Fraud_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8ba7dfeb",
   "metadata": {},
   "source": [
    "![Fraud Detection](shutterstock_1874193907.jpg)\n",
    "\n",
    "# Bank Fraud Detection with Imbalanced Data: Applying Oversampling and Hyperparameter Optimization\n",
    "\n",
    "In this project, the critical problem of detecting fraudulent bank transactions is addressed. This problem represents a significant challenge for the financial industry. The aim is to compare the efficacy of various machine learning algorithms in accurately identifying fraudulent transactions.\n",
    "\n",
    "The data used come from the Paysim dataset, available on the [Kaggle platform](https://www.kaggle.com/datasets/ealaxi/paysim1). This dataset is a realistic simulation of bank transactions and is commonly used to train and evaluate fraud detection models.\n",
    "\n",
    "The employed methodology begins with exploring the data to understand their characteristics and distributions. Next, the data are preprocessed to ensure they are suitable for model training. Due to the imbalance in the distribution of legitimate and fraudulent transactions, the oversampling technique is applied to prevent the models from being biased toward the majority class.\n",
    "\n",
    "Various models are trained using these preprocessed data, and the random forest model is optimized through a random hyperparameter search. Model performance is evaluated using metrics such as accuracy, recall, F1-score, and AUC-ROC, with the performance of each model being tracked and logged using MLflow."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a76ea82b",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Details of the Dataset Columns\n",
    "\n",
    "| Column Name      | Description                                                                                     |\n",
    "|:------------------:|:-------------------------------------------------------------------------------------------------:|\n",
    "| step             | Unit of time in the real world (1 hour). Total steps: 744 (30 days simulation)                                                         |\n",
    "| type             | CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER                                                  |\n",
    "| amount           | Amount of the transaction in local currency                                                     |\n",
    "| nameOrig         | Customer who started the transaction                                                            |\n",
    "| oldbalanceOrg    | Initial balance before the transaction                                                          |\n",
    "| newbalanceOrig   | New balance after the transaction                                                               |\n",
    "| nameDest         | Recipient of the transaction                                                                    |\n",
    "| oldbalanceDest   | Initial recipient balance before the transaction                                                |\n",
    "| newbalanceDest   | New recipient balance after the transaction                                                     |\n",
    "| isFraud          | This is the transactions made by the fraudulent agents inside the simulation. In this specific dataset the fraudulent behavior of the agents aims to profit by taking control or customers accounts and try to empty the funds by transferring to another account and then cashing out of the system |\n",
    "| isFlaggedFraud   | The business model aims to control massive transfers from one account to another and flags illegal attempts. An illegal attempt in this dataset is an attempt to transfer more than 200.000 in a single transaction                  |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "061d7d4d",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Importing Libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "20ec05f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import mlflow\n",
    "import mlflow.sklearn\n",
    "import os\n",
    "\n",
    "from sklearn.base import clone\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, make_scorer, accuracy_score,recall_score,precision_score, roc_curve\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split, ParameterSampler, cross_validate, RandomizedSearchCV, KFold\n",
    "from datetime import datetime\n",
    "os.environ['GIT_PYTHON_REFRESH'] = 'quiet'\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7707a4c",
   "metadata": {},
   "source": [
    "***\n",
    "## Loading and Initial Exploration of the Dataset\n",
    "\n",
    "This step involves reading the dataset using pandas, followed by an initial evaluation of the data. </br>First, the dataset's format is checked using the shape function, then information about the columns and data types is obtained using the info() function. Next, the presence of null values is checked using the isnull() and sum() functions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2eea0a95",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>C1231006815</td>\n",
       "      <td>170136.00</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>M1979787155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>C1666544295</td>\n",
       "      <td>21249.00</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>M2044282225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C1305486145</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C553264065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C840083671</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C38997010</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>C2048537720</td>\n",
       "      <td>41554.00</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>M1230701703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7817.71</td>\n",
       "      <td>C90045638</td>\n",
       "      <td>53860.00</td>\n",
       "      <td>46042.29</td>\n",
       "      <td>M573487274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7107.77</td>\n",
       "      <td>C154988899</td>\n",
       "      <td>183195.00</td>\n",
       "      <td>176087.23</td>\n",
       "      <td>M408069119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7861.64</td>\n",
       "      <td>C1912850431</td>\n",
       "      <td>176087.23</td>\n",
       "      <td>168225.59</td>\n",
       "      <td>M633326333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>4024.36</td>\n",
       "      <td>C1265012928</td>\n",
       "      <td>2671.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>M1176932104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>5337.77</td>\n",
       "      <td>C712410124</td>\n",
       "      <td>41720.00</td>\n",
       "      <td>36382.23</td>\n",
       "      <td>C195600860</td>\n",
       "      <td>41898.0</td>\n",
       "      <td>40348.79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
       "0     1   PAYMENT   9839.64  C1231006815      170136.00       160296.36   \n",
       "1     1   PAYMENT   1864.28  C1666544295       21249.00        19384.72   \n",
       "2     1  TRANSFER    181.00  C1305486145         181.00            0.00   \n",
       "3     1  CASH_OUT    181.00   C840083671         181.00            0.00   \n",
       "4     1   PAYMENT  11668.14  C2048537720       41554.00        29885.86   \n",
       "5     1   PAYMENT   7817.71    C90045638       53860.00        46042.29   \n",
       "6     1   PAYMENT   7107.77   C154988899      183195.00       176087.23   \n",
       "7     1   PAYMENT   7861.64  C1912850431      176087.23       168225.59   \n",
       "8     1   PAYMENT   4024.36  C1265012928        2671.00            0.00   \n",
       "9     1     DEBIT   5337.77   C712410124       41720.00        36382.23   \n",
       "\n",
       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
       "0  M1979787155             0.0            0.00        0               0  \n",
       "1  M2044282225             0.0            0.00        0               0  \n",
       "2   C553264065             0.0            0.00        1               0  \n",
       "3    C38997010         21182.0            0.00        1               0  \n",
       "4  M1230701703             0.0            0.00        0               0  \n",
       "5   M573487274             0.0            0.00        0               0  \n",
       "6   M408069119             0.0            0.00        0               0  \n",
       "7   M633326333             0.0            0.00        0               0  \n",
       "8  M1176932104             0.0            0.00        0               0  \n",
       "9   C195600860         41898.0        40348.79        0               0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"PS_20174392719_1491204439457_log.csv\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "479ffed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Shape:\n",
      "6362620 rows\n",
      "11 columns\n"
     ]
    }
   ],
   "source": [
    "print('Dataset Shape:\\n{} rows\\n{} columns'.format(df.shape[0],df.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27634114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6362620 entries, 0 to 6362619\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Dtype  \n",
      "---  ------          -----  \n",
      " 0   step            int64  \n",
      " 1   type            object \n",
      " 2   amount          float64\n",
      " 3   nameOrig        object \n",
      " 4   oldbalanceOrg   float64\n",
      " 5   newbalanceOrig  float64\n",
      " 6   nameDest        object \n",
      " 7   oldbalanceDest  float64\n",
      " 8   newbalanceDest  float64\n",
      " 9   isFraud         int64  \n",
      " 10  isFlaggedFraud  int64  \n",
      "dtypes: float64(5), int64(3), object(3)\n",
      "memory usage: 534.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "34088b99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "step              0\n",
       "type              0\n",
       "amount            0\n",
       "nameOrig          0\n",
       "oldbalanceOrg     0\n",
       "newbalanceOrig    0\n",
       "nameDest          0\n",
       "oldbalanceDest    0\n",
       "newbalanceDest    0\n",
       "isFraud           0\n",
       "isFlaggedFraud    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6873e04a",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Exploratory Data Analysis\n",
    "### Overview of Dataset Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "eb83c5c4",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>step</th>\n",
       "      <td>6362620.00</td>\n",
       "      <td>243.40</td>\n",
       "      <td>142.33</td>\n",
       "      <td>1.00</td>\n",
       "      <td>156.00</td>\n",
       "      <td>239.00</td>\n",
       "      <td>335.00</td>\n",
       "      <td>743.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>6362620.00</td>\n",
       "      <td>179861.90</td>\n",
       "      <td>603858.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13389.57</td>\n",
       "      <td>74871.94</td>\n",
       "      <td>208721.48</td>\n",
       "      <td>92445516.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <td>6362620.00</td>\n",
       "      <td>833883.10</td>\n",
       "      <td>2888242.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14208.00</td>\n",
       "      <td>107315.18</td>\n",
       "      <td>59585040.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <td>6362620.00</td>\n",
       "      <td>855113.67</td>\n",
       "      <td>2924048.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>144258.41</td>\n",
       "      <td>49585040.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <td>6362620.00</td>\n",
       "      <td>1100701.67</td>\n",
       "      <td>3399180.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>132705.66</td>\n",
       "      <td>943036.71</td>\n",
       "      <td>356015889.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newbalanceDest</th>\n",
       "      <td>6362620.00</td>\n",
       "      <td>1224996.40</td>\n",
       "      <td>3674128.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>214661.44</td>\n",
       "      <td>1111909.25</td>\n",
       "      <td>356179278.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isFraud</th>\n",
       "      <td>6362620.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count       mean        std  min      25%       50%  \\\n",
       "step           6362620.00     243.40     142.33 1.00   156.00    239.00   \n",
       "amount         6362620.00  179861.90  603858.23 0.00 13389.57  74871.94   \n",
       "oldbalanceOrg  6362620.00  833883.10 2888242.67 0.00     0.00  14208.00   \n",
       "newbalanceOrig 6362620.00  855113.67 2924048.50 0.00     0.00      0.00   \n",
       "oldbalanceDest 6362620.00 1100701.67 3399180.11 0.00     0.00 132705.66   \n",
       "newbalanceDest 6362620.00 1224996.40 3674128.94 0.00     0.00 214661.44   \n",
       "isFraud        6362620.00       0.00       0.04 0.00     0.00      0.00   \n",
       "\n",
       "                      75%          max  \n",
       "step               335.00       743.00  \n",
       "amount          208721.48  92445516.64  \n",
       "oldbalanceOrg   107315.18  59585040.37  \n",
       "newbalanceOrig  144258.41  49585040.37  \n",
       "oldbalanceDest  943036.71 356015889.35  \n",
       "newbalanceDest 1111909.25 356179278.92  \n",
       "isFraud              0.00         1.00  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef169eb5",
   "metadata": {},
   "source": [
    "### Percentage of Occurrences by Type of Bank Operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c62388e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAWCNzeT3YSnl5CR311r/TynlzCRfrLV+th/2+STza607r8PFZQ0JbQAAAAAa5PYoAAAAgAZpiBgAeMgppZyY/mkbQz5Ta333TJQHAGaC78ONn9ujAAAAABrk9igAAACABgltAAAAABoktAEAAABokNAGAAAAoEFCGwAAAIAG/f+hnk/SLbF3AQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "plt.title('Percentage of Occurrences by Type of Bank Operation',fontsize=25, y=1.05)\n",
    "colors = [\"#225588\", \"#6DA0D0\", \"#215284\", \"#C0D4E6\", \"#3D7DB1\"]\n",
    "ax = sns.countplot(x=df.type, palette = colors)\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate('{:.1f}%'.format(100 * p.get_height()/len(df.type)), (p.get_x() + p.get_width() / 2, p.get_height()), ha='center', va='bottom')\n",
    "ax.set(yticklabels=[])\n",
    "\n",
    "ax.set_xlabel('');\n",
    "ax.set_ylabel('');\n",
    "ax.yaxis.set_ticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d30e7ee",
   "metadata": {},
   "source": [
    "This bar chart presents the percentage of occurrences by type of banking operation in the dataset. It can be observed that the \"Cash_out\" operation is the most frequent, accounting for 35.2% of all transactions. Following this, the \"Payment\" operation represents 33.8% of the transactions. \"Cash_in\" operations make up 22% of the transactions, while \"Transfer\" operations constitute 8.4%. The \"Debit\" operations are the least frequent, constituting just 0.7% of all transactions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de74b968",
   "metadata": {},
   "source": [
    "### Percentage of Fraud Occurrences by Type of Bank Operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f493d173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frauds = df[df['isFraud'] == 1]\n",
    "plt.figure(figsize = (20,10))\n",
    "plt.title('Percentage of Fraud Occurrences by Type of Bank Operation',fontsize=25, y=1.05)\n",
    "colors = [\"#215284\", \"#215284\"]\n",
    "ax = sns.countplot(x=frauds.type, palette = colors)\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate('{:.1f}%'.format(100 * p.get_height()/len(frauds.type)), (p.get_x() + p.get_width() / 2, p.get_height()), ha='center', va='bottom')\n",
    "    \n",
    "ax.set(yticklabels=[])\n",
    "ax.set_xlabel('');\n",
    "ax.set_ylabel('');\n",
    "ax.yaxis.set_ticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f32296",
   "metadata": {},
   "source": [
    "This chart displays the distribution of fraud occurrences by type of banking operation in the dataset. Remarkably, the \"Transfer\" and \"Cash_out\" operations encompass all fraudulent transactions, with \"Transfer\" accounting for 49.9% and \"Cash_out\" constituting 50.1%. It is notable that the \"Payment\", \"Debit\", and \"Cash_in\" operations do not have any recorded fraud occurrences, suggesting that fraudulent activity in this dataset is concentrated in the \"Transfer\" and \"Cash_out\" operations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "097edc7e",
   "metadata": {},
   "source": [
    "### Fraud Occurrences among All Transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "411872fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frauds = df[df['isFraud'] == 1]\n",
    "plt.figure(figsize = (20,10))\n",
    "plt.title('Fraud Occurrences among All Transactions',fontsize=25, y=1.02)\n",
    "colors = [\"#215284\"]\n",
    "ax = sns.countplot(x=df.isFraud, palette = colors)\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate('{} occurrences ({:.2f}%)'.format(p.get_height(),100 * p.get_height()/df.shape[0]), (p.get_x() + p.get_width() / 2, p.get_height()), ha='center', va='bottom')\n",
    "    \n",
    "ax.set(yticklabels=[])\n",
    "ax.set_xlabel('');\n",
    "ax.set_ylabel('');\n",
    "ax.xaxis.set_ticklabels(['Non-fraudulent transactions', 'Fraudulent transactions'])\n",
    "ax.yaxis.set_ticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "092cf3fb",
   "metadata": {},
   "source": [
    "This chart illustrates the distribution of fraudulent and non-fraudulent transactions within the entire dataset. Non-fraudulent transactions overwhelmingly dominate the data, comprising 6,354,407 instances or 99.87% of all transactions. Meanwhile, fraudulent transactions are considerably rarer, accounting for just 8,213 instances or 0.13% of the total.\n",
    "\n",
    "While at first glance this may seem reassuring, it underscores a critical challenge in utilizing this dataset for machine learning models. The substantial class imbalance between fraudulent and non-fraudulent transactions necessitates careful preprocessing and selection of appropriate modelling techniques to ensure that the rare but significant class of fraudulent transactions is accurately detected and predicted."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06900ea4",
   "metadata": {},
   "source": [
    "### Fraud Occurrences by Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dc4bbc58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,7.5))\n",
    "df2 = df.copy()\n",
    "df2['day'] = (df2['step'] + 23) // 24\n",
    "fpd = df2.groupby('day').sum()['isFraud']\n",
    "plt.title('Fraud Occurrences by Day',fontsize=25, y=1.02)\n",
    "plt.plot(fpd.index, fpd.values, marker='o', linestyle='-')\n",
    "plt.axhline(y=fpd.values.mean(), color='r', linestyle='--', label='Mean')\n",
    "plt.legend(loc=\"lower right\", prop={'size': 20})\n",
    "label='Mean'\n",
    "plt.xticks(range(1,32));\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b73104",
   "metadata": {},
   "source": [
    "This chart presents the number of frauds occurring each day over a period of a month. The quantity of frauds varies from day to day, but does not show an evident pattern or clear trend. The daily number of frauds fluctuates between 216 and 320 occurrences.\n",
    "\n",
    "The day with the highest number of recorded frauds is the 17th, with a total of 320 occurrences, while the day with the fewest frauds is the 23rd, with 216 occurrences.\n",
    "\n",
    "The red dashed line in the chart represents the average daily fraud occurrences, calculated over the whole month. This line provides a reference point against which to compare the number of frauds on any given day, making it easier to identify days with unusually high or low numbers of frauds."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d76f3a1f",
   "metadata": {},
   "source": [
    "### Fraud Occurrences by hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e9f5c96",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,7.5))\n",
    "df2['hour'] = df2['step'] % 24\n",
    "fph = df2.groupby('hour').sum()['isFraud']\n",
    "plt.title('Fraud Occurrences by hour',fontsize=25, y=1.02)\n",
    "plt.plot(fph.index, fph.values, marker='o', linestyle='-')\n",
    "plt.axhline(y=fph.values.mean(), color='r', linestyle='--', label='Mean')\n",
    "plt.legend(loc=\"lower right\", prop={'size': 20})\n",
    "plt.xticks(range(0,24));\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ab516fb",
   "metadata": {},
   "source": [
    "This chart presents the total number of frauds occurring each hour throughout the entire data collection period. The number of frauds varies from hour to hour, but it does not show an obvious pattern or clear trend. The number of frauds per hour fluctuates between 274 and 375 occurrences.\n",
    "\n",
    "The hour with the highest number of recorded frauds is 10 a.m., with a total of 375 occurrences, while the lowest number of frauds is recorded at 4 a.m., with 274 occurrences.\n",
    "\n",
    "The red horizontal line represents the average number of fraud occurrences per hour. This line serves as a benchmark, allowing us to better comprehend the variations in the data, above or below the mean."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "760000e1",
   "metadata": {},
   "source": [
    "### Fraud Distribution by Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f2a97032",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>isFraud</th>\n",
       "      <td>11.053836</td>\n",
       "      <td>4.998631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mean       std  min  25%   50%   75%   max\n",
       "isFraud  11.053836  4.998631  0.0  8.0  10.0  14.0  40.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,7.5))\n",
    "plt.title('Fraud Distribution by Day',fontsize=25, y=1.02)\n",
    "fps = df2.groupby('step').sum()['isFraud']\n",
    "sns.boxplot(x=fps);\n",
    "plt.xlabel('')\n",
    "plt.xlabel('Number of Frauds');\n",
    "\n",
    "f = pd.DataFrame(fps.describe()).T\n",
    "f.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f252d690",
   "metadata": {},
   "source": [
    "This chart is a boxplot that represents the distribution of frauds per day. The central box of the chart represents the interquartile range (IQR), meaning, 50% of the data lies within this box. The line inside the box indicates the median of the data.\n",
    "\n",
    "The \"tails\" extending out from the box show the variability outside the IQR, while the dots represent possible outliers or anomalies in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d1d969d",
   "metadata": {},
   "source": [
    "### Descriptive Statistics by Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "f5025191",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</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>CASH_IN</th>\n",
       "      <td>1399284.00</td>\n",
       "      <td>168920.24</td>\n",
       "      <td>126508.26</td>\n",
       "      <td>0.04</td>\n",
       "      <td>70510.18</td>\n",
       "      <td>143427.71</td>\n",
       "      <td>239899.09</td>\n",
       "      <td>1915267.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CASH_OUT</th>\n",
       "      <td>2237500.00</td>\n",
       "      <td>176273.96</td>\n",
       "      <td>175329.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>72669.65</td>\n",
       "      <td>147072.18</td>\n",
       "      <td>246539.48</td>\n",
       "      <td>10000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEBIT</th>\n",
       "      <td>41432.00</td>\n",
       "      <td>5483.67</td>\n",
       "      <td>13318.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1500.18</td>\n",
       "      <td>3048.99</td>\n",
       "      <td>5479.18</td>\n",
       "      <td>569077.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYMENT</th>\n",
       "      <td>2151495.00</td>\n",
       "      <td>13057.60</td>\n",
       "      <td>12556.45</td>\n",
       "      <td>0.02</td>\n",
       "      <td>4383.82</td>\n",
       "      <td>9482.19</td>\n",
       "      <td>17561.22</td>\n",
       "      <td>238637.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRANSFER</th>\n",
       "      <td>532909.00</td>\n",
       "      <td>910647.01</td>\n",
       "      <td>1879573.53</td>\n",
       "      <td>2.60</td>\n",
       "      <td>215905.35</td>\n",
       "      <td>486308.39</td>\n",
       "      <td>974958.00</td>\n",
       "      <td>92445516.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             amount                                                          \\\n",
       "              count      mean        std  min       25%       50%       75%   \n",
       "type                                                                          \n",
       "CASH_IN  1399284.00 168920.24  126508.26 0.04  70510.18 143427.71 239899.09   \n",
       "CASH_OUT 2237500.00 176273.96  175329.74 0.00  72669.65 147072.18 246539.48   \n",
       "DEBIT      41432.00   5483.67   13318.54 0.55   1500.18   3048.99   5479.18   \n",
       "PAYMENT  2151495.00  13057.60   12556.45 0.02   4383.82   9482.19  17561.22   \n",
       "TRANSFER  532909.00 910647.01 1879573.53 2.60 215905.35 486308.39 974958.00   \n",
       "\n",
       "                      \n",
       "                 max  \n",
       "type                  \n",
       "CASH_IN   1915267.90  \n",
       "CASH_OUT 10000000.00  \n",
       "DEBIT      569077.51  \n",
       "PAYMENT    238637.98  \n",
       "TRANSFER 92445516.64  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = df2[['type','amount']].groupby('type')\n",
    "gb.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d8c7c55",
   "metadata": {},
   "source": [
    "This table provides detailed descriptive statistics for different types of banking operations: \"CASH_IN\", \"CASH_OUT\", \"DEBIT\", \"PAYMENT\", and \"TRANSFER\".\n",
    "\n",
    "Each type of operation is analyzed in terms of the total transaction value, the mean value, the standard deviation (indicating the dispersion of the values), as well as the minimum and maximum values. Additionally, the quartiles (25%, 50%, and 75%) are presented, providing a view of the distribution of transaction values.\n",
    "\n",
    "For instance, it can be observed that \"TRANSFER\" operations have the highest mean transaction value, indicating that this type of operation generally involves large amounts of money. On the other hand, \"DEBIT\" operations have the lowest mean value, suggesting that these operations involve small amounts of money.\n",
    "\n",
    "Furthermore, the presence of high values for the standard deviation and the difference between the minimum and maximum in some banking operations, such as \"CASH_OUT\" and \"TRANSFER\", suggest a large variability in the amount of transactions for these types of operations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96a6de5a",
   "metadata": {},
   "source": [
    "### Checking occurrences of 'Flagged Frauds'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "29535308",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isFlaggedFraud</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>6362604</td>\n",
       "      <td>6362604</td>\n",
       "      <td>6362604</td>\n",
       "      <td>6362604</td>\n",
       "      <td>6362604</td>\n",
       "      <td>6362604</td>\n",
       "      <td>6362604</td>\n",
       "      <td>6362604</td>\n",
       "      <td>6362604</td>\n",
       "      <td>6362604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   step     type   amount  nameOrig  oldbalanceOrg  \\\n",
       "isFlaggedFraud                                                       \n",
       "0               6362604  6362604  6362604   6362604        6362604   \n",
       "1                    16       16       16        16             16   \n",
       "\n",
       "                newbalanceOrig  nameDest  oldbalanceDest  newbalanceDest  \\\n",
       "isFlaggedFraud                                                             \n",
       "0                      6362604   6362604         6362604         6362604   \n",
       "1                           16        16              16              16   \n",
       "\n",
       "                isFraud  \n",
       "isFlaggedFraud           \n",
       "0               6362604  \n",
       "1                    16  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('isFlaggedFraud').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4234f447",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2736446</th>\n",
       "      <td>212</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>4953893.08</td>\n",
       "      <td>C728984460</td>\n",
       "      <td>4953893.08</td>\n",
       "      <td>4953893.08</td>\n",
       "      <td>C639921569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>3247297</th>\n",
       "      <td>250</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>1343002.08</td>\n",
       "      <td>C1100582606</td>\n",
       "      <td>1343002.08</td>\n",
       "      <td>1343002.08</td>\n",
       "      <td>C1147517658</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3760288</th>\n",
       "      <td>279</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>536624.41</td>\n",
       "      <td>C1035541766</td>\n",
       "      <td>536624.41</td>\n",
       "      <td>536624.41</td>\n",
       "      <td>C1100697970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5563713</th>\n",
       "      <td>387</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>4892193.09</td>\n",
       "      <td>C908544136</td>\n",
       "      <td>4892193.09</td>\n",
       "      <td>4892193.09</td>\n",
       "      <td>C891140444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996407</th>\n",
       "      <td>425</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>10000000.00</td>\n",
       "      <td>C689608084</td>\n",
       "      <td>19585040.37</td>\n",
       "      <td>19585040.37</td>\n",
       "      <td>C1392803603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996409</th>\n",
       "      <td>425</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>9585040.37</td>\n",
       "      <td>C452586515</td>\n",
       "      <td>19585040.37</td>\n",
       "      <td>19585040.37</td>\n",
       "      <td>C1109166882</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6168499</th>\n",
       "      <td>554</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>3576297.10</td>\n",
       "      <td>C193696150</td>\n",
       "      <td>3576297.10</td>\n",
       "      <td>3576297.10</td>\n",
       "      <td>C484597480</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6205439</th>\n",
       "      <td>586</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>353874.22</td>\n",
       "      <td>C1684585475</td>\n",
       "      <td>353874.22</td>\n",
       "      <td>353874.22</td>\n",
       "      <td>C1770418982</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6266413</th>\n",
       "      <td>617</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>2542664.27</td>\n",
       "      <td>C786455622</td>\n",
       "      <td>2542664.27</td>\n",
       "      <td>2542664.27</td>\n",
       "      <td>C661958277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6281482</th>\n",
       "      <td>646</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>10000000.00</td>\n",
       "      <td>C19004745</td>\n",
       "      <td>10399045.08</td>\n",
       "      <td>10399045.08</td>\n",
       "      <td>C1806199534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6281484</th>\n",
       "      <td>646</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>399045.08</td>\n",
       "      <td>C724693370</td>\n",
       "      <td>10399045.08</td>\n",
       "      <td>10399045.08</td>\n",
       "      <td>C1909486199</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6296014</th>\n",
       "      <td>671</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>3441041.46</td>\n",
       "      <td>C917414431</td>\n",
       "      <td>3441041.46</td>\n",
       "      <td>3441041.46</td>\n",
       "      <td>C1082139865</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6351225</th>\n",
       "      <td>702</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>3171085.59</td>\n",
       "      <td>C1892216157</td>\n",
       "      <td>3171085.59</td>\n",
       "      <td>3171085.59</td>\n",
       "      <td>C1308068787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362460</th>\n",
       "      <td>730</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>10000000.00</td>\n",
       "      <td>C2140038573</td>\n",
       "      <td>17316255.05</td>\n",
       "      <td>17316255.05</td>\n",
       "      <td>C1395467927</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362462</th>\n",
       "      <td>730</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>7316255.05</td>\n",
       "      <td>C1869569059</td>\n",
       "      <td>17316255.05</td>\n",
       "      <td>17316255.05</td>\n",
       "      <td>C1861208726</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362584</th>\n",
       "      <td>741</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5674547.89</td>\n",
       "      <td>C992223106</td>\n",
       "      <td>5674547.89</td>\n",
       "      <td>5674547.89</td>\n",
       "      <td>C1366804249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         step      type       amount     nameOrig  oldbalanceOrg  \\\n",
       "2736446   212  TRANSFER   4953893.08   C728984460     4953893.08   \n",
       "3247297   250  TRANSFER   1343002.08  C1100582606     1343002.08   \n",
       "3760288   279  TRANSFER    536624.41  C1035541766      536624.41   \n",
       "5563713   387  TRANSFER   4892193.09   C908544136     4892193.09   \n",
       "5996407   425  TRANSFER  10000000.00   C689608084    19585040.37   \n",
       "5996409   425  TRANSFER   9585040.37   C452586515    19585040.37   \n",
