYUAsXLrSp1szExMRo7969Gjp0qNk1s8+dO2fR1t3dPdP30708PT2VlpamCxcumI3+ZjT9x9o+M1KxYkUlJCTk2Xs0OzZt2iQvLy9Nnz7dbJ9Tp07NVr8VK1bU66+/rtdff13nzp1T+/bt9fnnn2vixIlWH4/Sf/9Pnjz5wNfW2teqZMmSKly4cIa/Z2fPnpWTk1OG3+RYo3bt2qpdu7ZGjBihb7/9Vm+++aYiIiLUqVMnm/pzNMwJht3t3btXM2fOVIUKFdS2bdtM2928edNiXfoZwelf/aZfecBeX9d98803Zl+Bbdy4UVevXjW7m5SXl5d+//13s6+ft23bZnEptazUFhoaqtTUVNMlddItWrRIBoPBbnezCg0N1dWrVxUREWFal5KSoiVLlsjNzc3swJ8b0oPPvaM1SUlJWrZsmUVba1/30NBQXblyxexSYYmJiVq5cqXNfd6vVKlSqlevnlasWKErV65YbL//a2ZrFS5c2Or3srOzswwGg9mI4KVLlzIMm9b217BhQ23ZssUsYJ05c0a7du2yqU9Jatu2rSpVqqTp06dbbLP3+7FMmTJq0KCB2X/ZldkIY/qlsO5VsWJFxcXF6c8//zStu3LlisUZ+em/z0uWLLFbnxlp1aqVDh48qJ07d1psi42NVUpKykP7uJ+9j7mZSX/d7z02/P777xaXDLNWYmKi7ty5Y7auYsWKKlKkiOn339rjUbVq1VShQgUtXrzY4nW497HWvlbOzs4KCQnR1q1bzQL3tWvX9N133+mZZ57J8h1WY2JiLEbw7//7iYdjJBjZsmPHDp09e1apqam6du2a9u/fr927d6t8+fKaNWvWAy8YPmPGDB04cECNGjWSp6enrl+/rmXLlqlcuXKmS45VrFhR7u7u+uqrr1SkSBG5ubmpZs2aDxzFexAPDw9169ZNHTt2NF0irVKlSmYnVXXq1EmbNm1Snz591KpVK124cEHffvutxdffWamtSZMmCgoK0qRJkxQVFSV/f3/t3r1bW7duVa9evR44bSQrunTpohUrVmjUqFH6448/5OnpqU2bNum3337T6NGj7XIr66wIDAyUh4eHRo0apR49eshgMGjdunUZfv1q7eveuXNnLV26VCNHjtQff/yh0qVLZ3qJO2v7zMj777+vbt26qU2bNurcubO8vLx07do1HTp0SJcvXza7Bra1qlWrpuXLl2vmzJmqVKmSSpYsmel8zkaNGmnhwoXq06ePwsPDTb8fFStWzPIUgHRDhw7Vzp079corr+jll19WamqqvvzyS1WpUsXmPp2dnTVgwIAMr0+a396PGSlatKjq1q2r+fPnKzk5WWXLltXu3bsz/LYiLCxMEydO1JAhQ9SjRw/dvn1by5cvl7e3t9kJdAEBAQoPD9eyZcsUFxenwMBA7du3L8NReGv7zEjv3r31448/asCAAerQoYOqVaumxMREnTx5Ups2bdLWrVsfeunD+1WrVk3S3Ut0NWzYUM7OzmrdunWW+rBG48aNtXnzZg0ePFiNGzfWpUuX9NVXX6lKlSpKSEjIcn/nzp3Tq6++qpYtW6pKlSpydnbWli1bdO3aNVP91h6PnJycNGbMGA0cOFDt27c3XTrv7NmzOn36tOmOpFl5rYYPH649e/aoW7du6tatm5ydnbVixQolJSXpX//6V5af79q1a7V8+XI1a9ZMFStW1K1bt7Ry5UoVLVqUW4RnASEY2ZL+1ZWLi4uKFy8uPz8/jR49Wh07dnzoH7gmTZooKipKq1ev1t9//60SJUqoXr16Gjp0qOnMVxcXF33yySf67LPPNGbMGKWkpGj8+PE2h+ABAwboxIkTmjt3rm7duqXg4GC9//77Znc2e+655zRq1CgtXLhQ48aNU/Xq1TV79mz95z//MesrK7U5OTlp1qxZmjp1qiIiIrRmzRp5enrq3//+t15//XWbnktGChUqpCVLlmjixIlau3at4uPj5e3trfHjx+fJrXpLlChheu0mT54sd3d3tW3bVsHBwerdu7dZW2tf98KFC2vRokX68MMP9eWXX6pQoUJq06aNQkNDLeZGWttnRqpUqaLVq1dr+vTpWrt2rW7evKmSJUuqatWqGjx4sE2vx+DBg/XXX39p/vz5unXrlurVq5dpCA4ODtbHH3+sefPmady4capQoYLefPNNRUVF2RxYn376aS1YsEDjx4/X1KlTVa5cOQ0dOlRXr161uU/p7mjwrFmzdOHCBbP1+e39mJlPP/1UH374oZYtWyaj0aiQkBDNmzfPYj54iRIlNH36dH3yySf673//qwoVKugf//iHzp8/bxFYx40bpxIlSujbb7/V1q1bFRQUpLlz56pRo0Y293m/woULa8mSJZozZ442btyob775RkWLFlXlypXNjqNZ0bx5c/Xo0UMbNmzQ+vXrZTQacyQEd+zYUdeuXdOKFSu0a9cuValSRf/973+1ceNG/fzzz1nur1y5cmrdurX27t2r9evXy9nZWT4+Ppo8ebLp9t5ZPR598cUXmjFjhj7//HMZjUZ5eXmZDZhk5bV66qmntHTpUn366aeaM2eOjEajatasqf/+978W1wi2Rr169XTkyBFFRETo2rVrKlasmGrWrKmJEyfa/PfRERmM+flMGwAAACAHMCcYAAAADocQDAAAAIdDCAYAAIDDIQQDAADA4RCCAQAA4HAIwQAAAHA4XCc4Cw4ePCij0SgXF5e8LgUAAAAZSE5OlsFgUGBg4APbEYKzwGg0ZninKwAAAOQP1mY1QnAWpI8A16hRI48rAQAAQEaOHDliVTvmBAMAAMDhEIIBAADgcAjBAAAAcDiEYAAAADgcQjAAAAAcTr4KwWvWrJG/v7/FfxMnTjRrt2rVKrVo0UI1atRQ27ZttW3bNou+4uLiNHr0aNWrV0+BgYEaNmyYrly5kltPBQAAAPlYvrxE2vz581WsWDHTctmyZU3/3rBhg959910NGDBA9evXV0REhIYMGaKlS5eqdu3apnbDhw/X6dOnNWbMGBUsWFCTJ09W3759tXr1ahUokC+fNgAAAHJJvkyD1apVU8mSJTPcNnXqVLVu3VrDhw+XJNWvX18nT57UjBkzNG/ePEl37+y2a9cuLViwQA0bNpQkeXt7KywsTJs3b1ZYWFiuPA8AAADkT/lqOsTDXLx4UefOnVOrVq3M1oeFhWnv3r1KSkqSJO3YsUPu7u4KCQkxtfHx8VFAQIB27NiRqzUDAAAg/8mXITg8PFwBAQFq2rSp5syZo9TUVEnS2bNnJd0d1b2Xr6+vkpOTdfHiRVM7b29vGQwGs3Y+Pj6mPgAAAOC48tV0iNKlS2vo0KGqVauWDAaDfvzxR02ePFnR0dF67733FBMTI0lyd3c3e1z6cvr22NhYsznF6Tw8PHT06NFs1Wg0GpWQkJCtPgAAAJAzjEajxUBoRvJVCH7uuef03HPPmZYbNmyoggUL6osvvtCAAQPysLL/l5ycrOPHj+d1GQAAAMiEq6vrQ9vkqxCckVatWunzzz/X8ePH5eHhIenu5c9Kly5tahMbGytJpu3u7u66fPmyRV8xMTGmNrZycXFRlSpVstUHAAAAcsbp06etapfvQ/C9fHx8JN2d85v+7/RlFxcXeXl5mdrt3bvXYjg8MjJSfn5+2arBYDDIzc0tW30AAAAgZ1gzFULKpyfG3SsiIkLOzs6qWrWqvLy8VLlyZW3cuNGiTXBwsGnoOzQ0VDExMdq7d6+pTWRkpI4dO6bQ0NBcrR8AAAD5T74aCe7du7eCgoLk7+8vSdq6datWrlypnj17mqY/DB06VG+++aYqVqyooKAgRURE6PDhw/ryyy9N/QQGBqphw4YaPXq0Ro4cqYIFC2rSpEny9/dX8+bN8+S5AQAAIP/IVyHY29tbq1ev1uXLl5WWlqbKlStr9OjR6tGjh6lNeHi4EhMTNW/ePM2dO1fe3t6aPn26AgMDzfqaPHmyxo8fr/fee08pKSlq2LCh3nnnHe4WBwAAABmMRqMxr4t4VBw5ckSSVKNGjTzZf1qaUU5O1s1zAfBocdTfb0d93oAjyKvfb2vzGsOijxAnJ4NmLN+tqCsxeV0KADvyLOOhwS+HPLzhY4jjGvB4ehSOa4TgR0zUlRidi/o7r8sAALvhuAYgL+T7q0MAAAAA9kYIBgAAgMMhBAMAAMDhEIIBAADgcAjBAAAAcDiEYAAAADgcQjAAAAAcDiEYAAAADocQDAAAAIdDCAYAAIDDIQQDAADA4RCCAQAA4HAIwQAAAHA4hGAAAAA4HEIwAAAAHA4hGAAAAA6HEAwAAACHQwgGAACAwyEEAwAAwOEQggEAAOBwCMEAAABwOIRgAAAAOBxCMAAAABwOIRgAAAAOhxAMAAAAh0MIBgAAgMMhBAMAAMDhEIIBAADgcAjBAAAAcDiEYAAAADgcQjAAAAAcDiEYAAAADocQDAAAAIdDCAYAAIDDIQQDAADA4RCCAQAA4HAIwQAAAHA4hGAAAAA4HEIwAAAAHA4hGAAAAA6HEAwAAACHQwgGAACAwyEEAwAAwOEQggEAAOBwCMEAAABwOIRgAAAAOBxCMAAAABwOIRgAAAAOhxAMAAAAh0MIBgAAgMMhBAMAAMDhEIIBAADgcAjBAAAAcDiEYAAAADgcQjAAAAAcDiEYAAAADocQDAAAAIdDCAYAAIDDIQQDAADA4eTbEHzr1i2FhobK399fR44cMdu2atUqtWjRQjVq1FDbtm21bds2i8fHxcVp9OjRqlevngIDAzVs2DBduXIlt8oHAABAPpZvQ/DMmTOVmppqsX7Dhg1699131apVK82bN0+1a9fWkCFDdOjQIbN2w4cP1+7duzVmzBhNnDhRkZGR6tu3r1JSUnLpGQAAACC/ypch+MyZM1q2bJmGDh1qsW3q1Klq3bq1hg8frvr16+uDDz5QjRo1NGPGDFObgwcPateuXfr4448VFhampk2basqUKTpx4oQ2b96cm08FAAAA+VC+DMEfffSRunbtKm9vb7P1Fy9e1Llz59SqVSuz9WFhYdq7d6+SkpIkSTt27JC7u7tCQkJMbXx8fBQQEKAdO3bk/BMAAABAvpbvQvDGjRt18uRJDR482GLb2bNnJckiHPv6+io5OVkXL140tfP29pbBYDBr5+PjY+oDAAAAjqtAXhdwr8TERH3yyScaMWKEihYtarE9JiZGkuTu7m62Pn05fXtsbKyKFStm8XgPDw8dPXo0WzUajUYlJCRkqw9bGAwGFS5cONf3CyD3JCYmymg05nUZuYbjGvD4y4vjmtFotBgIzUi+CsGzZs1SqVKl9OKLL+Z1KZlKTk7W8ePHc32/hQsXVtWqVXN9vwByT2RkpBITE/O6jFzDcQ14/OXVcc3V1fWhbfJNCI6KitLnn3+uGTNmKC4uTpJMI64JCQm6deuWPDw8JN29/Fnp0qVNj42NjZUk03Z3d3ddvnzZYh8xMTGmNrZycXFRlSpVstWHLaz5RAPg0ebt7e1wI8EAHm95cVw7ffq0Ve3yTQi+dOmSkpOT1a9fP4ttPXv2VK1atfTpp59Kujvn18fHx7T97NmzcnFxkZeXl6S7c3/37t1rMRweGRkpPz+/bNVpMBjk5uaWrT4AICNMDQDwuMmL45q1H7DzTQgOCAjQ4sWLzdYdP35c48eP19ixY1WjRg15eXmpcuXK2rhxo5o1a2ZqFxERoeDgYNPQd2hoqGbOnKm9e/eqQYMGku4G4GPHjqlPnz6596QAAACQL+WbEOzu7q6goKAMt1WrVk3VqlWTJA0dOlRvvvmmKlasqKCgIEVEROjw4cP68ssvTe0DAwPVsGFDjR49WiNHjlTBggU1adIk+fv7q3nz5rnyfAAAAJB/5ZsQbK3w8HAlJiZq3rx5mjt3rry9vTV9+nQFBgaatZs8ebLGjx+v9957TykpKWrYsKHeeecdFSjwyD1lAAAA2Fm+ToRBQUE6ceKExfpOnTqpU6dOD3xssWLFNG7cOI0bNy6nygMAAMAjKt/dLAMAAADIaXYNwRcvXtSZM2fs2SUAAABgdzaF4MWLF2vEiBFm69566y01b95c4eHh6tixo65fv26XAgEAAAB7sykEr1q1SqVKlTIt79y5U2vXrlXnzp31zjvv6NKlS5o+fbrdigQAAADsyaYT4/766y/5+vqalr///ntVqFBBY8eOlSRdu3ZN69ats0+FAAAAgJ3ZNBJ8/+3vdu/erdDQUNOyp6enrl27lr3KAAAAgBxiUwiuXLmytmzZIunuVIgrV66YheDLly/L3d3dPhUCAAAAdmbTdIjevXvrn//8p+rWravExET5+vqqYcOGpu379+/X008/bbciAQAAAHuyKQS3bt1axYsX1/bt2+Xu7q5u3bqZ7sR28+ZNeXh4qF27dnYtFAAAALAXm+8YFxISopCQEIv1xYsX58oQAAAAyNe4YxwAAAAcjk0jwUajUStWrNDXX3+tixcvKjY21qKNwWDQsWPHsl0gAAAAYG82heAJEyZo0aJFCggIUNu2beXh4WHvugAAAIAcY1MI/uabb9S8eXNNmTLF3vUAAAAAOc6mOcG3b99WgwYN7F0LAAAAkCtsCsHBwcE6cuSIvWsBAAAAcoVNIfj999/X77//rtmzZ+vvv/+2d00AAABAjrJpTnDLli1lNBo1ZcoUTZkyRQULFpSTk3meNhgM+vXXX+1SJAAAAGBPNoXgFi1ayGAw2LsWAAAAIFfYFII/+eQTe9cBAAAA5BruGAcAAACHY9NIsCTFx8dr0aJF+umnn/TXX39JksqXL6/GjRvr1VdfVdGiRe1WJAAAAGBPNo0ER0dHq3379po+fboSEhJUp04d1alTR4mJiZo+fbo6dOigK1eu2LtWAAAAwC5sGgmeOHGirl27pjlz5qhRo0Zm27Zv367hw4fr008/1X/+8x+7FAkAAADYk00jwTt37lSvXr0sArAkNWrUSD169ND27duzXRwAAACQE2wKwYmJiSpVqlSm25944gklJibaXBQAAACQk2wKwb6+vtqwYYOSkpIstiUnJ2vDhg3y9fXNdnEAAABATrBpTnDfvn01YsQIderUSd26dVPlypUlSZGRkfrqq6904sQJTZo0yZ51AgAAAHZjUwhu1aqVEhMT9emnn+r999833T3OaDSqVKlSGjdunFq2bGnXQgEAAAB7sfk6wR07dlTbtm119OhRs+sEV69eXQUK2NwtAAAAkOOylVYLFCig2rVrq3bt2nYqBwAAAMh5VoXgX375RZJUt25ds+WHSW8PAAAA5CdWheAePXrIYDDo999/l6urq2k5M0ajUQaDQcePH7dboQAAAIC9WBWCFy9eLElydXU1WwYAAAAeRVaF4Hr16j1wGQAAAHiU2HSzjJ49e2rv3r2Zbt+3b5969uxpc1EAAABATrIpBP/888+6du1apttv3Lhh9clzAAAAQG6zKQRLeuCJcefPn1eRIkVs7RoAAADIUVZfJ3jt2rVau3ataXnWrFlauXKlRbu4uDidOHFCoaGh9qkQAAAAsDOrQ3BiYqL+/vtv0/KtW7fk5GQ5kOzm5qauXbtq8ODB9qkQAAAAsDOrQ3C3bt3UrVs3SVKTJk309ttvq2nTpjlWGAAAAJBTbLpt8o8//mjvOgAAAIBcY9OJcXv27NFnn32W6fZJkyY98BJqAAAAQF6yKQTPnDlT//vf/zLdHh0drVmzZtlcFAAAAJCTbArBJ0+eVK1atTLdXqNGDZ04ccLmogAAAICcZFMITkpKUnJy8gO337592+aiAAAAgJxkUwh+6qmn9MMPP2S4zWg0avPmzfL19c1WYQAAAEBOsSkEd+/eXb/99puGDRumEydOKCUlRSkpKfrzzz/1xhtv6NChQ+rRo4e9awUAAADswqZLpLVr104XL17UzJkz9cMPP5humpGWliaDwaCBAweqQ4cOdi0UAAAAsBebQrAkDRkyRG3bttUPP/ygixcvSpIqVqyoZs2aqWLFinYrEAAAALA3m0OwdDf09u7d2161AAAAALnCpjnBAAAAwKPM5pHg7du3a9GiRTp27Jji4uJkNBot2hw/fjxbxQEAAAA5waaR4E2bNmnAgAG6du2awsLClJaWptatWyssLEyFChWSv7+/Bg8ebO9aAQAAALuwaSR4zpw5qlmzppYtW6aYmBgtX75cL774ooKDg3Xp0iV16dJFFSpUsHetAAAAgF3YNBJ85swZhYWFydnZWQUK3M3RKSkpkqQKFSro5Zdf1rx58+xXJQAAAGBHNoXgQoUKycXFRZLk7u4uV1dXXb161bT9iSee0KVLl+xTIQAAAGBnNoVgb29vnTlzxrQcEBCgdevWKSUlRXfu3NF3332nJ5980m5FAgAAAPZkUwh+4YUXtHXrViUlJUmSBgwYoJ9//ll169ZV/fr1deDAAfXr18+uhQIAAAD2YtOJcb179za7Scbzzz+vJUuWaNOmTSpQoIAaNWqk+vXr261IAAAAwJ6ydce4ez377LN69tln7dUdAAAAkGPsFoITExO1YcMGJSUlqVGjRvL09LRX1wAAAIBd2RSCR48ercOHD+u7776TJCUlJalz5846deqUJKlYsWL64osvVLVq1Sz1u337ds2bN0+nT59WfHy8ypYtq2bNmmnIkCEqVqyYqd2PP/6oyZMnKzIyUuXLl1e/fv304osvmvWVlJSkSZMmaf369bp165YCAwP17rvvysfHx5anDAAAgMeITSfG7d+/Xy+88IJp+bvvvtOpU6c0ceJEfffdd3riiSc0ffr0LPd78+ZN1axZU2PHjtWCBQv02muv6ZtvvtEbb7xhanPgwAENGTJEtWvX1rx589SqVSu9/fbb2rhxo1lfH330kVatWqURI0Zo2rRpSkpK0quvvqq4uDhbnjIAAAAeIzaNBF+7ds1susOWLVtUvXp1hYeHS5I6d+6sBQsWZLnfdu3amS0HBQXJ1dVV7777rqKjo1W2bFnNmjVLNWvW1AcffCBJql+/vi5evKipU6eqZcuWkqTLly/r66+/1vvvv6+XXnpJklSjRg09//zz+uqrr9S3b19bnjYAAAAeEzaNBBcuXNg0opqSkqKff/5ZDRs2NG0vUqSI3UZcixcvLklKTk5WUlKS9u/fbwq76cLCwnTmzBnTDTp27dqltLQ0s3bFixdXSEiIduzYYZe6AAAA8OiyKQRXq1ZNK1eu1LFjxzR79mzdunVLTZo0MW2/cOGCSpUqZXNRqampunPnjv744w/NmDFDTZo0UYUKFXThwgUlJydbzOv19fWVJJ09e9b0/1KlSsnDw8OiXXobAAAAOC6bpkMMHz5cffr00Ysvviij0agWLVqoZs2apu0//PCD6tSpY3NRzz//vKKjoyVJzz33nD799FNJUkxMjKS7t2q+V/py+vbY2FizE+nubZfexlZGo1EJCQnZ6sMWBoNBhQsXzvX9Asg9iYmJMhqNeV1GruG4Bjz+8uK4ZjQaZTAYHtrOphBco0YNff/99/rtt9/k7u6uevXqmbbFxsaqW7duZuuyau7cuUpMTNTp06c1a9YsDRgwQAsXLrS5P3tKTk7W8ePHc32/hQsXzvLVNgA8WiIjI5WYmJjXZeQajmvA4y+vjmuurq4PbWPzdYJLliypZs2aWax3d3dXr169bO1WkvT0009LkgIDA1WjRg21a9dOP/zwg6pUqSJJFvONY2NjJck0/cHd3V3x8fEW/cbGxlpMkcgqFxcXUx25yZpPNAAebd7e3g43Egzg8ZYXx7XTp09b1S5bN8uIj4/XX3/9pdjY2AyfYN26dbPTvSTJ399fLi4uunDhgpo0aSIXFxedPXtWzz33nKlN+jzf9LnCPj4+unbtmmJiYsxC79mzZ7N9nWCDwSA3N7ds9QEAGWFqAIDHTV4c16z9gG1TCP7777/14YcfavPmzUpNTbXYnj4Xwx7TBn7//XclJyerQoUKcnV1VVBQkDZt2mQ22hwRESFfX19VqFBBktSwYUM5OTlp8+bN6tSpk6S784V37dqlQYMGZbsmAAAAPNpsCsHvvvuutm3bph49eujZZ5+1OFHNVkOGDFH16tXl7++vQoUK6c8//9SCBQvk7+9vmnoxcOBA9ezZU2PGjFGrVq20f/9+fffdd5o0aZKpn3Llyumll17ShAkT5OTkpLJly2rOnDkqVqyYunbtapdaAQAA8OiyKQTv3r1bvXr10r///W+7FlOzZk1FRERo7ty5MhqN8vT0VKdOndS7d2/TBOdnn31W06ZN0+TJk/X111+rfPny+uijj9SqVSuzvt555x0VKVJEn376qW7duqU6depo4cKFGV41AgAAAI7FphBcqFAhszvG2Uu/fv3Ur1+/h7Zr2rSpmjZt+sA2rq6uGjlypEaOHGmv8gAAAPCYsOlmGW3bttWWLVvsXQsAAACQK2waCW7RooV++eUX9e7dW126dFG5cuXk7Oxs0a5atWrZLhAAAACwN5tCcLdu3Uz/3rNnj8V2e14dAgAAALA3m0Lw+PHj7V0HAAAAkGtsCsEdOnSwdx0AAABArrHpxDgAAADgUWbzbZPv3LmjTZs26dixY4qLi1NaWprZdoPBoHHjxmW7QAAAAMDebArBUVFR6tmzp6KiouTu7q64uDh5eHgoLi5OqampKlGihNzc3OxdKwAAAGAXNk2HmDBhguLj47Vy5Upt3LhRRqNRkyZN0sGDB/Xmm2+qUKFCWrBggb1rBQAAAOzCphC8b98+vfzyy6pZs6acnP6/C1dXV/Xp00f169dnKgQAAADyLZtC8O3bt023TS5atKgMBoPi4uJM2wMDA/Xrr7/ap0IAAADAzmwKwU8++aSio6MlSQUKFFDZsmV16NAh0/bTp0+rYMGCdikQAAAAsDebToyrX7++tm7dqiFDhki6e93guXPnKjY2VmlpaVq/fr3atWtn10IBAAAAe7EpBPfr109HjhxRUlKSXF1dNWDAAF25ckWbNm2Sk5OTwsPD9dZbb9m7VgAAAMAubArB5cuXV/ny5U3LBQsW1Mcff6yPP/7YboUBAAAAOSXLc4ITExMVFBSk+fPn50Q9AAAAQI7LcgguXLiwnJ2dVbhw4ZyoBwAAAMhxNl0donnz5tq0aZOMRqO96wEAAABynE1zglu3bq2xY8eqZ8+e6tSpkzw9PVWoUCGLdtWqVct2gQAAAIC9WR2C33rrLXXt2lW1atVSjx49TOsPHDhg0dZoNMpgMOj48eP2qRIAAACwI6tD8Nq1a9WgQQPVqlVL48aNk8FgyMm6AAAAgBxj03SIjh072rsOAAAAINfYdGIcAAAA8CjL0kjwgQMHlJqaanX79u3bZ7UeAAAAIMdlKQSvXLlSK1assKqtwWAgBAMAACBfylIIHjZsmJ577rmcqgUAAADIFVkKwRUqVFD16tVzqhYAAAAgV3BiHAAAABwOIRgAAAAOx+oQ3KFDB1WsWDEnawEAAAByhdVzgsePH5+TdQAAAAC5hukQAAAAcDiEYAAAADgcQjAAAAAcjlUhePHixYqMjMzpWgAAAIBcYVUIHj9+vI4ePWpaDggI0LfffptjRQEAAAA5yaoQ7O7uruvXr5uWjUZjjhUEAAAA5DSrLpEWFBSkadOm6fjx4ypWrJgk6ZtvvtHvv//+wMe988472a8QAAAAsDOrQvD777+vcePGaffu3bp+/boMBoN2796t3bt3Z/oYg8FACAYAAEC+ZFUILlWqlD799FPT8tNPP63//ve/atOmTY4VBgAAAOQUmy6RNn78eAUGBtq7FgAAACBXWH3b5Ht16NDB9O/Tp08rKipKkuTp6akqVarYpzIAAAAgh9gUgiVpy5Yt+uSTT0wBOF2FChU0atQoNW3aNNvFAQAAADnBphC8fft2DRs2TOXLl9eIESPk6+srSTpz5oxWrlypoUOHavbs2QoNDbVrsQAAAIA92BSCZ86cKX9/fy1dulRubm6m9U2bNlX37t3VrVs3zZgxgxAMAACAfMmmE+NOnDih9u3bmwXgdG5uburQoYNOnDiR7eIAAACAnGBTCC5YsKBiYmIy3R4TE6OCBQvaXBQAAACQk2wKwUFBQVq8eLEOHjxose3333/XkiVLFBwcnO3iAAAAgJxg05zgf/3rX+ratau6deummjVrytvbW5IUGRmpw4cPq1SpUnrzzTftWigAAABgLzaNBHt5eWn9+vXq0aOHYmJiFBERoYiICMXExKhnz55at26dKlSoYO9aAQAAALuw+TrBpUqV0ujRozV69Gh71gMAAADkOJtGggEAAIBHGSEYAAAADocQDAAAAIdDCAYAAIDDIQQDAADA4WQ5BCcmJqpjx45avnx5TtQDAAAA5Lgsh+DChQvr0qVLMhgMOVEPAAAAkONsmg7x3HPPadeuXfauBQAAAMgVNoXgQYMG6dy5c/rXv/6lAwcOKDo6Wjdv3rT4DwAAAMiPbLpjXOvWrSVJp0+f1nfffZdpu+PHj9tWFQAAAJCDbArBgwcPzpE5wd9//73Wr1+vP/74Q7GxsapUqZJ69OihF1980Wx/q1at0vz58/XXX3/J29tbI0aM0PPPP2/WV1xcnMaPH68tW7YoOTlZzz33nN555x2VKVPG7nUDAADg0WJTCB46dKi965AkLVq0SJ6enho1apRKlCihPXv26N1339Xly5c1ZMgQSdKGDRv07rvvasCAAapfv74iIiI0ZMgQLV26VLVr1zb1NXz4cJ0+fVpjxoxRwYIFNXnyZPXt21erV69WgQI2PW0AAAA8JuySBuPi4uTm5iZnZ+ds9TNr1iyVLFnStBwcHKybN29q4cKFGjRokJycnDR16lS1bt1aw4cPlyTVr19fJ0+e1IwZMzRv3jxJ0sGDB7Vr1y4tWLBADRs2lCR5e3srLCxMmzdvVlhYWLbqBAAAwKPN5ptlHDlyRL1791atWrUUFBSkn3/+WZJ048YNDRw4UPv3789yn/cG4HQBAQGKj49XQkKCLl68qHPnzqlVq1ZmbcLCwrR3714lJSVJknbs2CF3d3eFhISY2vj4+CggIEA7duzIcl0AAAB4vNgUgn/77Td169ZN58+fV9u2bZWWlmbaVrJkScXHx2vFihV2KfDXX39V2bJlVbRoUZ09e1bS3VHde/n6+io5OVkXL16UJJ09e1be3t4W85Z9fHxMfQAAAMBx2TQdYtKkSfL19dXKlSsVHx+vVatWmW0PCgrS2rVrs13cgQMHFBERoZEjR0qSYmJiJEnu7u5m7dKX07fHxsaqWLFiFv15eHjo6NGj2arJaDQqISEhW33YwmAwqHDhwrm+XwC5JzExUUajMa/LyDUc14DHX14c14xGo1UXcLApBB85ckT/+Mc/5OrqmuFOypYtq2vXrtnStcnly5c1YsQIBQUFqWfPntnqy56Sk5Pz5NJvhQsXVtWqVXN9vwByT2RkpBITE/O6jFzDcQ14/OXVcc3V1fWhbWwKwQUKFDCbAnG/6Ohoubm52dK1pLsjuX379lXx4sU1bdo0OTndnbXh4eEh6e6JeKVLlzZrf+92d3d3Xb582aLfmJgYUxtbubi4qEqVKtnqwxbcphp4/Hl7ezvcSDCAx1teHNdOnz5tVTubQnCtWrW0adMmvfrqqxbbEhIStGbNGtWtW9eWrnX79m31799fcXFxWrFihdm0Bh8fH0l35/ym/zt92cXFRV5eXqZ2e/futRgOj4yMlJ+fn011pTMYDNkK+ACQGaYGAHjc5MVxzdoP2DadGDds2DAdPXpU/fr1M11t4cSJE1q1apU6duyoGzduaNCgQVnuNyUlRcOHD9fZs2c1f/58lS1b1my7l5eXKleurI0bN5qtj4iIUHBwsGnoOzQ0VDExMdq7d6+pTWRkpI4dO6bQ0NAs1wUAAIDHi80jwXPnztWYMWNMJ6198sknkqSKFStq7ty5evrpp7Pc79ixY7Vt2zaNGjVK8fHxOnTokGlb1apV5erqqqFDh+rNN99UxYoVFRQUpIiICB0+fFhffvmlqW1gYKAaNmyo0aNHa+TIkSpYsKAmTZokf39/NW/e3JanDAAAgMeIzTfLCA4O1qZNm3Ts2DGdP39eRqNRXl5eql69us3zvHbv3i3p/wP1vbZu3aoKFSooPDxciYmJmjdvnubOnStvb29Nnz5dgYGBZu0nT56s8ePH67333lNKSooaNmyod955h7vFAQAAIPt3jKtatardzu798ccfrWrXqVMnderU6YFtihUrpnHjxmncuHH2KA0AAACPEZtDcFJSklauXKnt27crKipKkuTp6alGjRqpU6dOKliwoN2KBAAAAOzJphB8+fJlvfbaa4qMjFTp0qVVqVIlSdKff/6pnTt36ssvv9SiRYtUrlw5uxYLAAAA2INNIXjs2LH666+/NHnyZLVs2dJs2/fff69Ro0Zp7NixmjVrll2KBAAAAOzJphC8b98+vfrqqxYBWJJatWqlY8eOmV2tAQAAAMhPbLpOcJEiRVSyZMlMtz/xxBMqUqSIzUUBAAAAOcmmENyxY0etXbs2w3tB37p1S2vWrNGLL76Y7eIAAACAnGDVdIjNmzebLQcEBOinn35Sq1at1L59e9OJcefOndO6devk4eEhf39/+1cLAAAA2IFVIXjYsGEyGAwyGo2SZPbv2bNnW7S/fPmy/vnPfyosLMyOpQIAAAD2YVUIXrx4cU7XAQAAAOQaq0JwvXr1croOAAAAINfYdGIcAAAA8Ciz+bbJBw4c0OrVq3Xp0iXFxMSY5ginMxgMWr9+fbYLBAAAAOzNphC8cOFCTZgwQQULFpS3t7c8PDzsXRcAAACQY2wKwQsWLFCdOnU0e/ZsFStWzN41AQAAADnKpjnBiYmJatOmDQEYAAAAjySbQnBQUJBOnjxp71oAAACAXGFTCH733Xe1d+9eLViwQDdv3rRzSQAAAEDOsmlO8JNPPqkuXbpowoQJmjhxogoWLCgnJ/M8bTAY9Ouvv9qlSAAAAMCebArBU6ZM0ezZs1W2bFlVr16ducEAAAB4pNgUgr/66is1atRIM2fOtBgBBgAAAPI7mxJscnKyGjduTAAGAADAI8mmFNu4cWMdOHDA3rUAAAAAucKmEDxkyBCdOXNGY8aM0dGjR3Xjxg3dvHnT4j8AAAAgP7JpTnDLli0lScePH9eKFSsybXf8+HHbqgIAAABykE0hePDgwTIYDPauBQAAAMgVNoXgoUOH2rsOAAAAINdweQcAAAA4HJtGgqdPn/7QNgaDQYMHD7alewAAACBH2T0EGwwGGY1GQjAAAADyLZtC8J9//mmxLi0tTVFRUVq2bJl++eUXzZs3L9vFAQAAADnBbnOCnZyc5OXlpZEjR6pSpUr66KOP7NU1AAAAYFc5cmJc3bp1tX379pzoGgAAAMi2HAnBR48elZMTF54AAABA/mTTnOBvvvkmw/WxsbE6cOCANm/erE6dOmWnLgAAACDH2BSCR40alem2EiVKqF+/flwZAgAAAPmWTSF469atFusMBoPc3d1VtGjRbBcFAAAA5CSbQrCnp6e96wAAAAByDWevAQAAwOFYPRLcpk2bLHVsMBi0fv36LBcEAAAA5DSrQ3Dx4sWtanft2jVFRkbKYDDYWhMAAACQo6wOwUuWLHng9qtXr2revHlasWKFnJ2d1bZt22wXBwAAAOQEm06Mu9e1a9c0d+5crVy5UikpKWrTpo0GDhyoihUr2qM+AAAAwO5sDsHpI7/3ht9BgwbJy8vLnvUBAAAAdpflEHz16lXNnTtXq1atUkpKitq2bauBAwcSfgEAAPDIsDoEX7lyxRR+U1NT1a5dOw0YMIDwCwAAgEeO1SH4hRdeUFJSkgICAtS/f39VqFBBsbGx+uOPPzJ9TLVq1exSJAAAAGBPVofgO3fuSJKOHTum4cOHP7Ct0WiUwWDQ8ePHs1UcAAAAkBOsDsHjx4/PyToAAACAXGN1CO7QoUNO1gEAAADkGqe8LgAAAADIbYRgAAAAOBxCMAAAABwOIRgAAAAOhxAMAAAAh0MIBgAAgMMhBAMAAMDhEIIBAADgcAjBAAAAcDiEYAAAADgcQjAAAAAcDiEYAAAADocQDAAAAIeTr0Lw+fPn9d5776ldu3aqWrWqwsPDM2y3atUqtWjRQjVq1FDbtm21bds2izZxcXEaPXq06tWrp8DAQA0bNkxXrlzJ6acAAACAR0C+CsGnTp3S9u3bValSJfn6+mbYZsOGDXr33XfVqlUrzZs3T7Vr19aQIUN06NAhs3bDhw/X7t27NWbMGE2cOFGRkZHq27evUlJScuGZAAAAID8rkNcF3KtJkyZq1qyZJGnUqFE6evSoRZupU6eqdevWGj58uCSpfv36OnnypGbMmKF58+ZJkg4ePKhdu3ZpwYIFatiwoSTJ29tbYWFh2rx5s8LCwnLnCQEAACBfylcjwU5ODy7n4sWLOnfunFq1amW2PiwsTHv37lVSUpIkaceOHXJ3d1dISIipjY+PjwICArRjxw77Fw4AAIBHSr4KwQ9z9uxZSXdHde/l6+ur5ORkXbx40dTO29tbBoPBrJ2Pj4+pDwAAADiufDUd4mFiYmIkSe7u7mbr05fTt8fGxqpYsWIWj/fw8MhwikVWGI1GJSQkZKsPWxgMBhUuXDjX9wsg9yQmJspoNOZ1GbmG4xrw+MuL45rRaLQYCM3IIxWC84Pk5GQdP3481/dbuHBhVa1aNdf3CyD3REZGKjExMa/LyDUc14DHX14d11xdXR/a5pEKwR4eHpLuXv6sdOnSpvWxsbFm293d3XX58mWLx8fExJja2MrFxUVVqlTJVh+2sOYTDYBHm7e3t8ONBAN4vOXFce306dNWtXukQrCPj4+ku3N+0/+dvuzi4iIvLy9Tu71791oMh0dGRsrPzy9bNRgMBrm5uWWrDwDICFMDADxu8uK4Zu0H7EfqxDgvLy9VrlxZGzduNFsfERGh4OBg09B3aGioYmJitHfvXlObyMhIHTt2TKGhoblaMwAAAPKffDUSnJiYqO3bt0uSoqKiFB8fbwq89erVU8mSJTV06FC9+eabqlixooKCghQREaHDhw/ryy+/NPUTGBiohg0bavTo0Ro5cqQKFiyoSZMmyd/fX82bN8+T5wYAAID8I1+F4OvXr+uNN94wW5e+vHjxYgUFBSk8PFyJiYmaN2+e5s6dK29vb02fPl2BgYFmj5s8ebLGjx+v9957TykpKWrYsKHeeecdFSiQr54yAAAA8kC+SoQVKlTQiRMnHtquU6dO6tSp0wPbFCtWTOPGjdO4cePsVR4AAAAeE4/UnGAAAADAHgjBAAAAcDiEYAAAADgcQjAAAAAcDiEYAAAADocQDAAAAIdDCAYAAIDDIQQDAADA4RCCAQAA4HAIwQAAAHA4hGAAAAA4HEIwAAAAHA4hGAAAAA6HEAwAAACHQwgGAACAwyEEAwAAwOEQggEAAOBwCMEAAABwOIRgAAAAOBxCMAAAABwOIRgAAAAOhxAMAAAAh0MIBgAAgMMhBAMAAMDhEIIBAADgcAjBAAAAcDiEYAAAADgcQjAAAAAcDiEYAAAADocQDAAAAIdDCAYAAIDDIQQDAADA4RCCAQAA4HAIwQAAAHA4hGAAAAA4HEIwAAAAHA4hGAAAAA6HEAwAAACHQwgGAACAwyEEAwAAwOEQggEAAOBwCMEAAABwOIRgAAAAOBxCMAAAABwOIRgAAAAOhxAMAAAAh0MIBgAAgMMhBAMAAMDhEIIBAADgcAjBAAAAcDiEYAAAADgcQjAAAAAcDiEYAAAADocQDAAAAIdDCAYAAIDDIQQDAADA4RCCAQAA4HAIwQAAAHA4hGAAAAA4HEIwAAAAHA4hGAAAAA7nsQ7BZ86c0WuvvabatWsrJCREEyZMUFJSUl6XBQAAgDxWIK8LyCkxMTHq1auXKleurGnTpik6OlqffPKJbt++rffeey+vywMAAEAeemxD8FdffaVbt25p+vTpKl68uCQpNTVVY8eOVf/+/VW2bNm8LRAAAAB55rGdDrFjxw4FBwebArAktWrVSmlpadq9e3feFQYAAIA899iG4LNnz8rHx8dsnbu7u0qXLq2zZ8/mUVUAAADIDx7b6RCxsbFyd3e3WO/h4aGYmBib+kxOTpbRaNThw4ezW55NDAaDWtcrrdS0UnmyfwA5w9nJSUeOHJHRaMzrUnIdxzXg8ZSXx7Xk5GQZDIaHtntsQ3BOSH9BrXlhc4p70UJ5tm8AOSsvjy15ieMa8PjKi+OawWBw7BDs7u6uuLg4i/UxMTHy8PCwqc/AwMDslgUAAIB84LGdE+zj42Mx9zcuLk5Xr161mCsMAAAAx/LYhuDQ0FDt2bNHsbGxpnUbN26Uk5OTQkJC8rAyAAAA5DWD8TE9EyMmJkatW7eWt7e3+vfvb7pZRps2bbhZBgAAgIN7bEOwdPe2yR9++KEOHjyoIkWKqF27dhoxYoRcXV3zujQAAADkocc6BAMAAAAZeWznBAMAAACZIQQDAADA4RCCAQAA4HAIwQAAAHA4hGAAAAA4HEIwAAAAHA4hGAAAAA6HEAzkI2fOnNFrr72m2rVrKyQkRBMmTFBSUlJelwUANjt//rzee+89tWvXTlWrVlV4eHhelwRIkgrkdQEA7oqJiVGvXr1UuXJlTZs2zXSr79u3b3OrbwCPrFOnTmn79u2qVauW0tLSxD26kF8QgoF84quvvtKtW7c0ffp0FS9eXJKUmpqqsWPHqn///ipbtmzeFggANmjSpImaNWsmSRo1apSOHj2axxUBdzEdAsgnduzYoeDgYFMAlqRWrVopLS1Nu3fvzrvCACAbnJyIGsifeGcC+cTZs2fl4+Njts7d3V2lS5fW2bNn86gqAAAeT4RgIJ+IjY