       "6168499   554  TRANSFER   3576297.10   C193696150     3576297.10   \n",
       "6205439   586  TRANSFER    353874.22  C1684585475      353874.22   \n",
       "6266413   617  TRANSFER   2542664.27   C786455622     2542664.27   \n",
       "6281482   646  TRANSFER  10000000.00    C19004745    10399045.08   \n",
       "6281484   646  TRANSFER    399045.08   C724693370    10399045.08   \n",
       "6296014   671  TRANSFER   3441041.46   C917414431     3441041.46   \n",
       "6351225   702  TRANSFER   3171085.59  C1892216157     3171085.59   \n",
       "6362460   730  TRANSFER  10000000.00  C2140038573    17316255.05   \n",
       "6362462   730  TRANSFER   7316255.05  C1869569059    17316255.05   \n",
       "6362584   741  TRANSFER   5674547.89   C992223106     5674547.89   \n",
       "\n",
       "         newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  \\\n",
       "2736446      4953893.08   C639921569             0.0             0.0        1   \n",
       "3247297      1343002.08  C1147517658             0.0             0.0        1   \n",
       "3760288       536624.41  C1100697970             0.0             0.0        1   \n",
       "5563713      4892193.09   C891140444             0.0             0.0        1   \n",
       "5996407     19585040.37  C1392803603             0.0             0.0        1   \n",
       "5996409     19585040.37  C1109166882             0.0             0.0        1   \n",
       "6168499      3576297.10   C484597480             0.0             0.0        1   \n",
       "6205439       353874.22  C1770418982             0.0             0.0        1   \n",
       "6266413      2542664.27   C661958277             0.0             0.0        1   \n",
       "6281482     10399045.08  C1806199534             0.0             0.0        1   \n",
       "6281484     10399045.08  C1909486199             0.0             0.0        1   \n",
       "6296014      3441041.46  C1082139865             0.0             0.0        1   \n",
       "6351225      3171085.59  C1308068787             0.0             0.0        1   \n",
       "6362460     17316255.05  C1395467927             0.0             0.0        1   \n",
       "6362462     17316255.05  C1861208726             0.0             0.0        1   \n",
       "6362584      5674547.89  C1366804249             0.0             0.0        1   \n",
       "\n",
       "         isFlaggedFraud  \n",
       "2736446               1  \n",
       "3247297               1  \n",
       "3760288               1  \n",
       "5563713               1  \n",
       "5996407               1  \n",
       "5996409               1  \n",
       "6168499               1  \n",
       "6205439               1  \n",
       "6266413               1  \n",
       "6281482               1  \n",
       "6281484               1  \n",
       "6296014               1  \n",
       "6351225               1  \n",
       "6362460               1  \n",
       "6362462               1  \n",
       "6362584               1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = df.query('isFlaggedFraud == 1')\n",
    "q"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f191acea",
   "metadata": {},
   "source": [
    "The 'isFlaggedFraud' column in the dataset signifies an attempt to transfer more than 200.000 in a single transaction. After analyzing the data, it's observed that there are only 16 instances out of more than 6 million transactions where 'isFlaggedFraud' is marked as '1', indicating a potentially fraudulent transaction. This means that the 'isFlaggedFraud' column is heavily skewed towards '0'.\n",
    "\n",
    "Given this significant class imbalance and the rarity of flagged fraudulent transactions, it was decided not to use the 'isFlaggedFraud' column in the classification algorithms. The low frequency of this flag makes it difficult to extract meaningful or generalizable patterns for modeling and prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0ebb7bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('isFlaggedFraud', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78ea2eaa",
   "metadata": {},
   "source": [
    "### Pearson Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8658a893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o6/x3Oec+lXSTpP/KO+f8LG9Og0Prf5TXjqX+893yPvfLs0lfnCbvM/+lvC8RXvGXD5Z3n+ouf/nkI1Spr7xR1GR5F+ETjlA2a1velXf8jPO3nypvUslUeX15tqTf5H0+X5Y3Qiv/i5BL5P3c1Fp5o0Cd5U36dCzniB7yUoG3y5t4cYKOsw85rjLZaWZ75f18WytJXZ1zh867R+xjeUHsajPb47/Wlc65P4I+53J8pbvCf+93yuvn7ZL+fSgt/C/ep5yuoaTsr6Pys2w/h8e4jyflTZ62Td5xMifL+ofkZSL8Lu+zMe7QCufNEH+7v2yLXyaI25cAb8IpoKAx7/dGb/TTqgAUUv5ozUZJPZxzC/O6Pvh7zGyCvAnDshttBgCg0GKEGwCQr5hZCzMr66dNHrqv8qhHD5H3zOxc824DijDvN4LbyxsFBACgSGGWcgBAfnOhvDS/Q7eUdHDO7c/bKuEYVZSXzlteXobCbc65rPfhAgBQ6JFSDgAAAABAAEgpBwAAAAAgAATcAAAAAAAEgIAbAAAAAFDkmdmrZpZoZt/msN7M7Gkz+9nMvjazf/3VPgm4ke+Z2c15XQccPfqrYKG/Ch76rGChvwoe+qxgob8QZq/L+534nLSUdJr/uFnSc3+1QwJuFAScSAsW+qtgob8KHvqsYKG/Ch76rGChvxA2zrklknYcoUh7SW86z0pJZc3s5CPtk4AbAAAAAIC/VlnShpDnG/1lOeJ3uAuJEv+8o9D+vltUlcsKZfsSVjyd11UIxJP/fU67/0grdP1VLKpwfj/53/+9oD8OqtD1V2FWWPvsz5S0vK5CIJ585jnt2l/4zomFWWHtM1f4ThuSpCeeeU4796cWusaVLRFpeV2H45Ffr93/+PLZW5Q5K+JF59yLx7CL7PrliG3ld7gLifz6oUbOCmvAXVgV1oAbyC8Ka8AN5BeFNeAurAi4g7H/i//+5ftqZtUlzXTOnZHNuhckLXLOveM//0HSZc65LTntjytIAAAAAAD+2nRJ1/qzlV8gadeRgm2JlHIAAAAAQDhZwRzXNbN3JF0mqYKZbZT0sKRoSXLOPS9ptqRWkn6WtE/S9X+1TwJuAAAAAECR55zr/hfrnaTbj2WfBfOrBwAAAAAA8jlGuAEAAAAA4WMFes63sGKEGwAAAACAABBwAwAAAAAQAFLKAQAAAADhU0BnKQ8C7wQAAAAAAAEg4AYAAAAAIACklAMAAAAAwodZytMxwg0AAAAAQAAIuAEAAAAACAAp5QAAAACA8GGW8nS8EwAAAAAABICAGwAAAACAAJBSDgAAAAAIH2YpT8cINwAAAAAAASDgBgAAAAAgAKSUAwAAAADCh1nK0/FOAAAAAAAQAAJuAAAAAAACQEo5AAAAACB8mKU8HSPcAAAAAAAEgIAbAAAAAIAAkFIOAAAAAAgfZilPxzsBAAAAAEAAGOEGAAAAAIQPk6alY4QbAAAAAIAAEHADAAAAABAAUsoBAAAAAOHDpGnpeCcAAAAAAAgAATcAAAAAAAEgpRwAAAAAED7MUp6OEW4AAAAAAAJAwA0AAAAAQABIKQcAAAAAhA+zlKfjnQAAAAAAIAAE3AAAAAAABICUcgAAAABA+JBSno53AgAAAACAABBwAwAAAAAQAFLKAQAAAADhE2F5XYN8gxFuAAAAAAACQMANAAAAAEAASCkHAAAAAIQPs5Sn450AAAAAACAABNwBM7NBZvZ2XtcDAAAAAJC7CLiRLz3/cA+tm/+IPn13QF5XpUj7aPlSdW7XUh3btNDrr7x02HrnnB4bOVwd27RQ9y7t9f2a1ZKkrVu36NYbrlPXDq3VrWMbvTP2zfRtnnr8UXVp30rdu7RXv153KHn37lxrT1HjnNPIEcPU5vJm6tKxrdZ8tzrbchs3blCPK7uqbcvm6tenl1IOHJAk/fbrL7rmqit0ztln6I3XXsnNqhdJ9Ff+t2L5UnVp31Kd2rbQG6/mcE4cNVyd2rbQVV0zzokJW7fothuvU7eOrXVFpzYaH3JO/PH7NfrPNVeoR7eOuvaqLlr9zde51p6iIIg+k6QJ77ytLu1b6opObfT0E4/mSluKghXLl6pr+1bqfIT+GjNquDq3baEeXTvo+zXfSZL+/PNPXd/DO46u7NRWL/7vmfRtnn78UXXr0Fo9unbQvb3v5Lojt5jlz0ceIOAOIzPrb2abzCzZzH4ws9aSBki6wsz2mNlXfrkyZvaKmW3xyw8zs0h/XU8zW25mz5jZLjP73sya5GW78sJbM1aq/e3P5nU1irTU1FSNHjFUT/3vRU2cMkPz5szSr7/8nKnMR8uWaP36dZo8Y44GDByskcOGSJKiIiPVq++9enfqLL329gS9N35c+rbnX3CRxk+arnfem6Zqp1TX66+8mOttKyqWLV2i9evWasb78zRw0FANGzIo23JPPf6Yrr62p2a8P0+lS5fWlMnvSZJKlymr/vc/oOuuvyH3Kl2E0V/5W2pqqkY/MlRPPfuiJkyeobk5nBM3rF+nSdPn6P6HBmvUcO+cGBkZqbv73KuJU2bp1bcm6N0JGefEZ558TDfecrvGTpyiW267U888+Viut62wCqrPPv1klZYsmq9x707ThMkzdfV1/8n1thVGqampevSRYXry2Rc0fvIMzZszO8f+em/6HN330GCNHj5YklSsWDE9+9KrGjtxit6eMFkrP1qmb77+SpJ03gUXadx70zT23amqdkr1bAN5IEgE3GFiZv+QdIekc51zpSS1kPS9pBGSJjjnSjrnzvKLvyHpoKRakv4pqbmkG0N2d76kXyVVkPSwpMlmdlKuNCSfWP75L9qxa19eV6NIW/3t16patZqqVKmq6OhianZ5Ky1etCBTmcULF6h12/YyM9U/82wlJ+/WtqREVYiNU+069SRJJ554oqrXPFVJiQmSpAsuulhRUd58jWeceZYS/OUIv4UL5qttuw4yM515ltc/SUmJmco45/TxqpVq1ryFJKld+45aMH++JKl8+fI6o/6Z6f2FYNFf+dvqb79WlarVVNk/JzZv0UpLspwTlyxaoFZt/vqcWCPknCgz7d27R5K0Z88eVYiNy9V2FWZB9dmkieN13fU3qVixYpKkk04qn7sNK6S++/abTP3VrEXLbPurZXp/naXk5GRtS0qSmemEE06UJB08eFAHDx5MH8zMet2RmLA1V9sFEHCHT6qk4pLqmlm0c26tc+6XrIXMLF5SS0m9nHN7nXOJkp6QdGVIsURJTzrnUpxzEyT9IKl18E0AMiQlJiq+YsX05/Fx8UpKSMhSJkHx8Rll4uIrKjExc4CwedMm/fD9GtWrf5aymj51si66uEGYa45DEhMTMvdhfEUlZunDnTt/V6lSpdMvRuLjKyqRL0HyBP2Vv2U9J8bFx2cEzb6sfXg058R7+t2vp594TG1aNNLTj4/W7Xf1DrAVRUtQfbZ+3Vp9+flnuv7qK3TLDdfou2+/CbAVRUd2fZGUpS+O1Kepqam6ultHXd74Ep13wUU6I5vrjhlTJ+vCS7juyBUWkT8feYCAO0yccz9L6iVpkKREMxtvZpWyKXqKpGhJW8xsp5ntlPSCpNCvtDc551zI83WSDtuXmd1sZp+a2acHt2V/rx/wd2X+CHosy70vTkcus2/fXvXvc5fu6XefSpYsmancqy89r6jISLVs3TZMNcZhjqYPDy9yWBnkEvorX8vunHjY/YB/0Yf79u3VfX0znxMnvTtevfvep5lzF6pX3/s0bPCDYa13URZUn6WmHtTu5N169a3xuqtXP91/b+/sXwvHJtu+yFok5z6NjIzU2xOnaMbchVr97Tf65eefMhV77aXnFRkZqctbcd2B3EXAHUbOuXHOuUvkBdVO0ij/31AbJP0pqYJzrqz/KO2cqxdSprJlvoKqJmlzNq/3onPuHOfcOVEV6mVdDRyXuPh4JWzNSLtKSExQhbjMqY5xcRWVEJKalZiwVbGxsZKkgykp6n/P3bq8VVs1bto803Yzp0/VsiWLNPSRRwkWwmz8uLHq1qm9unVqr9jYuMx9mLBVsVn6sFy5ckpO3q2DBw9mlCGlNdfQXwVH1nNiYkLCYe99XHzFLGWynBP73K0WrdqqUZOMc+KsGVPVqEkzSVLT5pczWhpGQfVZXHxFNWrcTGamevXPVEREhHb+/nvArSn8suuLrLdYHE2flipdWv8+51ytWL40fdms6VO1bOliDRkxmusO5DoC7jAxs3+YWWMzKy7pD0n75aWZJ0iqbublMDjntkiaJ2mMmZU2swgzO9XMGobsLk7SXWYWbWZdJdWRNDtXG4Qir269+lq/fp02bdyolJQD+mDObF3asFGmMpde1kizZkyTc07ffP2lSpYspQqxcXLOaeigB1W9Zk31uLZnpm0+Wr5Ub772ssY89T/FlCiRiy0qGq68qocmTp6miZOnqVGTppoxfaqcc/r6K69/sl6YmJnOPe98fTBvriRp+rQpatS4cV5UvUiivwqOuvXqa8P6ddq0yTsnzps7Ww2ynBMbNGyk2TNzOCcOflA1atRUj2t6ZtomNjZOn3/6iSTpk49Xqmq1U3KrSYVeUH3WsFETffrJSknSunW/KSUlRWXLlcutZhVadeqdoQ3r12mz318fzH3/sOuOBg0b6/30/vrK769Y/b5jR/rs43/88Yc+XrVC1WvUlOTNfP7m6y/rsSef5bojN+X1bOT5aJZyIwUmPMzsTEkvywuOUyR9JOlmeaPZ0yTVk/Sbc+5fZlZG0khJbSWVkjdB2ijn3Hgz6ynpJklfSLpGXsB+h3Nu3pFev8Q/7yhUHfnGIz3V4N+nqULZkkrcsVtDn5+tN6auyOtqhVXCiqfzugp/afnSxXp89CNKTUtTuw6d9J+bbtWkieMlSZ27XSnnnEY/MlQrli9TTEyMBg4Zobr1ztCXn3+mm66/WrVOO10W4X2vd/udvXRxg4bq2KaFDhw4oDJly0qS6tc/S/c/NCiPWnj0ikUVvO8nnXN6ZNgQLV++VDExJTRk2AjVO6O+JOn2W2/Sw0OGKS4uXhs3bNC9fXtr965dql2njkaMekzFihXTtqQkdb+is/bu2aOIiAiVOOEETZk++7DbAxAeRb2//kxJy+sq/KXlSxfr8UcfUVpamtq298+J7/rnxK7eOfHRR4ZqxUfeOfGhwf458YvPdPOhc6J/D+H/+efEL7/4TI+PHqGDqakqXqy47h0wUHXqkrUWLkH0WUrKAQ19+EH9+MMaRUdH66577tW5512Ql808KtndBpbfLF+6WE88OtLvr466/qZbNdnvr07p/TVMK9P7a7jq1DtDP/34g4Y8dL/S0tKUlpamJs0v1423/J8kqXPbFjpwIEVlypSR5E2cdt+Dg/KqiUetbInIAj0UX6LZqHz5gdv/Qf9cf18JuPMZP+C+0U9NP2qFLeAuCgpCwI0MBTHgBgqSghBwAwVZQQi4kYGAOxh5EXDz2yEAAAAAgPDJoxnB8yPeCQAAAAAAAkDAnc84514/1nRyAAAAAED+Q0o5AAAAACB8+Pm1dIxwAwAAAAAQAAJuAAAAAAACQEo5AAAAACB8mKU8He8EAAAAAAABIOAGAAAAACAApJQDAAAAAMKHWcrTMcINAAAAAEAACLgBAAAAAAgAKeUAAAAAgPBhlvJ0vBMAAAAAAASAgBsAAAAAgACQUg4AAAAACB9mKU/HCDcAAAAAAAEg4AYAAAAAIACklAMAAAAAwodZytPxTgAAAAAAEAACbgAAAAAAAkBKOQAAAAAgfEgpT8c7AQAAAABAAAi4AQAAAAAIACnlAAAAAIDwMcvrGuQbjHADAAAAABAAAm4AAAAAAAJASjkAAAAAIHyYpTwd7wQAAAAAAAEg4AYAAAAAIACklAMAAAAAwodZytMxwg0AAAAAQAAIuAEAAAAACAAp5QAAAACA8GGW8nS8EwAAAAAABICAGwAAAACAAJBSDgAAAAAIH2YpT8cINwAAAAAAASDgBgAAAAAgAKSUAwAAAADCxkgpT8cINwAAAAAAASDgBgAAAAAgAKSUAwAAAADChpTyDIxwAwAAAAAQAAJuAAAAAAACQEo5AAAAACB8yChPxwg3AAAAAAABIOAGAAAAACAApJQDAAAAAMKGWcozEHAXEgkrns7rKuAYxV94V15XAcdgx8f/zesq4Bjxt75gWf7rtryuAo7B8vU787oKOEZ3XFQ9r6uAY1C2RGReVwFhQko5AAAAAAABYIQbAAAAABA2pJRnYIQbAAAAAIAAEHADAAAAABAAUsoBAAAAAGFDSnkGRrgBAAAAAAgAATcAAAAAAAEgpRwAAAAAEDaklGdghBsAAAAAgAAQcAMAAAAAEABSygEAAAAA4UNGeTpGuAEAAAAACAABNwAAAAAAASClHAAAAAAQNsxSnoERbgAAAAAAAkDADQAAAABAAEgpBwAAAACEDSnlGRjhBgAAAAAgAATcAAAAAAAEgJRyAAAAAEDYkFKegRFuAAAAAAACQMANAAAAAEAASCkHAAAAAIQNKeUZGOEGAAAAACAABNwAAAAAAASAlHIAAAAAQPiQUZ6OEW4AAAAAAAJAwA0AAAAAQABIKQcAAAAAhA2zlGdghBsAAAAAgAAwwg0AAAAACBtGuDMwwg0AAAAAQAAIuAEAAAAACAAp5QAAAACAsCGlPAMj3AAAAAAABICAGwAAAACAAJBSDgAAAAAIHzLK0zHCDQAAAABAAAi4AQAAAABFnpldbmY/mNnPZnZfNuvLmNkMM/vKzFab2fV/tU9SygEAAAAAYVMQZyk3s0hJz0pqJmmjpE/MbLpz7ruQYrdL+s4519bMYiX9YGZjnXMHctovI9wAAAAAgKLuPEk/O+d+9QPo8ZLaZynjJJUy7xuFkpJ2SDp4pJ0ScAMAAAAACj0zu9nMPg153ByyurKkDSHPN/rLQv1XUh1JmyV9I+lu51zakV6TlHIAAAAAQNjk15Ry59yLkl7MYXV2lXZZnreQ9KWkxpJOlfSBmS11zu3O6TUZ4QYAAAAAFHUbJVUNeV5F3kh2qOslTXaenyX9Jqn2kXZKwA0AAAAAKOo+kXSamdUws2KSrpQ0PUuZ9ZKaSJKZxUv6h6Rfj7RTUsoBAAAAAGGTX1PKj8Q5d9DM7pA0V1KkpFedc6vN7FZ//fOShkp63cy+kZeC3t85t+1I+yXgBgAAAAAUec652ZJmZ1n2fMj/N0tqfiz7JKUcAAAAAIAAMMINAAAAAAibgphSHhQC7nzMzKrLm/ku2jl3xB9ULyg+Wr5UY0aNUFpamtp37KKeN9yUab1zTmNGjdDyZUsUExOjh4eOUO069bR16xYNeuA+bd++TWamjl26qXuPayVJTz3+qJYuXqjo6GhVqVJVA4eMUKnSpfOieUXa8w/3UMtLz1DSjmSd03VEXlenyHDOafQjw7Vs6WLFxMRoyPCRqlO33mHlNm3coP797tGuXbtUp05dDR85WtHRxXLcfuuWLXpwwL3avm2bLCJCnbt0U49rrpMkff/9Gg0f8rD+/PNPRUVG6v6HBql+/TNzu+kFlnNOox4ZrmVLFiumRIyG5tBnGzduUP++92j3rl2qXbeuRjwyWtHFiuW4/drfftW9fXpn2v7/7rhLV1/bUz98/72GDXlY+/btU6VKlfXI6MdUsmTJ3Gx2obTm81Wa8upTcmlpOr9pGzXtdHWm9Z8tnqf5U8dKkorHnKAuN/dR5Rq1JEmLZ76rlR/MkJPThU3bqmHbbrle/6Jm65rP9OXkl+Rcmmpc0Ey1m3bNttyO9T9qwRP9dMF196rK2RdLkg7s26PPJjyj3VvWSTKd0/1ula9xxImJ8TesWrFM/x0zSqlpqWrdvpN6XHdjpvXOOT0zZqRWfrRUMTExum/gMJ1eu67Wr/tNgwf0Sy+3ZfNGXX/z7era/RoNHtBX69etlSTt2ZOskiVL6ZWx7+Vms1DEkVKOXJOamqrRI4bqqf+9qIlTZmjenFn69ZefM5X5aNkSrV+/TpNnzNGAgYM1ctgQSVJUZKR69b1X706dpdfenqD3xo9L3/b8Cy7S+EnT9c5701TtlOp6/ZWcfloPQXprxkq1v/3ZvK5GkbNs6RKtX79W02fP00ODhmr40EHZlnvyicd09TU9NWP2PJUuXVpTJr13xO0joyLVp999mjLjfb01boImjB+nX/xj7skxj+qW227XxEnTdNsdd+vJMY/mRlMLjWVLl2j9urWa8f48DRw0VMOGDMq23FOPP6arr+2pGe/7fTb5vSNuX71GTU2cPE0TJ0/TO+9OVkxMCTVu2kySNHjgA7q7dx9NmjpDjZs21euvvhx8Qwu5tNRUTXrpcd384GPq/9Rb+mLph9q64bdMZU6KP1l3DP2v7n3iDTXvep0mPj9akrRl3a9a+cEM9R79ovo9/ppWf/aRkjZvyItmFBkuLVVfvPe8LrllkFrc96w2fL5Eu7euz7bcNzPeUMXa/8y0/KspL6li7X+pxYDn1ezep1UqvkpuVb3ISE1N1VOjh2vUU//TGxOmacHc97X2118ylVn10VJt3LBOYyfNUp/7H9YTo4ZJkqqdUkOvjH1Pr4x9Ty++OUHFi8eowWVNJEkPj3gsfV3DRk11aaMmud42FG1FOuA2s/vM7BczSzaz78yso7+8p5ktN7MnzGynmf1qZhf5yzeYWaKZXReynzJm9qaZJZnZOjN70Mwi/HWDzOztkLLVzcyZWZT/fJGZDfVfL9nM5plZBb/4Ev/fnWa2x8wuzJ13Jhirv/1aVatWU5UqVRUdXUzNLm+lxYsWZCqzeOECtW7bXmam+meereTk3dqWlKgKsXGqXccbATrxxBNVveapSkpMkCRdcNHFiorykjXOOPMsJfjLkbuWf/6Lduzal9fVKHIWLZyvNu06yMx05lneMZOUlJipjHNOn6xaqabNW0iS2rbvqIUL5h9x+9jYuPRR1xNPLKmaNWsqMcE7tsxMe/fsleSNFsTGxeVWcwuFhQvmq+1R9NnHq1aqmd9n7dp31IL58496+1UrV6hq1aqqVKmyJGnt2t/073POlSRdeOHFmv/BvKCbWeit/3mNKpxcWRUqVlJUdLT+eUkTffvxskxlatSurxNKlpIknXJ6Pe3aniRJSti0TqecXlfFiscoMjJKteqera9XLTnsNRA+O9b9pJIVTlbJChUVERWtqv+8VJu/WXVYuZ+XzFTlMy9S8ZJl0pel/LFPSb98q+oXePMkRURFq9gJZIiE2/erv1HlKtVUqXJVRUdHq3Hzllq+ZGGmMsuXLFSLVu1kZqpX/yztSU7W9m1Jmcp8/skqVa5SVRVPrpRpuXNOCz+cqybNWwXeFsibvzs/PvJAkQ64Jf0iqYGkMpIGS3rbzE72150v6WtJ5SWNkzRe0rmSakm6WtJ/zezQ2fYZfx81JTWUdK28H0U/Wlf55eMkFZPU119+qf9vWedcSefcimNtYH6SlJio+IoV05/Hx8UrKSEhS5kExcdnlImLr6jExMwXkps3bdIP369RvfpnHfYa06dO1kUXNwhzzYH8KzEhQRVDj6v4iumB8SE7d/6uUqVKp38xFR9fUYn+F1NHs/2mTRv1/Zo1qn+md8z16z9AT4wZrRZNGurxx0bprl73BNK2wioxMSHzufBY++wotp/z/ixd3qpN+vNap52uRQu9gH3e3DnaunVLeBtVBO3cnqSy5TO+bCpTPla7duT8yzCrPpyp2v88X5J0crUa+vW7r7Q3eZcO/PmHvvt8pXZuS8xxWxy//bu2q0S5CunPS5Qtr/27tmcus3O7Nn2zQqdefHmm5Xu3bVXxkmX06bgn9eGjd+vT8U/r4J9/5Eq9i5KkpETFhlwDxsbFKykp63ViNmWyXCcu+OB9NW7e8rD9f/3FZyp3UnlVqXZKmGsOHFmRDridc+865zY759KccxMk/STpPH/1b86515xzqZImSKoqaYhz7k/n3DxJByTVMrNISVdIut85l+ycWytpjKRrjqEqrznnfnTO7Zc0UdLZYWlgPuOcO2xZ1gkVnI5cZt++verf5y7d0+++w+4/fPWl5xUVGamWrduGqcZA/ndUx9XhRdLL/NX2+/btVd/ed6lf/wHpx9y7E95R3/73a+78xep77/0aPPCB42lC0XOcffZX26ccOKDFCxeoeYuMoGHw0OEa/844Xdm1k/bt26vo6GJ/s/L4O3765nOtnD9Lba+9TZIUX6W6GnfsoecG9dYLQ/uqUvVaioiMzONaFnbZHlSZnn455SXVb9tTFpG5L9LSUrVz4y+qeXErNe33lKKKxej7+dwDHHbZndsOG5LM7vyX8f+UlBQtX7JIlzU5/Feb5s97X01aMLqN3FekJ00zs2sl3SOpur+opKQKklIlhX6ltl+SnHNZlx0qX0zSupB16yRVPoaqbA35/z5/v3/JzG6WdLMkPfnf53T9DTcfw0vmvrj4eCVszWhqQmKCKmRJRY2Lq6iEhIwyiQlbFRsbK0k6mJKi/vfcrctbtVXjpplPpDOnT9WyJYv0vxdfY1ZEFHrj3xmrye9NlCTVO6O+toYeVwlbD0vxLleunJKTd+vgwYOKioryysR6ZeIrVsxx+5SUFPXpdZdatW6rJs0yjrkZ06fo3vu9ILt5i5Ya8vCDwTS0EBk/LnOfJRxHn8XFVzzi9suWLVHtuvVUvkLGaF6NmqfqhZdeleSlly9ZvCjsbSxqypaP1c7tGSNru7YnqcxJFQ4rt3ntz5rwv1G6+aFHdWKpjDTlC5q20QVNvSyEWW+/oDLluTUjSCXKVND+3zMyEPbv3K4SpU/KVOb3DT9p1RvenBR/7t2trWs+k0VEqHz12ipRpoLKV/+HJKnyWRfrBwLusIuNi1dSyDVgUmKCKsTGHVOZVR8t1em16+ik8pmPxYMHD2rpog/1whsTAqo9suJ6PEORHeE2s1MkvSTpDknlnXNlJX2rY8/u3yYpRVJofko1SZv8/++VdELIuoo6etl8HRuy0rkXnXPnOOfOye/BtiTVrVdf69ev06aNG5WSckAfzJmtSxs2ylTm0ssaadaMaXLO6Zuvv1TJkqVUITZOzjkNHfSgqtesqR7X9sy0zUfLl+rN117WmKf+p5gSJXKxRUDeuLJ7D02cNE0TJ01To8ZNNXP6VDnn9PVX3jETm+UCxcx0znnn68N5cyVJM6ZN0WWNG0uSGl7WONvtnXMaPPAB1ahZU9dcl/kOmdjYOH36yceSpI9XrVS1U6oH3+gC7sqreqRPaNaoSVPNOIo+O/e88/WB32fTp01RI7/PLmvU+Ijbvz97llq2ap1pf9u3e6mzaWlpeumF59T1iiuDbG6RULVWbSVt2ajtCZt1MCVFXyybr3rnXpKpzO9JCXpt9IPqcfeDiqtULdO65J2/p5f5etUS/atB01yre1FUrtpp2rNts/Zu36q0gyna8MUSnXzGeZnKtBr4ilo97D2qnHWR/tnlNlU+80LFlC6nEuUqKDlhoyQp8cevVDq+al40o1D7R90ztHHDOm3ZtFEpKSlaMO99XdTgskxlLmrQSHNnT5dzTqu/+Uonliyp8hVi09fPn/e+mmSTTv7ZJytV7ZQaios/lstwIDyK8gj3ifIC2iRJMrPrJZ1xrDtxzqWa2URJw/0R85PkjZo/5hf5UlJ/M6smaZek+49h90mS0uTdG/7jsdYtv4mKitK99z+ou267UalpaWrXoZNOrXWaJk0cL0nq3O1KXdygoZYvW6KObVooJiZGA4d4Py/11Refa/bM6ap12um6qltHSdLtd/bSxQ0a6tFHhunAgQO6/dYbJEn165+l+x8alCdtLMreeKSnGvz7NFUoW1I/zxmqoc/P1htTC/S0AwVCg0sbatnSxWrbspliSpTQ4KEZP8l2+2036eHBwxQXF69evfupf7/eevaZJ/WPOnXUsVPXI27/5RefaeaMaTrttNPVrXN7SdKdd9+jBpc21MDBQzV65AilHjyoYsWL66GHh+R+wwuwBpc21LIli9WmZTPFxJTQkGEhfXbrTXp4iN9n9/TTvX1769mnn1TtOnXUsXPXv9x+//79WvnRR4f1yZzZMzX+nXGSpCZNm6lDx8650NLCLTIySp1v7K0XhvRRWlqazm/SWidXq6Hlc6dKki5u0UFzJ76mvcm79N6Lj0uSIiIj1edRb4b41x59UPuSd3n7ual3+uRqCEZEZKTO7nyrlj7/sFxamqqf31RlTj5Fvyx/X5J06sWHB2mh/tnpFn389hilHTyoE8vH65yreuVCrYuWqKgo3d1vgPrddavS0lLVsm1H1Ti1lqZN8rKD2nfupgsubqBVHy1Rj06tVDwmRv0fGpa+/R9/7Ndnq1aoz/0DD9v3gnnvqzGTpSGPWHb37xUVZjZc0m3ygto3Jf1b0luSDkq60Tl3iV+ulqSfnHMWsu1GSVc655aZWTl5E6e1kPSHvJHzYc65NL/ss5J6yBsNHyXpRfm/rW1miyS97Zx72S/bM8trD/HrGC3pcufcyuzasvuPtKLbkQVU/IV35XUVcAx2fPzfvK4CjhHZbAXLgh+YNKwgWb5+Z15XAcfojouq53UVcAxOLlOsQP8Vq/J/U/NlbLLxfx1y/X0t0gF3YULAXfAQcBcsBNwFDwF3wULAXbAQcBc8BNwFCwF3MPIi4C6y93ADAAAAABCkonwPNwAAAAAgzJilPAMj3AAAAAAABICAGwAAAACAAJBSDgAAAAAIHzLK0zHCDQAAAABAAAi4AQAAAAAIACnlAAAAAICwYZbyDIxwAwAAAAAQAAJuAAAAAAACQEo5AAAAACBsSCnPwAg3AAAAAAABIOAGAAAAACAApJQDAAAAAMKGlPIMjHADAAAAABAAAm4AAAAAAAJASjkAAAAAIGxIKc/ACDcAAAAAAAEg4AYAAAAAIACklAMAAAAAwoeM8nSMcAMAAAAAEAACbgAAAAAAAkBKOQAAAAAgbJilPAMj3AAAAAAABICAGwAAAACAAJBSDgAAAAAIG1LKMzDCDQAAAABAAAi4AQAAAAAIACnlAAAAAICwIaM8AyPcAAAAAAAEgIAbAAAAAIAAkFIOAAAAAAgbZinPwAg3AAAAAAABIOAGAAAAACAApJQDAAAAAMKGjPIMjHADAAAAABAAAm4AAAAAAAJASjkAAAAAIGyYpTwDI9wAAAAAAASAgBsAAAAAgACQUg4AAAAACBsyyjMwwg0AAAAAQAAIuAEAAAAACAAp5QAAAACAsImIIKf8EEa4AQAAAAAIAAE3AAAAAAABIKUcAAAAABA2zFKegRFuAAAAAAACQMANAAAAAEAASCkvJIpF8d1JQbPj4//mdRVwDE467468rgJQqK1d/EReVwHH4ILq5fO6CjhGXCsiNxk55ek48gAAAAAACAABNwAAAAAAASClHAAAAAAQNmSUZ2CEGwAAAACAABBwAwAAAAAQAFLKAQAAAABhwyzlGRjhBgAAAAAgAATcAAAAAAAEgJRyAAAAAEDYkFKegRFuAAAAAAACQMANAAAAAEAASCkHAAAAAIQNGeUZGOEGAAAAACAABNwAAAAAAASAlHIAAAAAQNgwS3kGRrgBAAAAAAgAATcAAAAAAAEgpRwAAAAAEDZklGdghBsAAAAAgAAQcAMAAAAAEABSygEAAAAAYcMs5RkY4QYAAAAAIAAE3AAAAAAABICUcgAAAABA2JBRnoERbgAAAAAAAkDADQAAAABAAEgpBwAAAACEDbOUZ2CEGwAAAACAABBwAwAAAAAQAFLKAQAAAABhQ0Z5Bka4AQAAAAAIACPcAAAAAICwYdK0DIxwAwAAAAAQAAJuAAAAAAACQEo5AAAAACBsyCjPwAg3AAAAAAABIOAGAAAAACAApJQDAAAAAMKGWcozMMINAAAAAEAACLgBAAAAAAgAKeUAAAAAgLAhozwDI9wAAAAAAASAgBsAAAAAgACQUg4AAAAACBtmKc/ACDcAAAAAAAEg4AYAAAAAIACklAMAAAAAwoaM8gy5OsJtZq+b2bAjrHdmVutoyv7F6wwys7f/bj0BAAAAADhepJQHxMyKm9kjZrbezPab2U9m1s+YQQAAAAAAigQC7uC8K6mJpFaSSkm6RtLNkp7KrrB5ilR/OOc0csQwtbm8mbp0bKs1363OttzGjRvU48quatuyufr16aWUAwckSb/9+ouuueoKnXP2GXrjtVdys+qFmnNOo0YMU9uWzdT1CP2yaeMGXd29q9q2aq57+/RSSsqBI26/dcsW3Xj9NerYtqU6tW+tsW+9kb6v779fo2uu6qZundvrqm6d9M03Xwff0CLu+Yd7aN38R/TpuwPyuio4SvRZ3lv10TL16NxG3Tu21Nuvv3zYeuecnnpshLp3bKme3Tvqh++/S1+XnLxbD/Xvrau7tNXVXdvq26+/TF83acJY9ejcRtd2a6/nnh6TG00pMlYsX6orOrZSl3Yt9OZrLx223jmnx0cPV5d2LXR1tw76Yc13mdanpqbq2u6d1Oeu29KXPfPEo7qiU2td3a2D+ve5U8nJuwNvR1H00bKl6tT2crVv3VyvvfLiYeudcxo9cpjat26uKzq3y3S9MnjgADVteJG6dWybm1VGCDPLl4+8EEiAZ2Z1zGyRme00s9Vm1i6Hcv3MbIuZbTaz/2RTpIKZfWBmyWa22MxOCdn2KTPbYGa7zewzM2twhPq8a2ZbzWyXmS0xs3oh6143s2fNbJb/OqvM7NSQ9fX8OuwwswQzG+AvjzCz+8zsFzPbbmYTzewkf10TSc0ldXbOfeucO+icWynpakm3h6TNLzKz4Wa2XNI+STXNrLmZ/eDX9X9+u288hre/wFi2dInWr1urGe/P08BBQzVsyKBsyz31+GO6+tqemvH+PJUuXVpTJr8nSSpdpqz63/+Arrv+htyrdBGwbOkSrV+/VtNnz9NDg4Zq+NBB2ZZ78onHdPU1PTVjtt8vk9474vaRUZHq0+8+TZnxvt4aN0ETxo/TL7/87O1rzKO65bbbNXHSNN12x916csyjudHUIu2tGSvV/vZn87oaOAb0Wd5KTU3VE6OH6dGnntObE6dr/rzZWvvrL5nKrPxoqTauX69xk2er34BBenzk0PR1T48ZqfMvvFhvvzdDr42brFNq1JQkff7px1q2eKFee2ey3pw4TVde3TM3m1WopaamasyoYXr8mRf0zqQZ+mDObP3268+ZyqxYvkQb1q/Tu9Pm6L4HB2v0I4MzrZ/4zluqXuPUTMvOu+AijZ04TW9PnKpq1arrzVcPD+RxfFJTUzVyxBA9/dxLem/qTM19f5Z+/SVz3y1ftkQb1q3T1Jlz9eDAIXpkWEbftW3XUc88R78gfwh7wG1m0ZJmSJonKU7SnZLGmtk/spS7XFJfSc0knSapaTa76yFpqKQKkr6UNDZk3SeSzpZ0kqRxkt41s5gcqvW+/xpxkj7Psh9J6i5psKRykn6WNNyvYylJH0qaI6mSpFqS5vvb3CWpg6SG/rrfJR26EmomaZVzbkPoizjnVknaKG/k+5BDI9+lJO2S9J6k+yWVl/SDpItyaFOBt3DBfLVt10FmpjPPOlvJybuVlJSYqYxzTh+vWqlmzVtIktq176gF870uKF++vM6of6aiopj7L5wWLZyvNkfRL5+sWqmmfr+0bd9RCxfMP+L2sbFxqlPX+67rxBNLqmbNmkpMSJDkfQu6d89eSdKePcmKjYvLreYWWcs//0U7du3L62rgGNBneWvN6m9UuWo1VapSVdHR0WrSrKWWLV6QqcyyxQvVonU7mZnq1T9Le5KTtW1bkvbu2aOvvvhMrdt3liRFR0erVKnSkqRpkyaox3U3qFixYpKkcieVz92GFWLfffuNqlSppspVqio6upiatmipJYsy99mSRQvUsk17mZnOONPvs6QkSVJiwlYtX7pY7Tp0zrTN+RdenH7tUa/+WUpM3Jo7DSpCVn/7tapWq6Yqft81v7yVFi2cn6nM4oXz1bqt13f1zzpbe0KuV/51zrkqU6ZMXlQdOEwQI9wXSCopaaRz7oBzboGkmfKC2lDdJL3mjwDvlTQom33Ncs4tcc79KekBSReaWVVJcs697Zzb7o8ej5FUXNI/stmHnHOvOueS/f0MknSWmYUehZOdcx875w7KC8bP9pe3kbTVOTfGOfeHv49V/rpbJD3gnNsYst8uZhYl7wuCLTm8P1v89Ye87pxb7b92S0mrnXOT/edPSyq0Z/HExATFV6yY/jw+vmJ6AHbIzp2/q1Sp0ul/2OLjKyoxMXMZhFdiQoIqHke/HM32mzZt1Pdr1qj+mWdJkvr1H6AnxoxWiyYN9fhjo3RXr3sCaRsA/F3bkhIVF59xbouNjz/sy8htSQmZy8TFa1tigjZv2qiyZcvpkcEP6oYeXTRq2EDt3+99ebJh3Vp9/eVnuqVnd915c0+tWf1N7jSoCEhKSlBcyN+juLiKSkrM3GdJiYmKz9JnSUne36wnHxupO+7uq4iInC+XZ06brAsvyjHJEn9TYkKC4uNPTn8eH19RSVmu/7zryIwycdmUQd7J69Txwp5SXknSBudcWsiydZIqZ1cuS5ms0tc75/ZI2uFvJzPrY2Zr/NTrnZLKKHMgK79cpJmN9FO/d0ta668KLRsa1O6T94WBJFWVlDlfLMMpkqb4afM7Ja2RlCopXtI2SSfnsN3J/vrD2qgs74lzzskbES+cnDtsUdYDIZsieXawFBXuOPvlr7bft2+v+va+S/36D1DJkt6h9u6Ed9S3//2aO3+x+t57vwYPfOB4mgAAYXd058bsy6SmHtRPP6xRhy5X6JWx7ykmpoTGvu7NPZKamqrk5N16/rVxuu3uPnp4QN9s94Njl31/HFbq8DIyLVuySOVOOkm169Y7bP0hr7/8vCKjItWiFfcJh1t2RwDXiCioggi4N0uqapknAKsmaVOWclvkBbShZbJKX29mJeWlj2/279fuL2+UvJxzrqy8dOzsjrKrJLWXl7JeRlL1Q7s8irZskHTqEda1dM6VDXnEOOc2yUtDP//QaHxIG87z2xSazxR6utgiqUpIeQt9npWZ3Wxmn5rZp6+8dPhkEvnR+HFj1a1Te3Xr1F6xsXFK2JrxXUdCwtbDUonLlSun5OTdOnjwYEaZWNKNw238O2PVrXN7devcXrFxcdp6HP0SX7FijtunpKSoT6+71Kp1WzVp1jy9zIzpU9Skqfe8eYuW+pZJ0wDkM7Fx8UpMyDi3JSUkqEKF2CxlKmYuk5ig8rFxio2rqNi4eNU940xJ0mVNmuvHH75L3++ljZrKzFS3Xn1FmGnXzt9zoUWFX1xcRSWG/D1KTNyqClmuIWLj4pWQpc8qxMbp668+19LFC9WxdVM9dH8fffbpKg164N70crNmTNXypYs1eNhogrwAxMfHKyEhI1k0IeHwvouPj1fC1owyidmUAfKDIALuVZL2SrrXzKLN7DJJbSWNz1JuoqSeZlbXzE6Q9HA2+2plZpeYWTF593Ifui+6lKSDkpIkRZnZQEmlc6hPKUl/Stou6QRJI46hLTMlVTSzXub9zFcpMzvfX/e8pOHmT+RmZrFm1l6SnHMfyrvXe5I/6VqkmV0gL139OefcTzm83ixJ9c2sg5+afrukijmUlXPuRefcOc65c2646eZjaFbeufKqHpo4eZomTp6mRk2aasb0qXLO6euvvlTJkqUOC6bNTOeed74+mDdXkjR92hQ1atw4L6peqF3ZvYcmTpqmiZOmqVHjppp5FP1yznnn60O/X2ZMm6LL/H5peFnjbLd3zmnwwAdUo2ZNXXPd9Zn2Fxsbp08/+ViS9PGqlap2SvXgGw0Ax6B23TO0cf16bd60USkpKZr/wfu6+NJGmcpccullmjtrupxzWv3NVzqxZElVqBCr8hUqKC6+otav/U2S9NknK9Mn4mpwWWN97p//Nqxbq5SUFJUpWy53G1dI1al3hjZsWOf32QF9OPd9NWiYuc8aNGys92dOk3NO3379lU4sWUoVYmP1f3feo+lzFmrKrA819JEx+vc552vQ8NGSvJnP3379ZY1+8lnFlCiRF00r9OrWq68N69Zp00av7+bNma2Gl2W+/rv0ssaaNcPru2+++lIlSx1+vYK8Y5Y/H3kh7LNNOecOmDcr+f/kTf61SdK1zrnvQ78BdM69b2ZPyhvtTZP0oLxJ0kKNkxeIXyhvsrND6+fKmwjtR3nB/RPKnJod6k1JLfx67JD0kKTbciibtS3JZtZM3k95PSwvcH9S3pcKT8kbJZ9nZpUkJUqaIGmav3lneROxzZGXvr5J0suSRh/h9baZWVd5926/IS9A/9R/3UKnwaUNtWzJYrVp2UwxMSU0ZFjGdyG333qTHh4yTHFx8ep1Tz/d27e3nn36SdWuU0cdO3eVJG1LSlL3Kzpr7549ioiI0NtvvaEp02enpynj72lwaUMtW7pYbVs2U0yJEho8NKRfbrtJDw/2+6V3P/Xv11vPPvOk/lGnjjp26nrE7b/84jPNnDFNp512urp1bi9JuvPue9Tg0oYaOHioRo8codSDB1WseHE99PCQ3G94EfPGIz3V4N+nqULZkvp5zlANfX623pi6Iq+rhSOgz/JWVFSUet07QH3vukVpqalq1a6japxaS9MmTZAkte98hS64+FKtWL5U3Tu2VPGYErp/YMYs5Xf3HaChA/srJSVFlSpXTV/Xql0njRzyoK67ooOioqM1YNAIRkzDJCoqSn36P6Bet9+ktLQ0tWnXUTVPPU2T3/PGgDp1uVIXXXKpPlq2RF3bX67iMTF6cNDwv9zvmFHDlJKSortv834lpV79s9T/gUFBNqXIiYqK0r0DHtIdt92g1NQ0te/QWafWOk3vTfT6rku3K3VJg4ZavnSJ2rdurpiYGA0KuV4ZcO89+vTTT7Rz5+9q2bShbvm/O9WhU5e8ag6KOOM+ofzLT8vfKKmHc27hkcr+cTDb212Qj3HoFSwnnXdHXlcBKNTWLn4ir6uAYxAdGcgvyyJAxaLos4KkZPGC/c1bwyeW58sr3cW9L87195XfU8pnzKyFvBH0/ZL6yRtFX5mnlQIAAACAo0SmTga+6sp/LpQ3M/o2efe+d3DO7c/bKgEAAAAAjhUj3PmMc26Qsv9NcgAAAABAAULADQAAAAAIGzLKM5BSDgAAAABAAAi4AQAAAAAIACnlAAAAAICwYZbyDIxwAwAAAAAQAAJuAAAAAAACQEo5AAAAACBsyCjPwAg3AAAAAAABIOAGAAAAACAApJQDAAAAAMImgpzydIxwAwAAAAAQAAJuAAAAAAACQEo5AAAAACBsyCjPwAg3AAAAAAABIOAGAAAAACAApJQDAAAAAMLGyClPxwg3AAAAAAABIOAGAAAAACAApJQDAAAAAMImgozydIxwAwAAAACKPDO73Mx+MLOfzey+HMpcZmZfmtlqM1v8V/tkhBsAAAAAUKSZWaSkZyU1k7RR0idmNt05911ImbKS/ifpcufcejOL+6v9EnADAAAAAMKmgM5Sfp6kn51zv0qSmY2X1F7SdyFlrpI02Tm3XpKcc4l/tVNSygEAAAAAhZ6Z3Wxmn4Y8bg5ZXVnShpDnG/1loU6XVM7MFpnZZ2Z27V+9JiPcAAAAAIBCzzn3oqQXc1id3bC8y/I8StK/JTWRVELSCjNb6Zz7MafXJOAGAAAAAIRNwcwo10ZJVUOeV5G0OZsy25xzeyXtNbMlks6SlGPATUo5AAAAAKCo+0TSaWZWw8yKSbpS0vQsZaZJamBmUWZ2gqTzJa050k4Z4QYAAAAAFGnOuYNmdoekuZIiJb3qnFttZrf66593zq0xszmSvpaUJull59y3R9ovATcAAAAAIGws29uh8z/n3GxJs7Msez7L80clPXq0+ySlHAAAAACAABBwAwAAAAAQAFLKAQAAAABhE1EwM8oDwQg3AAAAAAABIOAGAAAAACAApJQDAAAAAMLGjJzyQxjhBgAAAAAgAATcAAAAAAAEgJRyAAAAAEDYkFGegRFuAAAAAAACQMANAAAAAEAASCkHAAAAAIRNBDnl6RjhBgAAAAAgAATcAAAAAAAEgJRyAAAAAEDYkFGegRFuAAAAAAACQMANAAAAAEAASCkHAAAAAISNkVOejhFuAAAAAAACwAg3kEf44g8AAOSWqEguPIC8QMANAAAAAAgbBpYykFIOAAAAAEAACLgBAAAAAAgAKeUAAAAAgLCJIKc8HSPcAAAAAAAEgIAbAAAAAIAAkFIOAAAAAAgbEsozMMINAAAAAEAACLgBAAAAAAgAKeUAAAAAgLAxZilPxwg3AAAAAAABIOAGAAAAACAApJQDAAAAAMImgozydIxwAwAAAAAQAAJuAAAAAAACQEo5AAAAACBsmKU8AyPcAAAAAAAEgIAbAAAAAIAAkFIOAAAAAAgbMsozMMINAAAAAEAACLgBAAAAAAgAKeUAAAAAgLBhlvIMjHADAAAAABAAAm4AAAAAAAJASjkAAAAAIGwiyChPxwg3AAAAAAABIOAGAAAAACAApJQDAAAAAMKGWcozMMINAAAAAEAAGOEGAAAAAIQN49sZGOEGAAAAACAABNwAAAAAAASAlHIAAAAAQNhEMGlaOka4AQAAAAAIAAE3AAAAAAABIKUcAAAAABA2ZJRnYIQbAAAAAIAAEHADAAAAABAAUsoBAAAAAGFj5JSnY4QbAAAAAIAAEHADAAAAABAAUsoBAAAAAGFDRnkGRrgBAAAAAAgAATcAAAAAAAEgpRwAAAAAEDYR5JSnY4QbAAAAAIAAEHADAAAAABCAfBVwm5kzs1p/c9u1ZtY03HUKmplVM7M9ZhaZ13UBAAAAgONllj8feSFfBdwFnZnVNbPpZrbLzJLNbKGZXXSkbZxz651zJZ1zqblVTwAAAABA8Ai4w8TMTpW0XNI3kmpIqiRpiqR5ZnZhDtsU6UnrnHMaOWKY2lzeTF06ttWa71ZnW27jxg3qcWVXtW3ZXP369FLKgQOSpN9+/UXXXHWFzjn7DL3x2iu5WfVC7Xj7Jaft1/72q7p1ap/+uOi8f+ntN1+XJP3w/fe65qor1LlDW935f7dqz549udLWouz5h3to3fxH9Om7A/K6KjhK9FneW/XRMvXo3EbdO7bU26+/fNh655yeemyEundsqZ7dO+qH779LX5ecvFsP9e+tq7u01dVd2+rbr7+UJP30w/e69fqr9J+rOuuma7vpu9Xf5FZzioQVy5fqio6t1KVdC7352kuHrXfO6fHRw9WlXQtd3a2DfljzXab1qampurZ7J/W567b0Zc888aiu6NRaV3froP597lRy8u7A21GYBXXdIUlvvfG6OrZrrU7t26h/33v0559/SpLmzX1fHdu11tln1NbqbznmEKxjCrj9tO2+Zva1P4o7wcxi/HVtzOxLM9tpZh+Z2Zn+8uvNbEbIPn42s4khzzeY2dkhL9PKzH41s21m9qiZRfjlTjWzBWa23V831szK5lDP88xshV+XLWb2XzMrFrLemdmtZvaTmf1uZs+aZSQZmNlNZrbGH6X+zsz+5S+vZGaTzCzJzH4zs7tCXnaQpBXOuQecczucc8nOuaclvSVplL99df+1bzCz9ZIWhCyL8svUMLMl/mt/6Nft7WPpp4Ji2dIlWr9urWa8P08DBw3VsCGDsi331OOP6epre2rG+/NUunRpTZn8niSpdJmy6n//A7ru+htyr9JFwPH2S07bV69RUxMnT9PEydP0zruTFRNTQo2bNpMkDR74gO7u3UeTps5Q46ZN9fqrh1/IIrzemrFS7W9/Nq+rgWNAn+Wt1NRUPTF6mB596jm9OXG65s+brbW//pKpzMqPlmrj+vUaN3m2+g0YpMdHDk1f9/SYkTr/wov19nsz9Nq4yTqlRk1J0nPPjFHPG2/Tq+Mm6T+33KHnnx6Tq+0qzFJTUzVm1DA9/swLemfSDH0wZ7Z++/XnTGVWLF+iDevX6d1pc3Tfg4M1+pHBmdZPfOctVa9xaqZl511wkcZOnKa3J05VtWrV9earhwfyOHpBXXckJCRo3Ng39c7ESZo8babS0lI1Z/YsSVKtWqfriaee0b/POTf4BhZRZpYvH3nh74xwd5N0ubxR3DMl9fQD0lcl3SKpvKQXJE03s+KSFktqYGYRZnaypGhJF0uSmdWUVFLS1yH77yjpHEn/ktRe0n/85SbpEXkjx3UkVZUX5GYnVVJvSRUkXSipiaT/y1KmjaRzJZ3lt6mFX6eu/n6vlVRaUjtJ2/3Af4akryRV9vfZy8xa+PtrJundbOoyUdLFZnZCyLKGfhtaZFN+nKSP5b2PgyRdk0MbC7yFC+arbbsOMjOdedbZSk7eraSkxExlnHP6eNVKNWvuvVXt2nfUgvnzJUnly5fXGfXPVFRUkU4UCLvj7Zej2X7VyhWqWrWqKlWqLElau/a39D96F154seZ/MC/oZhZ5yz//RTt27cvrauAY0Gd5a83qb1S5ajVVqlJV0dHRatKspZYtXpCpzLLFC9WidTuZmerVP0t7kpO1bVuS9u7Zo6+++Eyt23eWJEVHR6tUqdKSvIvSvXu9rJ69e/aoQmxc7jasEPvu229UpUo1Va5SVdHRxdS0RUstWZS5z5YsWqCWbdrLzHTGmX6fJSVJkhITtmr50sVq16Fzpm3Ov/Di9GuPevXPUmLi1txpUCEV5HVHamqq/vzjDx08eFD7//hDsXHe8VXz1FNV3f/SCwja3wm4n3bObXbO7ZAXgJ4t6SZJLzjnVjnnUp1zb0j6U9IFzrlfJSX75RpKmitpk5nV9p8vdc6lhex/lD9CvF7Sk5K6S5Jz7mfn3AfOuT+dc0mSHve3P4xz7jPn3Ern3EHn3Fp5XwBkLTvSObfTf52Ffv0k6UZJo51znzjPz865dfKC81jn3BDn3AG/XS9JutLfroKkLdlUZ4u897lcyLJBzrm9zrn9oQXNrJr/OgP911gmaXp2bSwMEhMTFF+xYvrz+PiKSkxIyFRm587fVapU6fQ/bPHxFZWYmLkMwut4++Votp/z/ixd3qpN+vNap52uRQu9P5zz5s7R1q3ZHUoAkHe2JSUqLj7j3BYbH39YULAtKSFzmbh4bUtM0OZNG1W2bDk9MvhB3dCji0YNG6j9+70vT+68p7+ee3qMOrduov899Zhuvr1XrrSnKEhKSlBcyN+juLiKSkrM3GdJiYmKz9JnSUne36wnHxupO+7uq4iInC+XZ06brAsvahDmmhctQV13xMfH67qe/1GLpo3U9LJLVKpkSV108SW50CIgs78TcId+jbdP3gj1KZL6+CncO81sp7wR6Ep+ucWSLpN0qf//RfIC4Ib+81AbQv6/7tA+zCzOzMab2SYz2y3pbXlB7mHM7HQzm2lmW/2yI7Ipm1075Nf7Fx3uFEmVsrRxgKR4f/02SSdns93JktIk/Z5DG0NVkrTDObfvKMrKzG42s0/N7NNXXnoxp2L5l3OHLcqa6pFNkTxLBykyjrdf/mL7lAMHtHjhAjVvcXn6ssFDh2v8O+N0ZddO2rdvr6Kjix22DwDIS+6ozo3Zl0lNPaifflijDl2u0Ctj31NMTAmNfd2be2TapAm6457+mjRrvu7ofa9GDR0YTAOKoOz747BSh5eRadmSRSp30kmqXbdejvt//eXnFRkVqRat2h5nTYu4gK47du/apYUL5mv2vPn6YOFS7d+/XzNnTAtLlfHXIvLpIy+EKxd3g6ThzrnhOaxfLKmtvDT0EZJ2SuohL937v1nKVpV0aLaDapI2+/9/RN5Z8Uzn3HYz65DNtoc8J+kLSd2dc8lm1ktSl2Noy6k5LP/NOXdaDtt9KKmrpNeyLO8m797ufSEnj2xOG5K80fCTzOyEkKC7ak4Vdc69KOlFSfrjYI77zFfGjxurye95t/DXO6O+ErZmfO+RkLA1PdXnkHLlyik5ebcOHjyoqKgorwzpdmEXzn6Ji694xO2XLVui2nXrqXyFjO/AatQ8VS+89KokL718yeJFYW8jAByP2Lh4JSZknNuSEhJUoUJsljIVM5dJTFD52DiZTLFx8ap7xpmSpMuaNNfYN7y5KubMnK67+twvSWrUtIVGD3846KYUGXFxFZUY8vcoMXHrYSn7sXHxSsjSZxVi47Rg/lwtXbxQHy1bogMH/tTevXs16IF7NWj4aEnSrBlTtXzpYj3z/KsMBPwNuXHdsXLlR6pcpYpOOukkSVKTps311RdfqE3b9kE3D8gkXIH+S5JuNbPzzXOimbU2s1L++sWSGkkq4ZzbKGmpvPvAy8sLjEP1M7NyZlZV0t2SJvjLS0naI2mnmVWW1O8I9SklabekPX7q+m1HKJvVy5L6mtm//bbUMrNT5N1XvdvM+ptZCTOLNLMzzOzQbAuDJV1kZsPN7CQzK2Vmd8q7F7z/0bywn7r+qaRBZlbMvNnNC9XXplde1SN94qxGTZpqxvSpcs7p66++VMmSpQ4Lps1M5553vj6YN1eSNH3aFDVq3Dgvql6ohbNfLmvU+Ijbvz97llq2ap1pf9u3b5ckpaWl6aUXnlPXK64UAOQnteueoY3r12vzpo1KSUnR/A/e18WXNspU5pJLL9PcWdPlnNPqb77SiSVLqkKFWJWvUEFx8RW1fu1vkqTPPlmZPhFX+dhYffn5J5Kkzz9ZpSpVT8ndhhVideqdoQ0b1vl9dkAfzn1fDRpm7rMGDRvr/ZnT5JzTt19/pRNLllKF2Fj93533aPqchZoy60MNfWSM/n3O+enB9orlS/X26y9r9JPPKqZEibxoWoGXG9cdFU+upK+/+kr79++Xc06rVq5QjVOzG1MDghWWEW7n3KdmdpO8EefTJO2XtEzSEn/9j2a2R16gLefcbjP7VVJSNr8/PU3SZ5LKSHpd0qHfexos6U1JuyT9LG/27945VKmvvJHfe+UF9BMkHVWU5px718zKy5u8rLKktZKucc6tM7O2ksZI+k1ScUk/SHrQ3+4nM7tE0kh/mwh5wXML59zyo3ltXw957d4uL8ifICnyGLYvMBpc2lDLlixWm5bNFBNTQkOGjUhfd/utN+nhIcMUFxevXvf00719e+vZp59U7Tp11LFzV0nStqQkdb+is/bu2aOIiAi9/dYbmjJ9tkqWLJnTS+IoHG+/HGn7/fv3a+VHH+mhh4dkes05s2dq/DvjJElNmjZTh46ZJ6hB+L3xSE81+PdpqlC2pH6eM1RDn5+tN6auyOtq4Qjos7wVFRWlXvcOUN+7blFaaqpateuoGqfW0rRJ3rhA+85X6IKLL9WK5UvVvWNLFY8pofsHZsxSfnffARo6sL9SUlJUqXLV9HX3PjBYT48ZqdTUgypWrLj6DWCEO1yioqLUp/8D6nX7TUpLS1Obdh1V89TTNPm98ZKkTl2u1EWXXKqPli1R1/aXq3hMjB4clFOyZoYxo4YpJSVFd9/m/UpKvfpnqf8Dg4JsSqEW1HXHmWeepWbNW+jKrh0VGRml2nXqqEvXKyRJ8z/8QCNHDNXvO3bojv+7Rf/4Rx09/xI/MRtOZH5ksOzub0H+YWYTJH3vnDviX+CCklIOFFTlzr0jr6sAFGprFz+R11XAMYiOzKu7IfF3nVC8UI7fFFoxUSrQEetdU7/Pl7HJ0x1q5/r7ytkynzGzc837zfEIM7tc3k+jTc3jagEAAAAAjhE/YJz/VJQ0Wd797Rsl3eacy3qfOwAAAADkSxEFenw+vAi48xnn3Ax5v28OAAAAACjASCkHAAAAACAAjHADAAAAAMKGlPIMjHADAAAAABAAAm4AAAAAAAJASjkAAAAAIGzMyCk/hBFuAAAAAAACQMANAAAAAEAASCkHAAAAAIQNs5RnYIQbAAAAAIAAEHADAAAAABAAUsoBAAAAAGHDJOUZGOEGAAAAACAABNwAAAAAAASAlHIAAAAAQNhEkFOejhFuAAAAAAACQMANAAAAAEAASCkHAAAAAIQNo7oZeC8AAAAAAAgAATcAAAAAAAEgpRwAAAAAEDZMUp6BEW4AAAAAAAJAwA0AAAAAQABIKQcAAAAAhE0EOeXpGOEGAAAAACAABNwAAAAAAASAlHIAAAAAQNiQUZ6BEW4AAAAAAAJAwA0AAAAAQABIKQcAAAAAhE0EKeXpGOEGAAAAACAABNwAAAAAAASAlHIAAAAAQNhEME15Oka4AQAAAAAIAAE3AAAAAAABIKUcAAAAABA2ZJRnYIQbAAAAAIAAEHADAAAAABAAUsoBAAAAAGETQUp5Oka4AQAAAAAIAAE3AAAAAAABIKUcAAAAABA2JnLKD2GEGwAAAACAABBwAwAAAAAQAFLKAQAAAABhwyzlGRjhBgAAAAAgAATcAAAAAAAEgJRyAAAAAEDYkFKegYC7kPgzJS2vq4BjtPzXbXldBRyDtYufyOsqAIVa9Ya987oKOBbRMXldAxyjHcsfy+sqAEUSKeUAAAAAAASAEW4AAAAAQNiYkVN+CCPcAAAAAAAEgIAbAAAAAIAAkFIOAAAAAAgbZinPwAg3AAAAAAABIOAGAAAAACAApJQDAAAAAMKGScozMMINAAAAAEAACLgBAAAAAAgAKeUAAAAAgLCJIKc8HSPcAAAAAAAEgIAbAAAAAFDkmdnlZvaDmf1sZvcdody5ZpZqZl3+ap+klAMAAAAAwiaiAGaUm1mkpGclNZO0UdInZjbdOfddNuVGSZp7NPtlhBsAAAAAUNSdJ+ln59yvzrkDksZLap9NuTslTZKUeDQ7JeAGAAAAABR1lSVtCHm+0V+WzswqS+oo6fmj3Skp5QAAAACAsMmvk5Sb2c2Sbg5Z9KJz7sVDq7PZxGV5/qSk/s65VDvKRhJwAwAAAAAKPT+4fjGH1RslVQ15XkXS5ixlzpE03g+2K0hqZWYHnXNTc3pNAm4AAAAAQFH3iaTTzKyGpE2SrpR0VWgB51yNQ/83s9clzTxSsC0RcAMAAAAAwigi2+zs/M05d9DM7pA3+3ikpFedc6vN7FZ//VHftx2KgBsAAAAAUOQ552ZLmp1lWbaBtnOu59Hsk1nKAQAAAAAIACPcAAAAAICwya+zlOcFRrgBAAAAAAgAATcAAAAAAAEgpRwAAAAAEDYRpJSnY4QbAAAAAIAAEHADAAAAABAAUsoBAAAAAGETwTTl6RjhBgAAAAAgAIxwAwAAAADChgHuDIxwAwAAAAAQAAJuAAAAAAACQEo5AAAAACBsmDQtAyPcAAAAAAAEgIAbAAAAAIAAkFIOAAAAAAgbMsozMMINAAAAAEAACLgBAAAAAAgAKeUAAAAAgLBhVDcD7wUAAAAAAAEg4AYAAAAAIACklAMAAAAAwsaYpjwdI9wAAAAAAASAgBsAAAAAgACQUg4AAAAACBsSyjMwwg0AAAAAQAACCbjN7HUzG3aE9c7Mah1N2b94nUFm9vbfrScAAAAAAEEhpfw4mFl1Sb9J2usv2ivpE0lPOec+CNO+o51zB49nX/nJiuVLNWb0CKWlpal9xy667j83ZVrvnNOY0SP00bIliomJ0cAhI1S7Tj0lbN2iQQ/ep+3bt8nM1LFzN13Z41pJ0o/fr9HI4YP0558HFBkVqf73D1S9+mfmRfMKtTWfr9KUV5+SS0vT+U3bqGmnqzOt/2zxPM2fOlaSVDzmBHW5uY8q16glSVo8812t/GCGnJwubNpWDdt2y/X6FxWrPlqmp8eMVFpaqlq376yre96Yab1zTk+PeUQrly9V8ZgY3f/wcP2jdl1JUnLybo0e9rB+++VnyaT7HhqqM848W5I0acJYTZ74jiIjI3XhJZfqtrv65HbTCqUg+uunH77XmJFDdODPPxUZFane/R9S3Xr186J5RdrzD/dQy0vPUNKOZJ3TdUReVweSml3wDz3Wp70iIyL0+rRVeuzNhZnWly1VQi881E01KpfXnwcO6pahE/Xdr1slSXd2b6Ce7c+Xc9Lqn7fo5qET9OeBQnN5lqeccxr9yHAtW7pYMTExGjJ8pOrUrXdYuU0bN6h/v3u0a9cu1alTV8NHjlZ0dLEjbt+yeWOdeOKJioiIUFRkpMZNnCxJ+uH77zV86MPat2+fKlWqrBGjHlPJkiVztd2FXQSzlKcjpTw8yjrnSko6S9IHkqaYWc+8rVL+k5qaqtGPDNVTz76oCZNnaO6cWfr1l58zlflo2RJtWL9Ok6bP0f0PDdao4UMkSZGRkbq7z72aOGWWXn1rgt6dMC5922eefEw33nK7xk6coltuu1PPPPlYrretsEtLTdWklx7XzQ8+pv5PvaUvln6orRt+y1TmpPiTdcfQ/+reJ95Q867XaeLzoyVJW9b9qpUfzFDv0S+q3+OvafVnHylp84a8aEahl5qaqidGD9OjTz2nNydO1/x5s7X2118ylVn50VJtXL9e4ybPVr8Bg/T4yKHp654eM1LnX3ix3n5vhl4bN1mn1KgpSfr804+1bPFCvfbOZL05cZquvLpnbjar0Aqqv557Zox63nibXh03Sf+55Q49//SYXG0XPG/NWKn2tz+b19WALyLC9OS9HdX+7pf1zyseVdcW/1TtGvGZytzbs4m++nGzzuvxuG4Y9I4e69NeklQptrT+74oGuvi6J3VO98cUGRmhrs3OzoNWFE7Lli7R+vVrNX32PD00aKiGDx2Ubbknn3hMV1/TUzNmz1Pp0qU1ZdJ7R7X9S6++oYmTpqUH25I0+OEHdFevPnpvygw1btJUb7z2clDNA44v4DazOma2yMx2mtlqM2uXQ7l+ZrbFzDab2X+yKVLBzD4ws2QzW2xmp4Rs+5SZbTCz3Wb2mZk1OEJ93jWzrWa2y8yWmFm9kHWvm9mzZjbLf51VZnZqyPp6fh12mFmCmQ3wl0eY2X1m9ouZbTeziWZ2Unav75zb6px7StIgSaPMLMLfRyUzm2RmSWb2m5ndFfK655nZp377EszscX/VEv/fnWa2x8wuzKndBcXqb79WlarVVLlKVUVHF1PzFq20ZNGCTGWWLFqgVm3ay8xU/8yzlZy8W9uSElUhNk6163jdeeKJJ6pGzVOVlJjgbWSmvXv3SJL27NmjCrFxudquomD9z2tU4eTKqlCxkqKio/XPS5ro24+XZSpTo3Z9nVCylCTplNPradf2JElSwqZ1OuX0uipWPEaRkVGqVfdsfb1qyWGvgeO3ZvU3qly1mipVqaro6Gg1adZSyxZnPsaWLV6oFq3bycxUr/5Z2pOcrG3bkrR3zx599cVnat2+syQpOjpapUqVliRNmzRBPa67QcWKFZMklTupfO42rJAKqr8s5Jy4l3Ninln++S/asWtfXlcDvnPrVdMvG7dr7eYdSjmYqnfnfak2l2YeRa1dI16LPvlJkvTjuiSdcnI5xZ3kjXpGRUaoRPFoRUZGqERMtLZs253rbSisFi2crzbtOsjMdOZZ3rVfUlJipjLOOX2yaqWaNm8hSWrbvqMWLph/1NtntW7tb/r3OedKki648GLN/2BeAC0DPH874DazaEkzJM2TFCfpTkljzewfWcpdLqmvpGaSTpPUNJvd9ZA0VFIFSV9KGhuy7hNJZ0s6SdI4Se+aWUwO1Xrff404SZ9n2Y8kdZc0WFI5ST9LGu7XsZSkDyXNkVRJUi1J8/1t7pLUQVJDf93vkv7qK+vJfh3+4QfdMyR9JamypCaSeplZC7/sU/JS0EtLOlXSRH/5pf6/ZZ1zJZ1zK/7iNfO9pMRExVesmP48Lj4+I2j2JSYmZClTUYmJmU+amzdt0g/fr1G9+mdJku7pd7+efuIxtWnRSE8/Plq339U7wFYUTTu3J6ls+YyL9jLlY7Vrx7Ycy6/6cKZq//N8SdLJ1Wro1+++0t7kXTrw5x/67vOV2rntyH8I8fdsS0pUXHzG8RMbH3/YRce2pITMZeLitS0xQZs3bVTZsuX0yOAHdUOPLho1bKD27/eChQ3r1urrLz/TLT27686be2rN6m9yp0GFXFD9dec9/fXc02PUuXUT/e+px3Tz7b1ypT1AflYptow2JuxMf74pcacqx5bJVOabnzarfSPv9otz6lZVtYrlVDmujDYn7daTby/Sj9Mf1G+zB2r3nj80f9WPuVn9Qi0xIUEVQ6794uMrKjEh8/Xhzp2/q1Sp0oqKisoo419DHml7M+m2m29Q926d9N67E9LLnFrrdC1a6F3qfzBvjrZu3RJM44owy6ePvHA8I9wXSCopaaRz7oBzboGkmfKC2lDdJL3mnPvWObdX3uhvVrOcc0ucc39KekDShWZWVZKcc28757Y75w4658ZIKi7pH9nsQ865V51zyf5+Bkk6y8xCz6aTnXMf+/dEj5UXyEtSG0lbnXNjnHN/+PtY5a+7RdIDzrmNIfvtYmZHuv99s//vSZLOlRTrnBviv0+/SnpJ0pV+mRRJtcysgnNuj3Nu5RH2W6A55w5fmPX+jmzKWEiZffv26r6+d+mefvel32sz6d3x6t33Ps2cu1C9+t6nYYMfDGu9cWx++uZzrZw/S22vvU2SFF+luhp37KHnBvXWC0P7qlL1WoqIjMzjWhZO2R1jluUYy6lMaupB/fTDGnXocoVeGfueYmJKaOzrr0jyUp+Tk3fr+dfG6ba7++jhAX2zP55xTILqr2mTJuiOe/pr0qz5uqP3vRo1dGAwDQAKkOxuJ3XKfHw99uYClS11gla+3Vu3dbtEX/24WQdT01S2VAm1aXiG6nQYoZqthujEEsV05eX/yqWaF35Hdy48fLtDZY60/etvvaPx707Rs8+9pInvjNVnn34iSRo8dLgmvDNO3bt10t69exUdXex4mwHk6HgC7kqSNjjn0kKWrZM3intYuSxlskpf75zbI2mHv53MrI+ZrfHTxHdKKiNvJDwTM4s0s5F+6vduSWv9VaFlt4b8f5+8LwwkqaqkzDfOZThF3j3ZO/3XXyMpVVJ8DuWljPdgh799pUPb+/sYELL9DZJOl/S9mX1iZm2OsN9MzOxmPx3909dfefFoN8szcfHxStia0QWJCQmKzZLqGBdfMUuZrYqNjZUkHUxJUf8+d6tFq7Zq1KR5eplZM6aqUZNmkqSmzS/Xd98y+hZuZcvHauf2jJG3XduTVOakww5DbV77syb8b5RuuH+ETiyV8V3XBU3bqO+YV3XnsP/qxJKlFHty1Vypd1ETGxevxISM4ycpIUEVKsRmKVMxc5nEBJWPjVNsXEXFxsWr7hnehIOXNWmuH3/4Ln2/lzZqKjNT3Xr1FWGmXTt/z4UWFW5B9decmdPVsJGXTNaoaQut+Y5zIrApcZeqxJdNf145rqw2J2VOC0/e+6duGTpBF1z9hG4Y9I4qlD1RazfvUOPzTtPazdu1bedeHUxN09SF3+iCM6vnbgMKmfHvjFW3zu3VrXN7xcbFaWvItV9CwlbFxmW+PixXrpySk3fr4MGDGWX8a8j4ihVz3D4uzrvcPql8eTVq0kzffvO1JKlGzVP1/Euv6p2Jk9WyVWtVqcp1CYJzPAH3ZklVD92n7KsmaVOWclvkBbShZbJKX29mJeWNDG/279fuL2+UvJxzrqykXco+I+AqSe3lpayXkVT90C6Poi0b5KVz57SupXOubMgjxjmXtZ2hOkpKlPSDv/1vWbYv5ZxrJUnOuZ+cc93lpaCPkvSemZ0o6S+Hj5xzLzrnznHOndPzhpuPopl5q269+tqwfp02bdqolJQDmjd3tho0bJSpTIOGjTR75jQ55/TN11+qZMlSqhAbJ+echg5+UDVq1FSPa3pm2iY2Nk6f+99YfvLxSlWtdooQXlVr1VbSlo3anrBZB1NS9MWy+ap37iWZyvyelKDXRj+oHnc/qLhKmQ/zZD84+z0pQV+vWqJ/NcjuzhIcr9p1z9DG9eu1edNGpaSkaP4H7+viSzMfY5dcepnmzpou55xWf/OVTixZUhUqxKp8hQqKi6+o9Wu9yfA++2SlqtfwTosNLmuszz/5WJKXXp6SkqIyZcvlbuMKoaD6q3xsrL783Dsnfv7JKlWpyjkR+PS7DapVtYJOqXSSoqMi1bX52Zq1dHWmMmVKxig6ysvAur79+Vr25a9K3vunNmzdqfPOOEUlikdLkhqde5p+WJtw2Gvg6F3ZvYcmTpqmiZOmqVHjppo5faqcc/r6K+/aL+uAjJnpnPPO14fz5kqSZkybossaN5YkNbyscbbb79+3L30+i/379mnFR8tV67TTJEk7tm+XJKWlpemlF55T125XCuFllj8feeF4fhZslbyfwbrXzMZIulhSW3kp1PeFlJso6TUze1PeqPPD2eyrlZldIuljefdyr3LObTCz+pIOSkqSFGVm90kqnUN9Skn6U9J2SSdIOpbf4Jgp6XEz6yXpOUnFJNX108qflzTczK5zzq0zs1hJFznnpmXdiZnFS+rqt/Fu51yamX0sabeZ9Zf0tKQDkupIKuGc+8TMrpY01zmX5I9+S94IepKkNEk1JRWKG4WioqLU774HdddtNyotLU1t23fSqbVO06R3x0uSOne9Uhc3aKiPli1Rp7YtFBMTo4cGe9341Zef6/2Z01XrtNPVo1tHSdL/3dlLFzdoqAEDh+jx0SN0MDVVxYsV1/0PDcmzNhZWkZFR6nxjb70wpI/S0tJ0fpPWOrlaDS2fO1WSdHGLDpo78TXtTd6l91705v2LiIxUn0e9WT9fe/RB7Uve5e3npt7pk6shvKKiotTr3gHqe9ctSktNVat2HVXj1FqaNsm7b6195yt0wcWXasXyperesaWKx5TQ/QMzZr2+u+8ADR3YXykpKapUuWr6ulbtOmnkkAd13RUdFBUdrQGDRhyW7odjF1R/3fvAYD09ZqRSUw+qWLHi6jcguz+7CNobj/RUg3+fpgplS+rnOUM19PnZemNqgZ+OpcBKTU1T70enaMbTNykywvTGjE+05tcE3djJm5P25ckrVLtGvF5++Eqlpjl9/1uCbh3mTavzyer1mjL/a614q7cOpqbpqx826ZUphfYOwFzX4NKGWrZ0sdq2bKaYEiU0eGjGJfztt92khwcPU1xcvHr17qf+/Xrr2Wee1D/q1FHHTl2PuP327dt1z923S5IOpqaqZas2uvgSb4qk92fP1ITx4yRJTZo2U/uOnXOzyShi7Hjuw/NnAf+fvHuhN8m713mKmb0uaaNz7kG/3H2SeskLIB+U9Iqk05xzP/tl/5A3wnyhvMnOrnPO/WZmkZJelBfE7pX0hKT/k3Sjc+5DMxskqZZz7mp/ZHyspMbyUrkfkvRGltcJrdNlkt52zlXxn58hbwKzf8kL3J90zo30R/B7ybuXu5K8kesJzrkBlvl3uM3/91NJTzvn5oS8T5UkjZHUSN496D9IetBvw9uSmsv7kmCd/x5O9bcbIuk2SdGSLj/S/d279qdxQ2UBs/zXnCcdQ/7z76qM6AJBqt6QCS8LlOic5q9FfrVjOT+bWpCUiM6zOb7CYtznG/NlbHLVv6rk+vt6XAE38g8C7oKHgLtgIeAGgkXAXcAQcBc4BNwFS0EPuN/5YlO+jE26/7Nyrr+vx/U73AAAAAAAIHsE3AAAAAAABOB4Jk0DAAAAACATRnUz8F4AAAAAABAAAm4AAAAAAAJASjkAAAAAIGzMCvQk62HFCDcAAAAAAAEg4AYAAAAAIACklAMAAAAAwoaE8gyMcAMAAAAAEAACbgAAAAAAAkBKOQAAAAAgbJilPAMj3AAAAAAABICAGwAAAACAAJBSDgAAAAAIG0Z1M/BeAAAAAAAQAAJuAAAAAAACQEo5AAAAACBsmKU8AyPcAAAAAAAEgIAbAAAAAIAAkFIOAAAAAAgbEsozMMINAAAAAEAACLgBAAAAAAgAKeUAAAAAgLBhkvIMjHADAAAAABAAAm4AAAAAAAJASjkAAAAAIGwimKc8HSPcAAAAAAAEgIAbAAAAAIAAkFIOAAAAAAgbZinPwAg3AAAAAAABIOAGAAAAACAApJQDAAAAAMLGmKU8HSPcAAAAAAAEgIAbAAAAAIAAkFIOAAAAAAgbZinPwAg3AAAAAAABIOAGAAAAACAApJQDAAAAAMImglnK0zHCDQAAAABAAAi4AQAAAAAIACnlAAAAAICwYZbyDIxwAwAAAAAQAAJuAAAAAAACQEo5AAAAACBsSCnPwAg3AAAAAAABIOAGAAAAACAApJQDAAAAAMLGRE75IYxwAwAAAAAQAEa4gTyyfP3OvK4CjsEF1cvndRWAwi06Jq9rgGOR8kde1wDHyMnldRVwTBghLiwIuAEAAAAAYRPB9wXpSCkHAAAAACAABNwAAAAAAASAlHIAAAAAQNgwS3kGRrgBAAAAAAgAATcAAAAAAAEgpRwAAAAAEDZGRnk6RrgBAAAAAAgAATcAAAAAAAEgpRwAAAAAEDbMUp6BEW4AAAAAAAJAwA0AAAAAQABIKQcAAAAAhE0EGeXpGOEGAAAAACAABNwAAAAAAASAlHIAAAAAQNgwS3kGRrgBAAAAAAgAATcAAAAAAAEgpRwAAAAAEDZGRnk6RrgBAAAAAAgAATcAAAAAAAEgpRwAAAAAEDZklGdghBsAAAAAgAAQcAMAAAAAEABSygEAAAAAYRPBNOXpGOEGAAAAACAABNwAAAAAAASAlHIAAAAAQNiQUJ6BEW4AAAAAAAJAwA0AAAAAQABIKQcAAAAAhA855ekY4QYAAAAAIACMcAMAAAAAwsYY4k7HCDcAAAAAAAEg4AYAAAAAIACklAMAAAAAwsbIKE/HCDcAAAAAAAEg4AYAAAAAIACklAMAAAAAwoaM8gyMcAMAAAAAEAACbgAAAAAAAkBKOQAAAAAgfMgpT8cINwAAAAAAASDgBgAAAAAgAKSUAwAAAADCxsgpT8cINwAAAAAAASDgBgAAAAAgAKSUAwAAAADCxsgoT5fnI9xm5sys1t/cdq2ZNQ13nQAAAAAAOF55HnAXZGY2yMxSzCzZf/xoZv81s5PDtO+3w1HP/GTF8qXq0r6lOrVtoTdefemw9c45PTZquDq1baGrurbX92tWS5IStm7RbTdep24dW+uKTm00fuybmbab8M7b6tK+pa7o1EZPP/ForrSlqNm65jPNGX6r3h92s77/8N0cy+1Y/6Pe691eG79cnr7swL49WvHaI5o74lbNHXGbtv/2fW5UuUhasXyprujYSl3atdCbr2V/jD0+eri6tGuhq7t10A9rvsu0PjU1Vdd276Q+d92WvuyZJx7VFZ1a6+puHdS/z51KTt4deDuKCvqrYGt2wT/01bv36ttJ96nvtY0OW1+2VAlNGH2dPh57j5a+dpfq1qyYvu7O7g302fi++vSdvnpjaA8VL0bSYV57/uEeWjf/EX367oC8rkqht3zZUnVoc7natWyuV19+8bD1zjmNGjFM7Vo2V7eO7bTmu9V/ue2uXTt1643/UbtWLXTrjf/R7l27JEmbN23UBf8+S1d07qArOnfQsMEPH/Z6d99xm7p0aBtASwEC7nCY4JwrJekkSR0lVZT0WTiC7sImNTVVox8ZqqeefVETJs/Q3Dmz9OsvP2cq89GyJdqwfp0mTZ+j+x8arFHD/7+9+w6zqrgfP/4edkFQsAC7i1IsSCzYkliwIE1q6Api1G809phEjTVWEKwx/ozGaMTeQToIglKkWIINEaOJRqlhdwWRIuqyO78/7mUbS4v3siy8Xz77PHvOmTl35o7n7P2c+dzhVgAyMjK47MprGDryZR5/ZggvDXm+uO47s99m+rTJPP/SaIaMGMdZv/r1Nu/bji4WFfL+sIc56aL+dLzuQRa+N52VSxdUWG7u2KdocPBPy+yfM3IwDQ7+GR2vf5j219xPnZxG26rpO5XCwkL+fNcg7n3g77wwfCyvvjKeL/5T9hp7c1biGntp9Ctcd+MA7r5jQJnjQ194hv32b1pm37EtTuC5oaN5dugomjTZj6creFimred4VW3VqgXuu6YXPS57lJ+e/if6dPwpB++fU6bMNee0Y86/lnDsmfdyXv8XuOfKHgDsk7U7vzm9JSf+6j6OPuMeMjKq0af9UZXQC5X2zNi36HHpg5XdjB1eYWEhdw66lb8+NJjhY8bxyviX+bzc58GZM6azYMF8Ro+fyI39b+X2gQM2W/eJRwdzbIsWjBk/kWNbtOCJx0rufY0aN2HI8FEMGT6KG28pex+d/Ookdt111zT3eucTttOfyrDZgDuZtn1VCOHDEMI3IYQhIYSayWNdQwgfhBBWhBDeCCEckdx/bghhbKlzfBZCGFpqe2EI4ahSL9MlhPCfEMJXIYQ/hRCqJcs1DSFMCSEsSx57LoSw50baeWwI4c1kW/6bnGmuUep4DCFcHEL4dwjh6xDCgyGUfLsghHBBCOGfyZnqj0MIP0vu3yeEMDyEkB9C+CKE8PuKXj/GWBBjnAecDuQDV5Y6d4XvU/LYtSGExcnX/TSE0C6E0Am4Hjg9hLA6hDBnc+NUFcz76EMaNW5Cw0aNqV69Bh06dmH6tCllykyfNoUuXXsQQuDwI45i1aqVfJWfR/2sbA4+pDkAu+22G/sf0JT8vFwAhg99kV+dewE1aiSGu27detu2YzuB5fP/Te36e1O7fgOqZVan8U9PZsnctzco99n0cTQ84gR2qb1H8b6C774l//OP2K9FBwCqZVanxq61t1nbdyYffzSXRo1KrrFTOnau8BrrnLzGDjviSFavWsVX+fkA5OUuZdaM1+ne89QydY47/kQyMxOzb80PP5K8vKXbpkM7OMerajumeRM+X7SML5csp2BdIS9N+oCuJzcvU+bg/XOYNvvfAPxrfj777r0X2XUT97/MjGrU2qU6GRnVqFWzOv/9ykyEyjbrvc9Z/s23ld2MHd5Hcz+kcZMmNGqcuPd17NyFaVMmlynz+tTJdO2euPcdcWTi82B+ft4m606bOpluPXoC0K1HT6ZOeW2zbfn22zU8+/STnH/RJZstK/2vtnSGuy/QCdgfOAI4JxmQPg5cBNQD/g6MCSHsArwOtAwhVEvO9FYHTgQIIRwA1AY+LHX+XsDRwM+AHsD6KcoA3AHsAxwCNAb6b6SNhcAVQH3geKAd8JtyZboCxwBHJvvUMdmmPsnz/h+wO9AdWJYM/McCc4CGyXNeHkLouLE3KsZYCIwGWibPvdH3KYRwEPBb4JjkLHlH4MsY4yvA7SRmz2vHGI/c2OtVJfl5eeQ0KEmny87JKQ6a18vLyy1XpgF5eXllyixZvJhPP/knzQ9PvC0L5n/JB++9y7lnnc5F553Nxx/NTWMvdk5rv1lGrb3qF2/X2rMea79ZVrbMimUsnvsmTU/sVGb/mq+WskvtPXjn+ft47U+X8c6L97Pu+++2Sbt3Nvn5uWSXvn6yG5Bf7vrJz8sjJ6ekTFZ2Dvn5ievwvnvu5LeXXUW1ahv/0zBu9AiOP6Flilu+c3K8qrZ9svZgUe6K4u3FeStomLVHmTJz/72EHm0OB+DoQxvTpMFeNMzegyX5K7nv2Wn8a8yNfDH+Zlau/o7Jb/9rWzZfqjSJz3oliaA5OQ02/DyYm0uDcmXycnM3WXfZsmVkZWUDkJWVzfLly4vLLV68iH6n9eK8c87ivXffKd7/twfu5+xfnUutmjVT20mplC0NuO+PMS6JMS4nEYAeBVwA/D3G+HaMsTDG+BTwPdAixvgfYFWyXCtgIrA4hHBwcntGjLGo1PnvijEujzEuAO4DzgCIMX4WY3w1xvh9jDEfuDdZfwMxxndjjG/FGNfFGL8kEdiWL3tnjHFF8nWmJtsHcD5wd4xxdkz4LMY4n0RwnhVjvDXG+EOyX4OBfpt5v5aQSDFnU+8TiYcEuwCHhhCqxxi/jDF+vplzV1kxxg13ll/CsIIypRIR+PbbNVx31e/5w9XXUbt2YpagsHAdK1et5PFnXuT3l1/NH6+5ouLX0o+w+bH7YORgDu92DqFaRpn9RUWFrFj0OQec2IVTrv4LmTVq8snkYels7E6rov/vN1wltIIyBGZOn8Zedety8KHNNzi+3pOPPkxGZgYdu/g9t1RwvKq2ilbgjeXG656np7BnnV1569kruKTvScz51xLWFRaxZ51adG11GIf0vJ0DutzKbrVq0K/Tz7ZRy6VKVtFHtHIXVMUfGcMW1S2vflY2E16dwovDRnLl1ddx/TVXsXr1aj795J8sXDCftqe03/K2a8tVdu74dpRTvqUrdJTOR/uWxIxzXeBXIYTflTpWI3kMErPcrYEDk7+vIBEAH5/cLm1hqd/nrz9HCCEbuJ/EbHEdEg8Ivq6ogSGEn5AIyI8Gdk327d3N9GN9XmtjoKJAd19gnxDCilL7MoAZFbWhlIbA+sdq+7KR9ynG+HoI4XISs+vNQwgTgT/EGJds5vwAhBAuBC4EuO+BhzjnvAu3pFqlyc7JIXdpyRDk5eYWP4ksKdOgXJmlZGVlAbCuoIBrr7yMjl260aZdhzJ12rRtTwiB5ocfQbVq1Vjx9dfsVbcuSo1ae9Rn7ddfFW+vXbGMWruXfX+/Xvhv3n4qsWDd92tWsvSf7xKqVaPefgdTa4/61NvvIAAaHnkinxpwp0V2dgPySl8/eUupX+4ay8rOITe3pEx+Xi71s7KZMnkiM16fyhszp/PDD9+zZs0a+t9wDf1vuxuAl8eOYtaM13ng4cfLPATT/87xqtoW531Do5w9i7cbZu/JkvyyaeGr1nzPRQOHFG9/Mup6vlyynPYtDuLLJcv4asUaAEZNnUuLI/bjxVfe2yZtlypT4vPgf4u3c3OXbvB5MKdBDkvLl8nOpqCgYKN169WrR35+HllZ2eTn51E3+TmwRo0axV87PLT5YTRq3Jj5X37BvI8+4uOP59GlQ1sKCwtZvmw5559zNo8++Uza+q6d049ZNG0hcFuMcc9SP7vGGF9IHl8fcLdM/v46iYC7FRsG3I1L/d6ExAwxJNLJI3BEjHF34Cw2/mziIeAToFmy7PWbKFtRX5puZP8X5fpYJ8bYZWMnSqahd6MkKN/k+xRjfD7GeBKJwDwCdyXrbXaKNsb4SIzx6Bjj0dt7sA1waPPDWbhgPosXL6Kg4AcmTRxPy1ZlV3Vt2aoN48eNJsbI3A8/oHbtOtTPyibGyMABN7L//gdw5tnnlKnTqk073pn9FgDz539BQUEBe+6117bq1k5hrybNWP3VEtYsW0rRugIWvj+dvQ87tkyZLjc/RpdbEj+NjjyBn552CQ2POJ6au+9Frb3qsyp3EQB5/5rD7jmNK3oZ/UiHND+MhQvnsyR5jb02cUIF11hbJiSvsY8+nMNutetQPyuL3/zuD4x5ZSojX36NgXf8mZ8ffVxx8PbmrBk8++Sj3H3fg9SsVasyurZDcryqtnc+XsiBjeuz7z51qZ6ZQZ8OR/HyjHllyuxRuybVMxNZP+f2OI6ZH/yHVWu+Z+HSFRx72L7U2qU6AG2OacanX+Zu8BrSjqj5YYezYMF8Fi9K3PsmThhP6zZty5Rp1bot48Yk7n0fzkl8HszKyt5k3Vat2zJ29CgAxo4eRes27QBYvnw5hYWFACxauJAFC+bTqHFj+vY7g1enzmD8pCk88fRz7LvffgbbIoTQKbmu1mchhOsqOH5mcm2zD5Nrc232q78/5t+gGAyMDCG8BvyDxKxya2B6jHEViaD6XiA3xrgohLASeCb5mu+XO9fVIYS3Scw4X5asB4lZ7W+AFSGEhsDVm2hPHWAlsDqZun4JicXLtsSjwL0hhJnAeySC74Jkv1aGEK4lMdP+A4nvkteKMc4ufYIQQnUSs/n9SaxUvr4PG32fSMzkNwRmAd8Bayl5CJILtA8hVCuXfl9lZWZmcvV1N/L7S86nqKiIbj160/TAZgx/6UUATu3TjxNbtuKNmdPp3a0jNWvW5KYBtwMw54P3mDBuDAc2+wln9u0FwG9+dzkntmxF9569GXjLjfQ7tRvVq1fnloF3OKOTYtUyMjjq1IuZ8fAtxKIi9jvuFPbYe18+nzUBgKYndt5k/Z/2voh/PPtnitatY7d6ORz9y8u3Qat3PpmZmVx57Q1cfukFFBUV0bV7Lw5o2owRwxLXWO/T+nHCSSfzxszp9OnRiV1q1uTG/rdt9rx/vmsQBQUFXHbJeUBiIa5rb+ifzq7sFByvqq2wsIgr/jSSsfdfQEa1wFNjZ/PP/+Ryfu/jAXh0xJscvH8Oj97Sj8KiyCdf5HLxoMT6sbPnLWDk5A9585krWFdYxJxPF/PYyLcqszsCnrrjHFr+vBn196zNZ68MZODD43lq1JuV3awdTmZmJtdefxO/ueg8igqL6NHrVJoe2IyXhiTufX1O78dJJ7di5ozpdO/cgZq1atJ/4O2brAtw7vkXcO2VVzBqxHD23ntv7r73PgDee3c2D/31ATIyMsjIyOCGm/uzxx57VkbXdyqh0tYE/9+FEDKAB4H2wCJgdghhTIyx9L/J+QXQKsb4dQihM/AIcNwmz7u577qGEL4Ezo8xvpbc7g8cGGM8K7ma9kCgGYlgcSbw62TATQjhv8ArMcZzk9vvAPkxxs6lzh9JBNmXA3sATwLXxBgLQwjNgaeBg4DPSATsV8QYG5VvWwjh5GSHG5EI6KcCbZOzx+tfp1mM8bPk9pPAohjjjcnti0ksutYQ+BI4O8b4fghhH+DPQBsS37f+FLgx+Zr9gRtIfCc7kJiZf5XEjPbiUn2s8H0isQjdoySC+ALgDeDCGOOSEEI9EouvNScxy77JL3d9s7bILy1XMXdP+2zzhbTduPLkipJgJKVKwzbXVnYTtDUKXPyyqln2jwcquwnaCrtWr9qzR+/PX7VdxiY/3bfORt/XEMLxQP8Y4/qFtf8IEGO8YyPl9wI+ijE23NRrbjbgVtVgwF31GHBXLQbcUnoZcFcxBtxVjgF31WLAnR4/22/3i0iugZX0SIzxEYAQwmlApxjj+cnts4HjYoy/rehcIYSrgIPXl9+YH5NSLkmSJElSGdvr44JkcP3IRg5X1OoKHxyEENoA5wEnbe41DbglSZIkSTu7RZRdzLsRJYt5FwshHEHia8GdY4zLNnfSH7NKuSRJkiRJO4LZQLMQwv4hhBpAP2BM6QIhhCbACBLrff1rS07qDLckSZIkKWW204zyTYoxrgsh/BaYCGQAj8cY5yUX1ybG+DBwM1AP+FvyX0VaF2M8elPnNeCWJEmSJO30YozjgfHl9j1c6vfzgU0uklaeKeWSJEmSJKWBM9ySJEmSpNSpijnlaeIMtyRJkiRJaWDALUmSJElSGphSLkmSJElKmWBOeTFnuCVJkiRJSgMDbkmSJEmS0sCUckmSJElSygQzyos5wy1JkiRJUhoYcEuSJEmSlAamlEuSJEmSUsaM8hLOcEuSJEmSlAYG3JIkSZIkpYEp5ZIkSZKk1DGnvJgz3JIkSZIkpYEBtyRJkiRJaWBKuSRJkiQpZYI55cWc4ZYkSZIkKQ0MuCVJkiRJSgNTyiVJkiRJKRPMKC/mDLckSZIkSWlgwC1JkiRJUhqYUi5JkiRJShkzyks4wy1JkiRJUhoYcEuSJEmSlAamlEuSJEmSUsec8mLOcEuSJEmSlAYG3JIkSZIkpYEp5ZIkSZKklAnmlBdzhluSJEmSpDQw4JYkSZIkKQ1MKZckSZIkpUwwo7yYM9ySJEmSJKWBAbckSZIkSWlgSrkkSZIkKWXMKC/hDLckSZIkSWlgwC1JkiRJUhqYUi5JkiRJSh1zyos5wy1JkiRJUhoYcEuSJEmSlAamlEuSJEmSUiaYU17MGW5JkiRJktLAgFuSJEmSpDQwpVySJEmSlDLBjPJiIcZY2W1QCqxYW+hAVjFrfyis7CZoK9SpWb2ym6CtlJnhX/uqxI8jVUvEAatq6h37u8pugrbC2vf/WqX/iH2Wt3a7vEkcmF1rm7+vppRLkiRJkpQGppRLkiRJklKmSk/Pp5gz3JIkSZIkpYEBtyRJkiRJaWBKuSRJkiQpdcwpL+YMtyRJkiRJaWDALUmSJElSGphSLkmSJElKmWBOeTFnuCVJkiRJSgMDbkmSJEmS0sCUckmSJElSygQzyos5wy1JkiRJUhoYcEuSJEmSlAamlEuSJEmSUsaM8hLOcEuSJEmSlAYG3JIkSZIkpYEp5ZIkSZKk1DGnvJgz3JIkSZIkpYEBtyRJkiRJaWBKuSRJkiQpZYI55cWc4ZYkSZIkKQ0MuCVJkiRJSgNTyiVJkiRJKRPMKC/mDLckSZIkSWlgwC1JkiRJUhqYUi5JkiRJShkzyks4wy1JkiRJUhoYcEuSJEmSlAamlEuSJEmSUsZVyks4wy1JkiRJUhoYcEuSJEmSlAamlEuSJEmSUsic8vWc4ZYkSZIkKQ2c4ZYkSZIkpYyLppVwhluSJEmSpDQw4JYkSZIkKQ1MKZckSZIkpYwZ5SWc4ZYkSZIkKQ0MuCVJkiRJSgNTyiVJkiRJKeMq5SWc4ZYkSZIkKQ0MuCVJkiRJSgNTyiVJkiRJKRNcp7yYM9ySJEmSJKWBAbckSZIkSWlgSrkkSZIkKXXMKC/mDLckSZIkSWlgwA2EEOaFEFpXdjsqEkL4MoRwSmW3Q5IkSZK0dUwpB2KMzTd1PISwH/AFsKbU7s9jjEems12SJEmSVNWYUV7CGe6ts2eMsXbyZ4NgO4TgA4zNeHPWDPr06MKp3Try1OODNzgeY+TPd93Gqd06cmafnnzyz48B+P777zn3zNM5s28v+vXuxiN/e6C4zv33/om+PX/BmX16cs0Vv2PVypXbrD87urffnMnZp3Xjl7278NxTj25wPMbI/ffcwS97d+HXv+zNvz5JjNeC+V9w3pmnFf90adOCl154BoAB119VvP/0Hh0578zTtmmfdiZvzJxB726d6PGLDjzx2CMbHI8xcvedg+jxiw6cfmp3/vnxvOJjA26+nlNanUDfXt22ZZN3CjFG7rx9EF07tee0Xt3KvO+lLVq0kDP79aFb5w5cfeXlFPzww2brP/PUk/Tq/gt69+jKtVf9ge+//x6ASRMn0Kv7LzjqsIOZ99Hc9HdyBxJj5K7bB9Gtc3v6bGK8Fi9ayFln9KFblw5cc+XlFBT8sNn6nTu05bRe3eh7ag9+2bd38f5PP/mE/zvzdE7r1Y3fX3oxq1evTm8nq7hZM2fQs2snunfuwOOPVnyvu+v2QXTv3IG+vcre6zZW95tvVnDx+b+me5eOXHz+r1n5zTcALFm8iBY/P5LTT+3J6af2ZNCAWzZ4vct+ewmn9fTemW4P33Im8yffwTsvXV/ZTZE2yYCbkrTtEMKxIYR3QggrQwi5IYR7N1OvdQhhUQjh2hDCUuCJEMJeIYRxIYT8EMLXyd8blX+tUtv9QwjPlto+O4QwP4SwLIRwQ1o6XEkKCwv50x2DuO/Bv/PiiLFMemU8//n8szJl3pg5nYUL5jNszCtcd9MA7r5tAAA1atTgwcGP89zQkTw7ZARvvTGTuR/OAeDYFifw/LDRPPfSKJrsu1+Fgby2XmFhIX+5+zbu+svfeGrIaKZMnMCX//m8TJm335jBooXzeW74y1z5x1v4f3cNAqDJvvvz2HPDeOy5YTzy9BB22aUmLVu3A+CW2+8pPtaqzSmc3KbdNu/bzqCwsJA7b7+V+x8azLBR45g44eUNrrdZM6ezcP58Ro2byI0338odgwYUH+vWvRcPPOS1lA4zZ0xnwfwvGTthEjf3H8igW/tXWO4v997DWf93DmMnTGL33Xdn5Ihhm6yfm5vL8889zQtDhzNi9DiKigp5ZfzLABx44E/4f395gJ8ffUz6O7iDmTljOgsWfMmY8ZO4qf9AbhvYv8Jy9/2/ezjr7HMYOz45XsOHbVH9wY8/xdDho3l+6IjifQNuuYHfX34lw0aOpW27U3jqiQ0feCqhsLCQOwfdyl8fGszwMeN4ZfzLfF7uXpcYg/mMHj+RG/vfyu0DB2y27hOPDubYFi0YM34ix7ZowROPldwPGzVuwpDhoxgyfBQ33jKgzGtNfnUSu+66a5p7LYBnxr5Fj0sfrOxmSJtlwF3WX4C/xBh3B5oCQ7egTgOgLrAvcCGJ9/SJ5HYTYC3w1y158RDCocBDwNnAPkA9oNEmK1UhH380l0aNm9CwUWOqV69B+46dmT5tSpky06dNoXPXHoQQOPyII1m1ahVf5ecTQmDXXXcDYN26daxbt46QzFVpccKJZGYmkgsOO+JI8nKXbtN+7ag+mTeXho2asE/DxlSvXp22HToza/rUMmVmTZ9Kxy7dCSHQ/PAjWb1qFcu+yi9T5r3Zb9OwUWMa7L1Pmf0xRqa+NpF2HbqkvS87o3kffUjjJk1olLzeOnTqwrSpk8uUeX3qZH7RLXm9HXkUq1etJD8/D4CfHX0Me+yxR2U0fYc3dcpkunXvSQiBI448ilWl3vf1Yoz84+23aN+hIwDde/RiyuTJm61fWFjI9999x7p161j73XdkZWcDcEDTpuy3/wHbsJc7jmlTJ9N1C8Zr9ttvcUpyvLr16MXUKZO3uH5587/8ovjhSIvjT2Tyq5PS0LMdw0dzk/e6xol7XcfOXZg2ZcN7XdfuPTYYg03VnTZ1Mt169ASgW4+eTJ3y2mbb8u23a3j26Sc5/6JLUt5PbWjWe5+z/JtvK7sZ2ogQts+fymDAXVYBcGAIoX6McXWM8a1yx78KIaxI/lyV3FcE3BJj/D7GuDbGuCzGODzG+G2McRVwG9BqC1//NGBcjHF6jPF74Kbk+XcIeXm55DRoULydndOA/LyyHzry8/LKlckhPy8XSHyQPKtvLzq1PYljW5zAYYdv+BX6saNGcPxJLdPUg51Lfn4eWTklY5GVnUN+fm7ZMnkVlCk3plNenUDbDp03OP+H77/LXnXr0ajJviluuQDycnPJydm7eDsnp0HxtVRcJi+XnAYlZbIrKKPUK38vzMlpQF5u2fd9xYqvqVNn9+KHiTk5DchLjs3G6ufk5PCrc35Nx1PacErrk6hTuzYnnHjSNujRji0vN5cGP2a8NlE/BLjkwvM4o29vhr00pLhM0wN/UvyA7NVJr7B06X/T07kdQPn7WIX3utxcGpQrk5ebu8m6y5YtIysr8cAqKyub5cuXF5dbvHgR/U7rxXnnnMV7775TvP9vD9zP2b86l1o1a6a2k5KqNAPuss4DfgJ8EkKYHULoWu54/Rjjnsmfe5L78mOM360vEELYNYTw92Ra+EpgOrBnCCFjC15/H2Dh+o0Y4xpg2cYKhxAuTKbAv/PkY1Ug9TPGDXaVf9IUKyizvlBGRgbPDh3J2IlTmffRXD7/7N9lij0x+GEyMjLo1MXvTaVEReO1wRIYmx7TgoICZk2fRut2HTYoN3nSBNp1dHY7XSq4kgjlLriKLzeXOUm7Cu+FWzE2G6m/8ptvmDplMuMnTebVqTNYu3Yt48aOTkmTd2YV/V3amvHaVP0nn3mBF18ayYMPDWboC8/x7juzARgw8DaGvPA8Z/TtzZo1a6hevcaP7caOq+KbXdkiGxufLahbXv2sbCa8OoUXh43kyquv4/prrmL16tV8+sk/WbhgPm1Pab/lbZe0U3CRr1JijP8GzgghVAN6A8NCCPU2V63c9pXAQcBxMcalIYSjgPcpWaxvDVD6yz0NSv3+X+CQ9RshhF1JpJVvrL2PAI8ArFhbWNGfje1Kdk4DcpeWpHvn5S6lfvLpcUmZnHJlcoufMK9XZ/fd+fnRx/DmrBk0PbAZAC+PGcXMGa/z4N8fN2BIkazsHPJLpefn5+VuMF6bK/P2GzP4ycGHULde/TL11q1bx4xpr/H3p4ag9MjJySE3t2RWLLeC6y0nJ4fcUjNnFV2TSo0Xn3+OEcMS31JqftjhZe5zublLi1O/19trr71YtWol69atIzMzM1EmOTbl76Xr67/11hs0bNSIunXrAtDulA7Mef99unbrke7u7XBefKHseC39EeOV06DBRutnZ+cAULdePdq0a89Hcz/k50cfw/4HNOXhwY8DifTyGdOnpa2vVV12uftY6fd+vZwGOWWyBNaPQUFBwUbr1qtXL5HplZVNfn5e8XVVo0YNatRIPAA5tPlhNGrcmPlffsG8jz7i44/n0aVDWwoLC1m+bDnnn3M2jz75TNr6Lm3PNpyk2Xk5w11KCOGsEEJWjLEIWJHcXbiVp6lD4nvbK0IIdYHyy1d+APQLIVQPIRxNIo18vWFA1xDCSSGEGsCt7EBjdEjzw1i4YD5LFi+ioOAHXp04gZNbtSlTpmWrtkwYN5oYI3M/nEPt2nWon5XF18uXF68+/t133/GPt98s/j7im7Nm8PSTj3LPfQ9Ss1atbd6vHdVBhx7GooXz+e/iRRQUFDBl0gROaNm6TJkTWrZh4vgxxBiZN3cOu9WuTb36WcXHJ0+aQLsK0snfnf0WTfbdn+ycBhscU2oc2vxwFs6fz+JFiett0ivjadW6bZkyJ7duy8tjk9fbnA+oXafOBh9UlRr9fnkmQ0eMZuiI0bRpdwpjx4wixsiHcz6gdu0N3/cQAsccexyvTpoIwJjRI2nTNjF+rdu0rbB+g7334cM5c1i7di0xRt5+6032b9p0m/d1R9DvjDMZOnw0Q4ePpk3bUxi3BeN19LHH8VpyvMaOHknr5Hi1at22wvprv/2WNWsSq4+v/fZb3nxjFgc2SzxEXr4skdxWVFTE4L8/RJ++/bZV16uc5ocdzoIFJfe6iRPG07pN2XtdYgxGbzAGm6rbqnVbxo4eBcDY0aNonVzgc/ny5RQWJj4aLlq4kAUL5tOocWP69juDV6fOYPykKTzx9HPsu99+BtuSAGe4y+sE3JucWZ4P9IsxfreVM6b3Ac8DXwFLgD8DPUsdvwl4AfgaeD1Zti5AjHFeCOHS5L7dgHuBRf97d7YvmZmZXHXdDfz+kgsoKiqiW49eHHBgM0a89CIAvfv048SWJ/PGzOmc2q0TNWvW5KYBtwHw1Vf53HrTHykqKqKoqIh2HTpx0smtAbjnzkH88EMBv7v4PCCxcNp1N/avjC7uUDIzM7ns6uu5+vcXU1RUSOduvdi/6YGMHp6Y9elxal9anNiSt9+Yzpm9u7BLzZpce9Og4vrffbeWd99+kyv/ePMG554yaQJtXSwtrTIzM7nm+pv47SXnUVhYRI+ep9L0wGYMG5q43k7r24+TWrZi1ozp9PhFB2rWrEn/gbcX17/+mj/wzjuzWbHiazqf0oqLfvM7evb2n3BLhZYnt2Lm9Nfp2rk9NWvW4tZBJe/7pRdfwC23DiI7O4fL/3A111x1BQ/efx8HH3IIvU7ts8n6RxxxJO07dKRfn15kZGRy8CGHcFqf0wGY/Nqr3Hn7QL5evpzf/uYiDjroEB4e/Ni273wV1PLkVsyc8TrdOrenZq1aDCh1nVx6yQXcMiA5XldczbVXX8GDD9zHQYccQq/efTZZf9myZfzhsksBWFdYSOcuXTnxpJMBmDB+HENefB6Adqe0p0evU7dll6uUzMxMrr3+Jn5z0XkUFRbRo1fiXvfSkMS9rs/p/Tjp5FbMnDGd7p07ULNWyb1uY3UBzj3/Aq698gpGjRjO3nvvzd333gfAe+/O5qG/PkBGRgYZGRnccHN/9thjz8ro+k7vqTvOoeXPm1F/z9p89spABj48nqdGvVnZzZI2ECr8zqyqnKqQUq6y1v6wtckTqkx1alav7CZoK2VmmM5WlfhxpGqJFX4BWtuzesf+rrKboK2w9v2/Vuk/Yvmr122XN4ms2pnb/H3dYdKVJUmSJEnanhhwS5IkSZKUBn6HW5IkSZKUMlU6Hz7FnOGWJEmSJCkNDLglSZIkSUoDU8olSZIkSSmzdf+q8o7NGW5JkiRJktLAgFuSJEmSpDQwpVySJEmSlDLBdcqLOcMtSZIkSVIaGHBLkiRJkpQGppRLkiRJklLGVcpLOMMtSZIkSVIaGHBLkiRJkpQGBtySJEmSJKWBAbckSZIkSWlgwC1JkiRJUhq4SrkkSZIkKWVcpbyEM9ySJEmSJKWBAbckSZIkSWlgSrkkSZIkKWUC5pSv5wy3JEmSJElpYMAtSZIkSVIamFIuSZIkSUoZVykv4Qy3JEmSJElpYMAtSZIkSVIamFIuSZIkSUoZM8pLOMMtSZIkSVIaGHBLkiRJkpQGppRLkiRJklLHnPJiznBLkiRJkpQGBtySJEmSJKWBKeWSJEmSpJQJ5pQXc4ZbkiRJkqQ0MOCWJEmSJCkNTCmXJEmSJKVMMKO8mDPckiRJkiSlgQG3JEmSJElpYMAtSZIkSUqZsJ3+bLbdIXQKIXwaQvgshHBdBcdDCOH+5PEPQwg/29w5DbglSZIkSTu1EEIG8CDQGTgUOCOEcGi5Yp2BZsmfC4GHNndeA25JkiRJ0s7uWOCzGON/Yow/AC8CPcqV6QE8HRPeAvYMIey9qZMacEuSJEmSUqeyc8f/t5zyhsDCUtuLkvu2tkwZBtySJEmSpB1eCOHCEMI7pX4uLH24giqx/Cm2oEwZ/jvckiRJkqQdXozxEeCRjRxeBDQutd0IWPI/lCnDGW5JkiRJUsqE7fS/zZgNNAsh7B9CqAH0A8aUKzMG+L/kauUtgG9ijP/d1Emd4ZYkSZIk7dRijOtCCL8FJgIZwOMxxnkhhIuTxx8GxgNdgM+Ab4FzN3deA25JkiRJ0k4vxjieRFBdet/DpX6PwKVbc04DbkmSJElSyoTNrwi+0/A73JIkSZIkpYEBtyRJkiRJaRASaeiSJEmSJCmVnOGWJEmSJCkNDLglSZIkSUoDA25JkiRJktLAgFuSJEmSpDQw4JYkSZIkKQ0MuCVJkiRJSoP/D+pCSgG4utd8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1224x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (17,14))\n",