2Vu7u7xXoPDw/FxMTkQUUAADy+CMEAAABwOIRgIJ9wd3dXXFycxfqYmBh5eHjkQUUAADy+CMFAPuHj42Mx9zcuLk5Xr161mCsMAACyhxAM5BOhoaHas2ePYmNjTes2btwoJycnhYSE5GFlAAA8frhOMJBPdO3aVUuWLNHgwYPVv39/RUdHa8KECeratSvXCAbwyEpMTNT27dslSVFRUYqPj9fGjRslSfXq1VPJkiXzsjw4MIORW7cA+caZM2f04Ycf6uDBgypSpIjatWunESNGyNXVNa9LAwCbXLp0SU2bNs1w2+LFixUUFJTLFQF3EYIBAADgcJgTDAAAAIdDCAYAAIDDIQQDAADA4RCCAQAA4HAIwQAAAHA4hGAAAAA4HEIwAAAAHA4hGIBdNGnSRKNGjcrrMjI0b948tWzZUmlpaXldCh4iP7+P8rM1a9bI399fly5dMq3r3LmzJkyYkIdVAfkbIRjAA124cEHvvfeemjZtqho1aqhOnTrq2rWrvvjiC92+fTuvy3uo+Ph4zZ8/X3379pWTk/khb+vWrerQoYNq1Kihxo0ba+rUqUpJSbF5X9OmTZO/v78aNGigxMREi+1NmjRR//79be4/O9Jry+i/5cuX50lN9nb27FmNGzdOXbt2VY0aNSxCoa0ye91CQkLsUHXO6du3r5YtW6arV6/mdSlAvlQgrwsAkH/99NNPeuONN+Tq6qp27drJz89PycnJ+vXXX/Xf//5Xp0+f1ocffpjXZT7Q119/rZSUFIWHh5ut3759uwYPHqx69erp3Xff1cmTJzVr1ixdv35dY8eOzdY+r1+/ruXLl+v111/PVj85YcyYMXJzczNbV6tWrTyqxr4OHTqkJUuWqEqVKvL19dXx48ft1ndISIjatWtntq5QoUJ26z8nNG3aVEWLFtWyZcv0xhtv5HU5QL5DCAaQoYsXL2rEiBEqX768vvjiC5UpU8a07ZVXXtH58+f1008/5V2BVlqzZo2aNGmiggULmq2fMGGC/P399fnnn6tAgbuHwiJFimjOnDnq2bOnfH19bd5nQECAFixYoG7duuW7oNSiRQuVLFnSqrYJCQkWgTk/a9KkiX755RcVLVpUCxYssGsIrly5skUIzozRaNSdO3fy/Gfv5OSkFi1aaN26dRo2bJgMBkOe1gPkN0yHAJCh+fPnKyEhQR9//LFZAE5XqVIl9erVK9PH37x5U//5z3/Upk0bBQYGqk6dOurTp4/+/PNPi7ZLlixR69atVatWLdWtW1cdO3bUt99+a9oeHx+vjz/+WE2aNFH16tUVHBys1157TX/88ccDn8PFixd14sQJNWjQwGz96dOndfr0aXXu3NkUgCWpW7duMhqN2rRpk2ldcnKyzpw5oytXrjxwX/caPHiwrl27ZtU0g4SEBH3yySdq1KiRqlevrhYtWmjBggUyGo1m7fz9/fXBBx9oy5YtCg8PV/Xq1dW6dWvt2LHD6roeJH1O6c8//6wxY8YoODhYjRo1kiRFRUVpzJgxatGihWrWrKmgoCANGzbMYqpB+pSLzPq+t73RaNTMmTMVGhqqWrVqqUePHjp16lS2nkPx4sVVtGhRq9peuXJFZ86cUXJycrb2Kf3/NJedO3eqY8eOqlmzpr766itJ0urVq9WzZ08FBwerevXqCgsL07Jlyyz68Pf317Rp0zLs+/450qdOnVLPnj1Vs2ZNhYaGaubMmZnOd2/QoIGioqLs+oEAeFwwEgwgQ9u2bZOXl5fq1Klj0+MvXryoLVu2qGXLlqpQoYKuXbumFStWqHv37tqwYYPKli0rSVq5cqU++ugjtWjRQj179tSdO3d04sQJ/f7772rTpo0k6f3339emTZvUvXt3+fr66ubNm/r111915swZVatWLdMaDh48KEmqWrWq2fpjx45JkmrUqGG2vmzZsipXrpxZYIiOjlZYWJg6dOigTz75xKrn/swzz6h+/fqaP3++Xn755UxHBI1GowYOHKj9+/frpZdeUkBAgHbu3KkJEyYoOjpao0ePNmv/66+/avPmzerWrZuKFCmiJUuWaNiwYdq2bZtKlChhVW0xMTFmy87OzvLw8DAtjx07ViVLltTgwYOVkJAgSTpy5IgOHjyo1q1bq1y5coqKitLy5cvVs2dPbdiwQYULF7Zq3/eaMmWKZs2apUaNGqlRo0b6448/9Prrr9sllFrjs88+09q1a7V161ZVqFDhoe3v3LmjGzdumK0rWrSoXF1dJUmRkZH65z//qS5duqhz587y9vaWJC1fvlxPPfWUmjRpogIFCmjbtm0aO3asjEajXnnllSzXffXqVfXs2VOpqanq16+fChcurJUrV1p805GuevXqkqTffvvN4vcAcHSEYAAW4uPjFR0draZNm9rch7+/vzZt2mR2Mlq7du3UqlUrff311xo8eLCku/OOn3rqKU2dOjXTvrZv367OnTubjYj17dv3oTWcPXtWkixCTvqJQqVLl7Z4TOnSpbM06puZIUOGqHv37vrqq6/06quvZthm69at2rdvn4YPH66BAwdKujvVZNiwYVq8eLG6d++uihUrmtqfOXNGERERpnVBQUFq166dNmzYoO7du1tVV8uWLc2WPT099eOPP5qWPTw8tGjRIjk7O5vWNW7c2OJxzz//vLp06aJNmzapffv2Vu073Y0bNzR//nw1btxYs2fPNn1NP2nSJM2ePTtLfeWWr7/+Wl9//bXZuvHjx6tjx46SpPPnz2v+/Pl67rnnzNp8+eWXZh+Cunfvrt69e2vhwoU2heB58+bpxo0bWrVqlWrWrClJ6tChg5o3b55h+7Jly8rFxUWnT5/O8r6Axx0hGICF+Ph4SXfnyNoqfYRMklJTUxUbGys3Nzd5e3ubRmIlyd3dXZcvX9bhw4dNf9Tv5+7urt9//13R0dGmEWRr3Lx5UwUKFLB4HulXtbi3xnQFCxY0PX/pboA+ceKE1ftMV7duXQUFBWn+/Pnq2rVrhqPBO3bskLOzs3r06GG2/vXXX9emTZu0Y8cOs3DboEEDs1D89NNPq2jRorp48aLVdU2bNs1sysD9I4idO3c2C8CS+QlgycnJio+PV8WKFeXu7q5jx45lOQTv2bNHycnJ6t69u9k81V69euVaCP7kk0+sHtmX7p5kdv8HjSpVqpj+XaFCBYsALJm/dnFxcUpOTla9evW0a9cuxcXFqVixYlmqe/v27apdu7bZ70rJkiXVpk2bDKdZSHc/2Pz9999Z2g/gCAjBACykh6Rbt27Z3EdaWpoWL16sZcuW6dKlS0pNTTVtK168uOnfffv21Z49e9SpUydVqlRJISEhCg8P1zPPPGNq8+abb2rUqFFq3LixqlWrpkaNGql9+/by8vKyqbb0YJKUlGSxzZ4nNA0dOvSBo8FRUVEqU6aMxTzW9JPyoqKizNY/+eSTFn14eHgoNjZW0t3nc/90h5IlS5qF2mefffaBJ8ZlNDXg9u3bmjNnjtasWaPo6Giz+cpxcXGZ9pWZv/76S9Ldk83ur/XeqRn5Sbly5Szmlt8rsykVv/76q6ZNm6ZDhw5ZXDbPlhD8119/ZXg1j/TpFxkxGo2cFAdkgBAMwELRokVVpkyZbJ2oNHv2bE2ZMkUvvvii3njjDXl4eMjJyUnjxo0zC1G+vr7auHGjfvrpJ+3cuVObN2/WsmXLNHjwYA0bNkySFBYWpmeffVY//PCDdu/erQULFmjevHmaNm2a6eStjBQvXlwpKSmKj483C5rp0yCuXr1qESyvXr2a6Yh0VtWtW1f16tUzjQZn1/0jtOnSX8+DBw+qZ8+eZtusnfOaLqO5pR9++KHWrFmjXr16qXbt2ipWrJgMBoNGjBhh9rPMLGjd+wHocZXRB6cLFy7o1VdflY+Pj0aNGqUnn3xSLi4u2r59uxYtWmTVzVvs8drFxsZaPWcccCSEYAAZev7557VixQodPHhQgYGBWX78pk2bFBQUpHHjxpmtz+gPspubm8LCwhQWFqakpCQNHTpUs2fPVv/+/U2hrEyZMnrllVf0yiuv6Pr16+rQoYNmz579wBDs4+MjSbp06ZKefvpp0/qAgABJd0/4ujfwRkdH6/Lly+rcuXOWn29mhg4dqh49epiuFnAvT09P7d271yKkp89l9vT0zNK+nn76aS1cuNBsXUbznrMqfd7vvXOy79y5YzEK7O7uLunuzzj939L/j/ymK1++vCTp3LlzZqP5N27csBjJfpT9+OOPSkpK0qxZs0zPWZL2799v0fbeEf10SUlJFje6KF++vM6fP2/x+MjIyAxriI6OVnJycrYu+Qc8rrhEGoAM9enTR25ubnrnnXd07do1i+0XLlzQF198kenjnZ2dLS7z9f333ys6Otps3f1zFV1dXeXr6yuj0ajk5GSlpqZahK1SpUqpTJkyGU5nuFd6eD969KjZ+qeeeko+Pj5auXKl2Ujb8uXLZTAYzE4Cs+USafeqV6+eaTT4zp07ZttCQ0OVmpqqpUuXmq1ftGiRDAaDQkNDs7QvDw8PNWjQwOy/zK4akBUZjUAvWbLEYpQyfb7yL7/8YlqXkJCgb775xqxdgwYN5OLioi+//NLsPfKg95O92fMSaZlJf93unz6yevVqi7ZeXl46cOCA2br735+S1KhRIx06dEiHDx82rbtx44bZJQXvlf7et+WDLPC4YyQYQIYqVqyoiRMnasSIEQoLCzPdMS4pKUkHDx7Uxo0bTWfGZ6Rx48aaMWOG3nrrLQUGBurkyZP69ttvLebx9u7dW0888YTq1KmjUqVK6ezZs/ryyy/VqFEjFS1aVLGxsWrUqJFatGihp59+Wm5ubtqzZ4+OHDlicf3U+3l5ecnPz0979+7VSy+9ZLbt3//+twYOHKjXX39drVu31smTJ7V06VJ16tTJbNTMlkuk3W/IkCEW0xSku9eADQoK0qRJkxQVFSV/f3/t3r1bW7duVa9evcxOgstLjRs31rp161S0aFFVqVJFhw4d0p49e8zmdkt376pWvnx5vf322zp79qycnZ21evVqlShRwmw0uGTJknr99dc1Z84c9e/fX40aNdKxY8e0Y8eODL+2b9KkiSSZXcUiI3FxcVqyZImku5cEk6SlS5eqWLFicnd3NzuxLauXSLNFSEiIXFxcNGDAAHXt2lW3bt3SqlWrVKpUKYsR3k6dOun999/X0KFD1aBBA/3555/atWuXxevRp08frVu3Tn369FHPnj1Nl0grX758hidw7tmzR+XLl+fyaEAGCMEAMtW0aVOtX79eCxYs0NatW7V8+XK5urrK399fo0aNeuC0gQEDBigxMVHffvutIiIiVLVqVc2ZM0effvqpWbsuXbro22+/1cKFC5WQkKBy5cqpR48eGjRokKS7cy1ffvll7d69W5s3b5bRaFTFihX1/vvvq1u3bg99Di+++KKmTJmi27dvm83bfP755zV9+nRNnz5dH374oUqWLKn+/fubLt1mT0FBQapXr55+/vlns/VOTk6aNWuWpk6dqoiICK1Zs0aenp7697//na9uufz222/LyclJ3377re7cuaM6depo4cKF6tOnj1k7FxcXTZ8+XWPHjtWUKVNUunRp9erVS+7u7nrrrbfM2g4fPlyurq766quvtH//ftWsWVOff/65+vfvb7H/hIQEVapU6aF1xsTEaMqUKWbrPv/8c0l3p5ZYexk5e/Hx8dHUqVM1efJk/ec//9ETTzyhl19+WSVLlrS4BnTnzp116dIlff3119q5c6eeeeYZLVy40OKEyjJlymjx4sX66KOPNHfuXBUvXlxdu3ZVmTJl9Pbbb5u1TUtL06ZNm/TSSy9xYhyQAYPx/u8rAeAxEhcXp2bNmunNN99Up06d8rocZNHp06fVunVrzZkzR40bN87rch4pW7Zs0T//+U/98MMPGd71EXB0zAkG8FgrVqyYevfurQULFlh1Nj7yl/379yswMJAAbIN58+bplVdeIQADmWAkGAAAAA6HkWAAAAA4HEIwAAAAHA4hGAAAAA6HEAwAAACHQwgGAACAwyEEAwAAwOEQggEAAOBwCMEAAABwOIRgAAAAOBxCMAAAABwOIRgAAAAO5/8AVUn+LpUm9/0AAAAASUVORK5CYII=\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Compute the correlation matrix\n",
-        "corr = data.corr()\n",
-        "\n",
-        "# Set up the matplotlib figure\n",
-        "plt.figure(figsize=(12, 10))\n",
-        "\n",
-        "# Draw the heatmap\n",
-        "sns.heatmap(corr, annot=False, fmt=\".1f\", cmap='coolwarm', square=True, cbar_kws={\"shrink\": .8})\n",
-        "plt.title('Correlation Heatmap')\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 876
-        },
-        "id": "twpVUupdvUf2",
-        "outputId": "935b02a1-1b82-4809-d2f0-77c2d166d674"
-      },
-      "execution_count": 27,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1200x1000 with 2 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Plot the distribution of transaction amounts\n",
-        "plt.figure(figsize=(10, 5))\n",
-        "sns.histplot(data['Amount'], bins=50, kde=True)\n",
-        "plt.title('Distribution of Transaction Amounts')\n",
-        "plt.xlabel('Transaction Amount')\n",
-        "plt.ylabel('Frequency')\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 496
-        },
-        "id": "zEbh2B1fvUbX",
-        "outputId": "f92a8a2a-180e-448f-968e-e0ee620c3e77"
-      },
-      "execution_count": 28,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x500 with 1 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Calculate the proportion of fraudulent transactions\n",
-        "fraud_count = len(data[data['Class'] == 1])\n",
-        "non_fraud_count = len(data[data['Class'] == 0])\n",
-        "\n",
-        "# Create a pie chart\n",
-        "plt.figure(figsize=(8, 6))\n",
-        "labels = ['Non-Fraudulent', 'Fraudulent']\n",
-        "sizes = [non_fraud_count, fraud_count]\n",
-        "colors = ['#ff9999','#66b3ff']\n",
-        "explode = (0.1, 0)  # explode 1st slice\n",
-        "\n",
-        "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
-        "        autopct='%1.1f%%', shadow=True, startangle=140)\n",
-        "\n",
-        "plt.title('Proportion of Fraudulent Transactions')\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 521
-        },
-        "id": "MACWRE3DvUXw",
-        "outputId": "f2fa15b4-76a0-426a-a883-094e3d8fdd83"
-      },
-      "execution_count": 29,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 800x600 with 1 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Create a scatter plot to visualize the relationship between V1 and V2\n",
-        "plt.figure(figsize=(10, 8))\n",
-        "sns.scatterplot(x='V1', y='V2', hue='Class', data=data)\n",
-        "plt.title('Scatter Plot: V1 vs V2')\n",
-        "plt.xlabel('V1')\n",
-        "plt.ylabel('V2')\n",
-        "plt.legend(title='Class')\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 727
-        },
-        "id": "a5e74QkFvUU5",
-        "outputId": "f6763c4f-508c-4673-feac-4905d440dac4"
-      },
-      "execution_count": 30,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x800 with 1 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Create a line chart to visualize the average transaction amount over time\n",
-        "data['Time'] = pd.to_datetime(data['Time'], unit='s')\n",
-        "data.set_index('Time', inplace=True)\n",
-        "\n",
-        "avg_amount = data['Amount'].resample('D').mean()\n",
-        "\n",
-        "plt.figure(figsize=(12, 6))\n",
-        "avg_amount.plot()\n",
-        "plt.title('Average Transaction Amount over Time')\n",
-        "plt.xlabel('Time')\n",
-        "plt.ylabel('Average Transaction Amount')\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 607
-        },
-        "id": "oVU_jmVEvw86",
-        "outputId": "09cebd5b-590a-45ee-a6e9-8c440c7fdc7e"
-      },
-      "execution_count": 31,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1200x600 with 1 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from sklearn.inspection import permutation_importance\n",
-        "\n",
-        "# Perform permutation importance\n",
-        "perm_importance = permutation_importance(model, x_test, y_test, scoring='accuracy')\n",
-        "\n",
-        "# Create a bar plot of the results\n",
-        "importance_df = pd.DataFrame({\n",
-        "    'Feature': x_test.columns,\n",
-        "    'Importance': perm_importance.importances_mean\n",
-        "}).sort_values(by='Importance', ascending=False)\n",
-        "\n",
-        "plt.figure(figsize=(10, 6))\n",
-        "plt.barh(importance_df['Feature'], importance_df['Importance'])\n",
-        "plt.xlabel('Permutation Importance')\n",
-        "plt.ylabel('Features')\n",
-        "plt.title('Permutation Importance of Features')\n",
-        "plt.gca().invert_yaxis()\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "id": "KmOAkOlaJP-7",
-        "outputId": "d85408a4-65a8-4412-fa73-1b252b2c9407",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 573
-        }
-      },
-      "execution_count": 33,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x600 with 1 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "### **Thank You :)**"
-      ],
-      "metadata": {
-        "id": "l8NYTWmhuGFK"
-      }
-    }
-  ]
-}
\ No newline at end of file