    "\n",
    "ax.tick_params(top=True, labeltop=True, bottom=False, labelbottom=False)\n",
    "g = sns.heatmap(df.corr(), annot=True, cmap=\"Blues\", cbar=True);\n",
    "g.set_yticklabels(g.get_yticklabels(), rotation = 0, fontsize = 12);\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation = 0, fontsize = 12);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c95d6c4",
   "metadata": {},
   "source": [
    "This heatmap shows the Pearson correlation coefficients between different features in the dataset. The Pearson correlation coefficient measures the linear relationship between two datasets and ranges from -1 to 1, where a value of 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156c31fe",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Data Encoding for ML Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d900f1a8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>type_CASH_IN</th>\n",
       "      <th>type_CASH_OUT</th>\n",
       "      <th>type_DEBIT</th>\n",
       "      <th>type_PAYMENT</th>\n",
       "      <th>type_TRANSFER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0     1   9839.64       170136.0       160296.36             0.0   \n",
       "1     1   1864.28        21249.0        19384.72             0.0   \n",
       "2     1    181.00          181.0            0.00             0.0   \n",
       "3     1    181.00          181.0            0.00         21182.0   \n",
       "4     1  11668.14        41554.0        29885.86             0.0   \n",
       "\n",
       "   newbalanceDest  isFraud  type_CASH_IN  type_CASH_OUT  type_DEBIT  \\\n",
       "0             0.0        0             0              0           0   \n",
       "1             0.0        0             0              0           0   \n",
       "2             0.0        1             0              0           0   \n",
       "3             0.0        1             0              1           0   \n",
       "4             0.0        0             0              0           0   \n",
       "\n",
       "   type_PAYMENT  type_TRANSFER  \n",
       "0             1              0  \n",
       "1             1              0  \n",
       "2             0              1  \n",
       "3             0              0  \n",
       "4             1              0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded = pd.get_dummies(data=df, columns=['type'])\n",
    "df_encoded.drop(['nameOrig', 'nameDest'], axis=1, inplace=True)\n",
    "df_encoded.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa72fe72",
   "metadata": {},
   "source": [
    "In this step, data encoding was performed to prepare it for training the machine learning model. The pd.get_dummies() method was used to encode the 'type' column, which represents the transaction type. This resulted in the creation of binary variables for each transaction type, allowing the model to capture categorical information appropriately.\n",
    "\n",
    "Next, the 'nameOrig' and 'nameDest' columns were removed, as they are not relevant to the model training. These columns contain user identification information associated with the transactions and do not directly contribute to fraud detection.\n",
    "\n",
    "The result was a new dataframe called df_encoded, where the columns were encoded and the irrelevant columns were removed. The data is now ready to be used in training the fraud detection model. The provided code snippet shows the first few rows of the encoded dataframe.\n",
    "\n",
    "This step is crucial for properly preparing the data, ensuring that the model can understand and interpret the transaction information accurately, thereby enhancing the effectiveness of fraud detection."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8d1827",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Data Split: Dividing into Training and Test Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eec40673",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['step', 'amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest',\n",
       "       'newbalanceDest', 'type_CASH_IN', 'type_CASH_OUT', 'type_DEBIT',\n",
       "       'type_PAYMENT', 'type_TRANSFER'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_encoded.drop('isFraud',  axis=1)\n",
    "y = df_encoded['isFraud']\n",
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "19492879",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 101\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state = SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef8f2978",
   "metadata": {},
   "source": [
    "In this step, the data was split into training and test sets using the train_test_split function. A seed value of 101 was used to ensure reproducibility of the experiments. This approach is useful for maintaining consistency in results and facilitating comparison across different models and configurations.\n",
    "\n",
    "The data was divided such that 70% of the samples were assigned to the training set (x_train and y_train), while 30% were set aside for the test set (x_test and y_test). This division allows for evaluating the model's performance on unseen data and assessing its ability to generalize."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94650adb",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Model Evaluation Functions: Metrics and Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8690df52",
   "metadata": {},
   "outputs": [],
   "source": [
    "def conf_matrix(y_test, y_pred, plot = True):\n",
    "    \n",
    "    cmlst = []\n",
    "    \n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    cmlst.append(cm)\n",
    "    \n",
    "    cm_norm = confusion_matrix(y_test, y_pred, normalize='true')\n",
    "    cmlst.append(cm_norm)\n",
    "    \n",
    "    if (plot==True):\n",
    "        fig, ax = plt.subplots(1, 2)\n",
    "        fig.set_size_inches(20,10)\n",
    "        \n",
    "        cbar_ax = fig.add_axes([.91, .15, .03, .7])\n",
    "        hm = sns.heatmap(cm, annot=True, annot_kws={\"size\": 18},fmt='.12g', ax=ax[0], cmap=\"Blues\", square=True, cbar=True,cbar_ax=cbar_ax); \n",
    "        \n",
    "        ax[0].set_xlabel('Predicted label', fontsize=18);\n",
    "        ax[0].set_ylabel('True label', fontsize=18); \n",
    "        ax[0].set_title('Confusion Matrix', fontsize=18); \n",
    "        ax[0].xaxis.set_ticklabels(['0', '1']); \n",
    "        ax[0].yaxis.set_ticklabels(['0', '1']);  \n",
    "        \n",
    "        hm2 = sns.heatmap(cm_norm, annot=True, annot_kws={\"size\": 18} , fmt='.2%', ax=ax[1], cmap=\"Blues\", square=True, cbar_ax = cbar_ax); \n",
    "        \n",
    "        ax[1].set_xlabel('Predicted label', fontsize=18);\n",
    "        ax[1].set_ylabel('True label', fontsize=18); \n",
    "        ax[1].set_title('Normalized confusion matrix with percentage values', fontsize=18); \n",
    "        ax[1].xaxis.set_ticklabels(['0', '1']); \n",
    "        ax[1].yaxis.set_ticklabels(['0', '1']);  \n",
    "    return cmlst"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "336b30c1",
   "metadata": {},
   "source": [
    "A confusion matrix is a tabular representation that summarizes the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions. It provides valuable insights into the model's ability to correctly classify instances, identify any imbalances between classes, and evaluate the trade-offs between different types of errors, such as false positives and false negatives. By analyzing the confusion matrix, we can gain a comprehensive understanding of the model's predictive accuracy and make informed decisions based on its performance.\n",
    "\n",
    "![Metrics](metrics.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9a988fae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_metrics(model):\n",
    "    \n",
    "    y_pred = model.predict(x_test)\n",
    "    \n",
    "    lstMetrics = []\n",
    "    lstMetrics.append(metrics.accuracy_score(y_test, y_pred))\n",
    "    lstMetrics.append(metrics.precision_score(y_test, y_pred))\n",
    "    lstMetrics.append(metrics.recall_score(y_test, y_pred))\n",
    "    lstMetrics.append(metrics.f1_score(y_test, y_pred))\n",
    "    \n",
    "    df_metrics = pd.DataFrame(lstMetrics)\n",
    "    df_metrics = df_metrics.T\n",
    "    df_metrics = df_metrics.rename(columns={0: 'Accuracy Score', \n",
    "                                            1: 'Precision Score',\n",
    "                                            2: 'Recall Score',\n",
    "                                            3: 'F1 Score'})\n",
    "    print(df_metrics.to_string(index=False) + '\\n')\n",
    "    \n",
    "    conf_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22247be8",
   "metadata": {},
   "source": [
    "### Metrics:\n",
    "\n",
    "<b>Accuracy Score</b>: The accuracy score measures the overall correctness of the model's predictions, calculating the ratio of correctly classified instances to the total number of instances. While accuracy is an important metric, in the case of imbalanced datasets, <b>it can be misleading due to the majority class overwhelming the minority class</b>.\n",
    "\n",
    "<b>Precision Score</b>: Precision quantifies the ability of the model to correctly identify positive instances (fraudulent transactions) among the instances it predicted as positive. It calculates the ratio of true positive predictions to the total number of predicted positive instances. In the context of fraud detection, precision is <b>valuable in reducing false positives</b>, as it represents the proportion of correctly predicted fraud cases among all predicted fraud cases.\n",
    "\n",
    "<b>Recall Score</b>: Recall, also known as sensitivity or true positive rate, measures the ability of the model to correctly identify positive instances among all actual positive instances. It calculates the ratio of true positive predictions to the total number of actual positive instances. In fraud detection, recall is crucial as it captures the ability to detect <b>true fraudulent transactions, minimizing false negatives</b>.\n",
    "\n",
    "<b>F1 Score</b>: The F1 score is a harmonic mean of precision and recall, providing a single metric that balances both measures. It considers both the ability to reduce false positives and false negatives, making it useful for evaluating model performance in imbalanced datasets. The F1 score provides a balanced evaluation, taking into account the trade-off between precision and recall."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4ff9f76",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Initial Model Evaluation with Imbalanced Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "05ae22ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trained. Elapsed time: 26.95\n",
      "\n",
      "Logistic regression metrics:\n",
      " Accuracy Score  Precision Score  Recall Score  F1 Score\n",
      "       0.998354         0.368325      0.419528  0.392263\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = LogisticRegression(max_iter=1000, random_state=SEED)\n",
    "t1 = datetime.now()\n",
    "lr.fit(x_train, y_train)\n",
    "t2 = datetime.now()\n",
    "fit_time = (t2-t1).total_seconds()\n",
    "print('Trained. Elapsed time: {:.2f}\\n'.format(fit_time))\n",
    "print('Logistic regression metrics:')\n",
    "model_metrics(lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11381e91",
   "metadata": {},
   "source": [
    "In this stage, a logistic regression model was trained to perform the initial test using imbalanced data in order to observe the model's behavior. Subsequently, the \"model_metrics\" function was used to calculate the performance metrics of the model, which are presented as follows: an Accuracy Score of 0.998354, Precision Score of 0.368325, Recall Score of 0.419528, and F1 Score of 0.392263.\n",
    "non-fraudulent transactions\n",
    "\n",
    "Furthermore, a confusion matrix was generated to visualize the rates of correct and incorrect classification. The confusion matrix reveals that 99.91% of instances from the negative class (0) that are non-fraudulent transactions were correctly classified, while only 0.09% were non-fraudulent transactions incorrectly classified as fraud (1). On the other hand, in the positive class, it was observed that 41.95% of fraudulent transactions were correctly classified, while 58.05% were fraudulent transactions incorrectly classified as non-fraudulent.\n",
    "\n",
    "It is important to note that, in cases like this, accuracy should not be considered in isolation as the sole metric to evaluate model performance. Due to the data imbalance, it is necessary to consider other metrics such as precision and recall, which take into account the false positive rate and the positive detection rate, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc4a680b",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Addressing Class Imbalance: Solving the Imbalance Problem\n",
    "When it comes to classifying data, we sometimes run into a problem where our dataset is imbalanced. This means we have many more instances of one class than another. For example, in a dataset of credit card transactions, the vast majority of transactions are legitimate, but a small fraction are fraudulent. This imbalance can lead to difficulties in modeling, as the model will be biased towards predicting the majority class, resulting in poor performance in detecting the minority class (the fraudulent transactions).\n",
    "\n",
    "### Cost-Sensitive Learning\n",
    "\n",
    "Cost-Sensitive Learning is a technique used to handle such imbalanced datasets. It works by assigning a higher cost to misclassifying the minority class.\n",
    "In a non-cost-sensitive learning approach, every error made by the model during the learning process is considered equal. However, cost-sensitive learning differentiates these errors, assigning them different weights or costs. This ensures the model learns to pay more attention to the minority class despite its smaller representation in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8ed888d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression metrics:\n",
      " Accuracy Score  Precision Score  Recall Score  F1 Score\n",
      "       0.911551         0.012623      0.891601  0.024893\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = LogisticRegression(class_weight='balanced', max_iter=1000, random_state=SEED)\n",
    "lr.fit(x_train, y_train)\n",
    "\n",
    "print('Logistic regression metrics:')\n",
    "model_metrics(lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53cbf73c",
   "metadata": {},
   "source": [
    "In this stage, the Logistic Regression technique was applied with the class_weight='balanced' option, a Cost-Sensitive Learning approach, to handle the class imbalance problem in the dataset. This technique adjusts the class weights inversely proportional to the number of instances in each class, giving more importance to the minority class during the training process.\n",
    "\n",
    "The model's performance improved significantly with this technique. The resulting metrics were: Accuracy of 91.16%, Precision of 1.26%, Recall of 89.16%, and F1 Score of 2.49%.\n",
    "\n",
    "The confusion matrix displayed reveals that 91.16% of legitimate transactions (class 0) were correctly identified by the model, while 8.84% were incorrectly classified as fraudulent (class 1). On the other hand, 10.84% of fraudulent transactions (class 1) were incorrectly classified as legitimate, but the model was able to correctly identify 89.16% of fraudulent transactions.\n",
    "\n",
    "Despite Precision still being low, the high Recall value indicates that the model is effectively identifying the majority of fraudulent transactions, which is a desirable result in this context. The adjustment of class weights managed to mitigate the model's bias towards the majority class, enhancing its ability to detect the minority class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0803979b",
   "metadata": {},
   "source": [
    "### Oversampling with SMOTE\n",
    "\n",
    "SMOTE stands for Synthetic Minority Over-sampling Technique. It's a popular resampling technique used to address the issue of class imbalance in machine learning datasets. The primary purpose of SMOTE is to create 'synthetic' samples in order to increase the number of minority class instances.\n",
    "\n",
    "To prevent information leakage, the SMOTE technique was only applied to the training data because the purpose of the test data is to simulate a real and unknown environment. By applying SMOTE to the test data, we would be introducing synthetic examples that do not accurately represent the distribution of the real data, compromising the model's ability to generalize correctly to new samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c40ecfd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install -U imbalanced-learn\n",
    "from imblearn.over_sampling import SMOTE\n",
    "smote = SMOTE(random_state=SEED)\n",
    "x_resampled, y_resampled = smote.fit_resample(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a5b23f7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>type_CASH_IN</th>\n",
       "      <th>type_CASH_OUT</th>\n",
       "      <th>type_DEBIT</th>\n",
       "      <th>type_PAYMENT</th>\n",
       "      <th>type_TRANSFER</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isFraud</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>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "      <td>4448038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            step   amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "isFraud                                                                    \n",
       "0        4448038  4448038        4448038         4448038         4448038   \n",
       "1        4448038  4448038        4448038         4448038         4448038   \n",
       "\n",
       "         newbalanceDest  type_CASH_IN  type_CASH_OUT  type_DEBIT  \\\n",
       "isFraud                                                            \n",
       "0               4448038       4448038        4448038     4448038   \n",
       "1               4448038       4448038        4448038     4448038   \n",
       "\n",
       "         type_PAYMENT  type_TRANSFER  \n",
       "isFraud                               \n",
       "0             4448038        4448038  \n",
       "1             4448038        4448038  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_balanced = pd.concat([y_resampled, x_resampled], axis=1)\n",
    "df_balanced.groupby('isFraud').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "05ac07fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trained. Elapsed time: 47.72\n"
     ]
    }
   ],
   "source": [
    "t1 = datetime.now()\n",
    "lr = LogisticRegression(max_iter=1000, random_state=SEED)\n",
    "lr.fit(x_resampled, y_resampled)\n",
    "t2 = datetime.now()\n",
    "fit_time = (t2-t1).total_seconds()\n",
    "print('Trained. Elapsed time: {:.2f}'.format(fit_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "235ebc7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression metrics:\n",
      " Accuracy Score  Precision Score  Recall Score  F1 Score\n",
      "       0.915615         0.013266      0.894497  0.026143\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Logistic regression metrics:')\n",
    "model_metrics(lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2393674d",
   "metadata": {},
   "source": [
    "In this stage of the project, an alternative to the previous Cost-Sensitive Learning model was tested, by applying the SMOTE oversampling technique. The metrics of this model showed similar performance to the previous model but with some notable improvements.\n",
    "\n",
    "The accuracy of the SMOTE model was 0.915615, a slight increase compared to the accuracy of the Cost-Sensitive Learning model, which was 0.911551. Precision also saw a slight increase, from 0.012623 to 0.013266.\n",
    "\n",
    "The same pattern was observed in the recall metric, which went up from 0.891601 to 0.894497, indicating a larger proportion of true positives being correctly identified. Furthermore, the F1 Score metric, which considers both precision and recall, also had an increase from 0.024893 to 0.026143.\n",
    "\n",
    "As for the confusion matrix, the new model presented similar rates of correct and incorrect classification for both fraudulent and non-fraudulent transactions. However, there was a slight improvement in the detection rate of fraudulent transactions (11), which increased from 89.16% to 89.45%.\n",
    "\n",
    "In summary, the application of the SMOTE oversampling technique resulted in a model with slightly better performance than the previous model that used Cost-Sensitive Learning, especially when it comes to detecting fraudulent transactions.\n",
    "\n",
    "Due to the improved performance achieved with the use of the SMOTE technique to tackle class imbalance, the synthetically generated data will be employed in the subsequent stages of this project."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3b032be",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Evaluation of Classification Models\n",
    "\n",
    "In this stage of the project, four different classification algorithms are tested. These include the Logistic Regression model, known for its simplicity and efficiency with binary classification problems; the Decision Tree Classifier, appreciated for its ease of interpretation and ability to handle both numerical and categorical data; the Gaussian Naive Bayes, noted for its effectiveness in high-dimensional spaces and the K-Nearest Neighbors (KNN), an instance-based method, robust to noise and effective when the decision boundary is very irregular. By exploring these different algorithms, the aim is to determine the one that offers the best performance for this particular dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0ffd6df3",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [\n",
    "    LogisticRegression(max_iter=1000, random_state=SEED),\n",
    "    DecisionTreeClassifier(random_state=SEED),\n",
    "    KNeighborsClassifier(),\n",
    "    GaussianNB()\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "322f09e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression trained. Elapsed time: 49.59\n",
      "DecisionTreeClassifier trained. Elapsed time: 97.60\n",
      "KNeighborsClassifier trained. Elapsed time: 46.79\n",
      "GaussianNB trained. Elapsed time: 2.30\n"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "for i, m in enumerate(models):\n",
    "    \n",
    "    t1 = datetime.now()\n",
    "    m.fit(x_resampled, y_resampled)\n",
    "    t2 = datetime.now()\n",
    "    fit_time = (t2-t1).total_seconds()\n",
    "    \n",
    "    print('{} trained. Elapsed time: {:.2f}'.format(m.__class__.__name__,fit_time))\n",
    "    \n",
    "    y_pred = m.predict(x_test)\n",
    "    \n",
    "    score = []\n",
    "    score.append(m.__class__.__name__)\n",
    "    score.append(metrics.accuracy_score(y_test, y_pred))\n",
    "    score.append(metrics.precision_score(y_test, y_pred))\n",
    "    score.append(metrics.recall_score(y_test, y_pred))\n",
    "    score.append(metrics.f1_score(y_test, y_pred))\n",
    "    score.append(fit_time)\n",
    "    score.append(conf_matrix(y_test, y_pred, False))\n",
    "    \n",
    "    scores.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bc5d0243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model_Name</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Fit_Time</th>\n",
       "      <th>Conf_Matrix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.915615</td>\n",
       "      <td>0.013266</td>\n",
       "      <td>0.894497</td>\n",
       "      <td>0.026143</td>\n",
       "      <td>49.591998</td>\n",
       "      <td>[[[1745552, 160817], [255, 2162]], [[0.9156422...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.999402</td>\n",
       "      <td>0.687998</td>\n",
       "      <td>0.965246</td>\n",
       "      <td>0.803375</td>\n",
       "      <td>97.603964</td>\n",
       "      <td>[[[1905311, 1058], [84, 2333]], [[0.9994450182...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.995202</td>\n",
       "      <td>0.195318</td>\n",
       "      <td>0.894084</td>\n",
       "      <td>0.320599</td>\n",
       "      <td>46.794994</td>\n",
       "      <td>[[[1897466, 8903], [256, 2161]], [[0.995329865...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.965703</td>\n",
       "      <td>0.017140</td>\n",
       "      <td>0.462971</td>\n",
       "      <td>0.033056</td>\n",
       "      <td>2.303983</td>\n",
       "      <td>[[[1842201, 64168], [1298, 1119]], [[0.9663401...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model_Name  Accuracy  Precision    Recall        F1   Fit_Time  \\\n",
       "0      LogisticRegression  0.915615   0.013266  0.894497  0.026143  49.591998   \n",
       "1  DecisionTreeClassifier  0.999402   0.687998  0.965246  0.803375  97.603964   \n",
       "2    KNeighborsClassifier  0.995202   0.195318  0.894084  0.320599  46.794994   \n",
       "3              GaussianNB  0.965703   0.017140  0.462971  0.033056   2.303983   \n",
       "\n",
       "                                         Conf_Matrix  \n",
       "0  [[[1745552, 160817], [255, 2162]], [[0.9156422...  \n",
       "1  [[[1905311, 1058], [84, 2333]], [[0.9994450182...  \n",
       "2  [[[1897466, 8903], [256, 2161]], [[0.995329865...  \n",
       "3  [[[1842201, 64168], [1298, 1119]], [[0.9663401...  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_models = pd.DataFrame(scores, columns=['Model_Name', 'Accuracy', 'Precision', 'Recall', 'F1', 'Fit_Time', 'Conf_Matrix'])\n",
    "df_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9e337e67",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2)\n",
    "fig.set_size_inches(20,20)\n",
    "fig.suptitle('Normalized Confusion Matrix of each Model', fontsize=50)\n",
    "cbar_ax = fig.add_axes([.91, .15, .03, .7])\n",
    "norm = 1\n",
    "\n",
    "for i, ax in enumerate(ax.flatten()):\n",
    "    cm = df_models.iloc[i].Conf_Matrix[norm]\n",
    "    title = df_models.iloc[i].Model_Name\n",
    "    hm = sns.heatmap(cm, annot=True, annot_kws={\"size\": 18}, fmt='.2%', ax=ax, cmap=\"Blues\", square=True, cbar_ax=cbar_ax)\n",
    "    ax.set_xlabel('Predicted label', fontsize=18)\n",
    "    ax.set_ylabel('True label', fontsize=18)\n",
    "    ax.set_title(title, fontsize=18)\n",
    "    ax.xaxis.set_ticklabels(['0', '1'])\n",
    "    ax.yaxis.set_ticklabels(['0', '1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3e88eaf",
   "metadata": {},
   "source": [
    "The <b>Logistic Regression</b> model achieved a solid performance by correctly classifying 91.56% of non-fraudulent transactions and 89.45% of fraudulent transactions. However, it misclassified 8.44% of non-fraudulent transactions as fraudulent and 10.55% of fraudulent transactions as non-fraudulent.\n",
    "\n",
    "The <b>Decision Tree Classifier</b> displayed an outstanding performance, correctly classifying 99.94% of non-fraudulent transactions and 96.52% of fraudulent transactions. Only a tiny percentage of transactions was misclassified: 3.48% of fraudulent transactions were classified as non-fraudulent and 0.06% of non-fraudulent transactions were classified as fraudulent.\n",
    "\n",
    "The <b>KNN</b> also displayed a good performance by correctly classifying 99.53% of non-fraudulent transactions and 89.41% of fraudulent transactions. Moreover, it misclassified 0.47% of non-fraudulent transactions as fraudulent and 10.59% of fraudulent transactions as non-fraudulent.\n",
    "\n",
    "Lastly, the <b>GaussianNB</b> correctly classified 96.63% of non-fraudulent transactions and 46.30% of fraudulent transactions. On the other hand, it misclassified 3.37% of non-fraudulent transactions as fraudulent and 53.70% of fraudulent transactions as non-fraudulent.\n",
    "\n",
    "These results indicate that, although all four models are capable of correctly classifying most non-fraudulent transactions, performance in classifying fraudulent transactions significantly varies among the models. Specifically, the <b>DecisionTreeClassifier</b> stands out, displaying the highest true positive rate and the lowest false negative rate.\n",
    "\n",
    "As part of the improvement process, the <b>Random Forest Classifier</b> will be tested as an extension of the Decision Tree Classifier, aiming to leverage its ensemble approach and mitigate the potential overfitting risks associated with a single decision tree model.\n",
    "\n",
    "***\n",
    "\n",
    "## Hyperparameter Optimization using Randomized Search and MLflow Tracking\n",
    "\n",
    "To find the best hyperparameter values for the model, the <b>Randomized Search</b> from Sklearn will be used with <b>cross-validation</b>. This approach involves exploring different combinations of hyperparameters through random iterations. By doing so, the optimal configuration for the model can be determined more efficiently and accurately.\n",
    "\n",
    "Also, <b>MLflow</b> will be used to monitor the evolution of the model. MLflow is an open platform for managing the machine learning lifecycle, which allows us to track and log model metrics over time, as well as details of different model runs and versions. This will make it easier to compare different hyperparameter configurations and help identify the best setup for our model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fdb55de",
   "metadata": {},
   "source": [
    "### Defining a Customized RandomizedSearchCV Class\n",
    "\n",
    "In this step, a custom class called CustomRandomizedSearchCV is being created. This class has been developed to enable cross-validation during the random parameter search. Additionally, it has been designed to store the resulting metrics in MLflow. With this custom class, it will be possible to perform a more accurate and organized analysis of the model metrics throughout the hyperparameter optimization process, thereby facilitating decision-making."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dcc3bca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomRandomizedSearchCV(RandomizedSearchCV):\n",
    "    def __init__(self, estimator, param_distributions, n_iter=10,\n",
    "                 scoring=None, n_jobs=None, refit=True,\n",
    "                 cv=None, verbose=0, pre_dispatch='2*n_jobs',\n",
    "                 random_state=None, error_score=np.nan, return_train_score=False):\n",
    "\n",
    "        super().__init__(\n",
    "            estimator=estimator, \n",
    "            param_distributions=param_distributions, \n",
    "            n_iter=n_iter, \n",
    "            scoring=scoring, \n",
    "            n_jobs=n_jobs, \n",
    "            refit=refit, \n",
    "            cv=cv, \n",
    "            verbose=verbose, \n",
    "            pre_dispatch=pre_dispatch, \n",
    "            random_state=random_state, \n",
    "            error_score=error_score, \n",
    "            return_train_score=return_train_score\n",
    "        )\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.best_score_ = 0.0\n",
    "        self.best_params_ = None\n",
    "        self.best_estimator_ = None\n",
    "\n",
    "        candidate_params = list(ParameterSampler(\n",
    "            self.param_distributions, self.n_iter,\n",
    "            random_state=self.random_state))\n",
    "\n",
    "        for i, parameters in enumerate(candidate_params):\n",
    "            with mlflow.start_run():\n",
    "                run_name = f\"Run {i+1} - {self.estimator.__class__.__name__}\"\n",
    "                mlflow.set_tag(\"mlflow.runName\", run_name)\n",
    "                \n",
    "                self.estimator.set_params(**parameters)\n",
    "                for key, value in parameters.items():\n",
    "                    mlflow.log_param(key, value)\n",
    "                \n",
    "                cv_results = cross_validate(self.estimator, X, y, cv=self.cv, scoring=self.scoring, n_jobs=self.n_jobs)\n",
    "                \n",
    "                for key, value in cv_results.items():\n",
    "                    mean_score = np.mean(value)\n",
    "                    mlflow.log_metric(key, mean_score)\n",
    "                    \n",
    "                    if (key == \"test_recall\" and mean_score > self.best_score_): \n",
    "                        self.best_score_ = mean_score\n",
    "                        self.best_params_ = parameters\n",
    "                        \n",
    "        return self"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b919dbf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimization(param_grid):\n",
    "    rf = RandomForestClassifier(random_state = SEED)\n",
    "    scoring = {'recall': make_scorer(recall_score),'precision': make_scorer(precision_score),\"accuracy\": make_scorer(accuracy_score)}\n",
    "    cross_val = KFold(n_splits = 3, shuffle=True, random_state = SEED)\n",
    "\n",
    "    random_cv = CustomRandomizedSearchCV(rf, param_grid, n_iter=50, cv=cross_val, scoring=scoring, refit=\"recall\", n_jobs=4, random_state = SEED)\n",
    "\n",
    "\n",
    "    t1 = datetime.now()\n",
    "    rcv = random_cv.fit(x_resampled, y_resampled)\n",
    "    t2 = datetime.now()\n",
    "\n",
    "    fit_time = (t2-t1).total_seconds()\n",
    "    print('Trained. Elapsed time: {:.2f}'.format(fit_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cae30fcb",
   "metadata": {},
   "source": [
    "### Parameter Search: Setting a new Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0f960f7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023/07/12 10:44:49 INFO mlflow.tracking.fluent: Experiment with name 'RF_Search' does not exist. Creating a new experiment.\n"
     ]
    }
   ],
   "source": [
    "mlflow.set_tracking_uri(\"http://localhost:5000\")\n",
    "experiment = mlflow.set_experiment('RF_Search')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ededee",
   "metadata": {},
   "source": [
    "In the following code block, an MLflow tracking environment is set up to log the results of the experiment. The tracking URI is set to \"http://localhost:5000\" and a new experiment entitled \"RF_Search\" is created. This setup allows for the storage and comparison of the results from the parameter search in an organized manner."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a47a1c30",
   "metadata": {},
   "source": [
    "### Parameter Grid Definition and Search Execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6db803e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trained. Elapsed time: 77539.69\n"
     ]
    }
   ],
   "source": [
    "n_estimators = np.arange(10, 250, step=15)\n",
    "criterion = [\"gini\", \"entropy\"]\n",
    "max_features = [\"auto\", \"sqrt\", \"log2\"]\n",
    "max_depth = list(np.arange(2, 10, step=1))\n",
    "bootstrap = [True, False]\n",
    "\n",
    "param_grid = {\n",
    "    \"n_estimators\": n_estimators,\n",
    "    \"criterion\": criterion,\n",
    "    \"max_features\": max_features,\n",
    "    \"max_depth\": max_depth,\n",
    "    \"bootstrap\": bootstrap,\n",
    "}\n",
    "\n",
    "optimization(param_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c4bb0af",
   "metadata": {},
   "source": [
    "In the hyperparameter search process, 50 random forest classification models were tested using RandomizedSearchCV. The results of the hyperparameter search were ranked according to recall, which was the metric chosen to refine the model, as it is vital that the model is able to identify as many true positives as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fa1d8ad7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>run_id</th>\n",
       "      <th>metrics.test_recall</th>\n",
       "      <th>metrics.test_accuracy</th>\n",
       "      <th>metrics.test_precision</th>\n",
       "      <th>metrics.fit_time</th>\n",
       "      <th>metrics.score_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>eeb80c7c7f394119a8c7722b9b5d5958</td>\n",
       "      <td>0.997334</td>\n",
       "      <td>0.987045</td>\n",
       "      <td>0.977226</td>\n",
       "      <td>3932.265649</td>\n",
       "      <td>53.885743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c8f35a2e4a8a44f29475d31e52ff971a</td>\n",
       "      <td>0.997325</td>\n",
       "      <td>0.986971</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>4525.077519</td>\n",
       "      <td>58.136928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>54ccd80a68dd4ae2a125bbe009b18ff7</td>\n",
       "      <td>0.997184</td>\n",
       "      <td>0.987440</td>\n",
       "      <td>0.978124</td>\n",
       "      <td>1322.601321</td>\n",
       "      <td>25.432360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>d30ea74816e04f74a8cb3e41fe382e86</td>\n",
       "      <td>0.997180</td>\n",
       "      <td>0.987333</td>\n",
       "      <td>0.977920</td>\n",
       "      <td>2764.908997</td>\n",
       "      <td>50.770707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1b616d4629d24a1a8de93406dee31881</td>\n",
       "      <td>0.997147</td>\n",
       "      <td>0.987307</td>\n",
       "      <td>0.977902</td>\n",
       "      <td>3455.877894</td>\n",
       "      <td>61.626054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>b4080d068de842ea865321414340c504</td>\n",
       "      <td>0.996830</td>\n",
       "      <td>0.987304</td>\n",
       "      <td>0.978195</td>\n",
       "      <td>1530.327455</td>\n",
       "      <td>28.705132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>14502ec4b1dd454a98a84be5c05c0092</td>\n",
       "      <td>0.996785</td>\n",
       "      <td>0.988251</td>\n",
       "      <td>0.980064</td>\n",
       "      <td>1696.384142</td>\n",
       "      <td>34.837516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>e032c7dc9b6948f18d972fb8103d5931</td>\n",
       "      <td>0.996753</td>\n",
       "      <td>0.986983</td>\n",
       "      <td>0.977651</td>\n",
       "      <td>2068.084902</td>\n",
       "      <td>38.239337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7c3cd99c737d4a639d511d17c42ad72a</td>\n",
       "      <td>0.996703</td>\n",
       "      <td>0.983594</td>\n",
       "      <td>0.971240</td>\n",
       "      <td>1430.287556</td>\n",
       "      <td>20.295892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>8be8418856534cba8be3439a95ffd7a5</td>\n",
       "      <td>0.996477</td>\n",
       "      <td>0.989563</td>\n",
       "      <td>0.982887</td>\n",
       "      <td>1165.434837</td>\n",
       "      <td>17.971000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ca31e32302e2441eb86750ba278e8266</td>\n",
       "      <td>0.996477</td>\n",
       "      <td>0.989563</td>\n",
       "      <td>0.982887</td>\n",
       "      <td>1177.021646</td>\n",
       "      <td>17.902369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4099effab4374f13848f4d145071538b</td>\n",
       "      <td>0.996341</td>\n",
       "      <td>0.986542</td>\n",
       "      <td>0.977205</td>\n",
       "      <td>342.781505</td>\n",
       "      <td>8.099001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ad4e066c7823465385ddf95a7840b5c9</td>\n",
       "      <td>0.996128</td>\n",
       "      <td>0.986309</td>\n",
       "      <td>0.976946</td>\n",
       "      <td>3610.768192</td>\n",
       "      <td>51.348266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>81a366f3a78d4681a7fd67f9d362fc66</td>\n",
       "      <td>0.996021</td>\n",
       "      <td>0.986493</td>\n",
       "      <td>0.977401</td>\n",
       "      <td>2679.185560</td>\n",
       "      <td>51.614022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>c9eef2dbeb3740d6833a516140d14b84</td>\n",
       "      <td>0.995869</td>\n",
       "      <td>0.985975</td>\n",
       "      <td>0.976547</td>\n",
       "      <td>2831.913746</td>\n",
       "      <td>42.740001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>170c3f0330ff4f088578214d24520ba4</td>\n",
       "      <td>0.995639</td>\n",
       "      <td>0.985698</td>\n",
       "      <td>0.976230</td>\n",
       "      <td>2760.819985</td>\n",
       "      <td>38.086013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>e9072235d36b44f3a302fc96fc57692f</td>\n",
       "      <td>0.995246</td>\n",
       "      <td>0.981356</td>\n",
       "      <td>0.968346</td>\n",
       "      <td>962.500906</td>\n",
       "      <td>20.113518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4b96c7fa65e84b1c8a50b4378e7bd428</td>\n",
       "      <td>0.994377</td>\n",
       "      <td>0.980582</td>\n",
       "      <td>0.967679</td>\n",
       "      <td>2611.283167</td>\n",
       "      <td>37.223000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4d73c21871a6432ea062e9299eee4186</td>\n",
       "      <td>0.994211</td>\n",
       "      <td>0.980652</td>\n",
       "      <td>0.967963</td>\n",
       "      <td>2955.809634</td>\n",
       "      <td>41.306533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>62735d6c9b6b407db5c055b625f362f4</td>\n",
       "      <td>0.993347</td>\n",
       "      <td>0.980126</td>\n",
       "      <td>0.967759</td>\n",
       "      <td>1747.536740</td>\n",
       "      <td>35.358992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>319a54ad1da846d2948a067cb38fa149</td>\n",
       "      <td>0.984577</td>\n",
       "      <td>0.972693</td>\n",
       "      <td>0.961720</td>\n",
       "      <td>1601.735905</td>\n",
       "      <td>35.115334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>286ca764d9a24f208ee820bae13fe9dd</td>\n",
       "      <td>0.982483</td>\n",
       "      <td>0.967471</td>\n",
       "      <td>0.953893</td>\n",
       "      <td>96.656987</td>\n",
       "      <td>4.896000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>d2bc15a641da40deb888d8ac399f065f</td>\n",
       "      <td>0.981277</td>\n",
       "      <td>0.974224</td>\n",
       "      <td>0.967629</td>\n",
       "      <td>234.611847</td>\n",
       "      <td>7.388015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4e3a042229b54cbdad021d6ff53144b8</td>\n",
       "      <td>0.981236</td>\n",
       "      <td>0.963512</td>\n",
       "      <td>0.947644</td>\n",
       "      <td>484.041669</td>\n",
       "      <td>11.753666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>430c2b95d53748b0bdf1d5c5806a48ab</td>\n",
       "      <td>0.977193</td>\n",
       "      <td>0.972456</td>\n",
       "      <td>0.968022</td>\n",
       "      <td>2831.329181</td>\n",
       "      <td>45.532745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>b882dbb353de4a3da61513ebd7ff194e</td>\n",
       "      <td>0.976357</td>\n",
       "      <td>0.971964</td>\n",
       "      <td>0.967853</td>\n",
       "      <td>1805.255453</td>\n",
       "      <td>39.848334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>07d325b504c744c98fe7bfcd0cb04648</td>\n",
       "      <td>0.973881</td>\n",
       "      <td>0.969091</td>\n",
       "      <td>0.964644</td>\n",
       "      <td>350.599318</td>\n",
       "      <td>10.409004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>27e861b0489d48a7976c129cbf89b5fb</td>\n",
       "      <td>0.972567</td>\n",
       "      <td>0.957392</td>\n",
       "      <td>0.943919</td>\n",
       "      <td>1559.345654</td>\n",
       "      <td>40.480753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3ee64cf7d89a43a58382a1d7db218e1b</td>\n",
       "      <td>0.971655</td>\n",
       "      <td>0.957162</td>\n",
       "      <td>0.944285</td>\n",
       "      <td>2195.295126</td>\n",
       "      <td>41.968025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3efc3e31bdc84816807f0a1b2babb89b</td>\n",
       "      <td>0.971592</td>\n",
       "      <td>0.953047</td>\n",
       "      <td>0.936844</td>\n",
       "      <td>252.544006</td>\n",
       "      <td>7.649999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>dd76ffadff984dafb86edc3593e60921</td>\n",
       "      <td>0.971459</td>\n",
       "      <td>0.956685</td>\n",
       "      <td>0.943579</td>\n",
       "      <td>1520.494840</td>\n",
       "      <td>36.016013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4d509909568e48c3b5cbfcc7dc95f4aa</td>\n",
       "      <td>0.970992</td>\n",
       "      <td>0.955763</td>\n",
       "      <td>0.942291</td>\n",
       "      <td>1051.924028</td>\n",
       "      <td>28.776346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b30594db1c344a60b54fc0df175eb8c1</td>\n",
       "      <td>0.969200</td>\n",
       "      <td>0.967869</td>\n",
       "      <td>0.966628</td>\n",
       "      <td>2125.369897</td>\n",
       "      <td>38.491666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>71a79ce69548435bbad15363f4f12bd5</td>\n",
       "      <td>0.968741</td>\n",
       "      <td>0.967443</td>\n",
       "      <td>0.966234</td>\n",
       "      <td>1708.297224</td>\n",
       "      <td>31.878004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9fce8ffacd3547e2882dee9dfbbc91a0</td>\n",
       "      <td>0.961664</td>\n",
       "      <td>0.960518</td>\n",
       "      <td>0.959465</td>\n",
       "      <td>399.474642</td>\n",
       "      <td>12.866202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>986fa85a8cda41c98f6bd5dcea2882a2</td>\n",
       "      <td>0.961433</td>\n",
       "      <td>0.962198</td>\n",
       "      <td>0.962906</td>\n",
       "      <td>832.906188</td>\n",
       "      <td>23.438103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>d2e4a4567fcc4c6bb3728de64cbf6fcb</td>\n",
       "      <td>0.961228</td>\n",
       "      <td>0.959105</td>\n",
       "      <td>0.957164</td>\n",
       "      <td>106.878506</td>\n",
       "      <td>5.484337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>fa22db186d7948b5bd4923d745d28446</td>\n",
       "      <td>0.960957</td>\n",
       "      <td>0.962571</td>\n",
       "      <td>0.964068</td>\n",
       "      <td>882.601329</td>\n",
       "      <td>25.419847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>68675d6c2c204d6092c7a916381ffee1</td>\n",
       "      <td>0.960890</td>\n",
       "      <td>0.962149</td>\n",
       "      <td>0.963315</td>\n",
       "      <td>1516.290361</td>\n",
       "      <td>37.712541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>c7843120ab124f698013f8ed92a01edf</td>\n",
       "      <td>0.960883</td>\n",
       "      <td>0.962106</td>\n",
       "      <td>0.963239</td>\n",
       "      <td>1828.247472</td>\n",
       "      <td>39.341335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>354d3ba70772407ca163af9d3aac365c</td>\n",
       "      <td>0.957638</td>\n",
       "      <td>0.946203</td>\n",
       "      <td>0.936227</td>\n",
       "      <td>343.857012</td>\n",
       "      <td>12.145337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>51d6c289801849aeb016891c7d3de454</td>\n",
       "      <td>0.945718</td>\n",
       "      <td>0.951043</td>\n",
       "      <td>0.955898</td>\n",
       "      <td>1103.382241</td>\n",
       "      <td>32.429000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9b80ed73736e46c4a22e9acbfd9bcb73</td>\n",
       "      <td>0.943625</td>\n",
       "      <td>0.950026</td>\n",
       "      <td>0.955863</td>\n",
       "      <td>1059.332208</td>\n",
       "      <td>31.936845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4dccaf48ad10473fb4b7d618471bb584</td>\n",
       "      <td>0.926668</td>\n",
       "      <td>0.930481</td>\n",
       "      <td>0.933790</td>\n",
       "      <td>594.229646</td>\n",
       "      <td>23.365666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>712f20e232344f2ab19ee7faa2ffb575</td>\n",
       "      <td>0.926627</td>\n",
       "      <td>0.930457</td>\n",
       "      <td>0.933779</td>\n",
       "      <td>774.567491</td>\n",
       "      <td>25.459999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7e9d7760e43541959251d6826da9d7e6</td>\n",
       "      <td>0.926627</td>\n",
       "      <td>0.930423</td>\n",
       "      <td>0.933716</td>\n",
       "      <td>993.347172</td>\n",
       "      <td>32.663670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>c316caf4f479474887132332affc63a6</td>\n",
       "      <td>0.926484</td>\n",
       "      <td>0.930334</td>\n",
       "      <td>0.933674</td>\n",
       "      <td>114.018331</td>\n",
       "      <td>6.900334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ea6801fa4eec46a6bd106d02f4cdd820</td>\n",
       "      <td>0.926482</td>\n",
       "      <td>0.930300</td>\n",
       "      <td>0.933610</td>\n",
       "      <td>134.590074</td>\n",
       "      <td>6.088358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5558934ffe944da1947add435ab7b7bd</td>\n",
       "      <td>0.908931</td>\n",
       "      <td>0.931635</td>\n",
       "      <td>0.952161</td>\n",
       "      <td>191.084571</td>\n",
       "      <td>9.629333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3beb41195f58416f9aba00776cab682e</td>\n",
       "      <td>0.875494</td>\n",
       "      <td>0.913790</td>\n",
       "      <td>0.948111</td>\n",
       "      <td>45.455030</td>\n",
       "      <td>4.328245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              run_id  metrics.test_recall  \\\n",
       "29  eeb80c7c7f394119a8c7722b9b5d5958             0.997334   \n",
       "1   c8f35a2e4a8a44f29475d31e52ff971a             0.997325   \n",
       "5   54ccd80a68dd4ae2a125bbe009b18ff7             0.997184   \n",
       "21  d30ea74816e04f74a8cb3e41fe382e86             0.997180   \n",
       "4   1b616d4629d24a1a8de93406dee31881             0.997147   \n",
       "30  b4080d068de842ea865321414340c504             0.996830   \n",
       "23  14502ec4b1dd454a98a84be5c05c0092             0.996785   \n",
       "43  e032c7dc9b6948f18d972fb8103d5931             0.996753   \n",
       "11  7c3cd99c737d4a639d511d17c42ad72a             0.996703   \n",
       "39  8be8418856534cba8be3439a95ffd7a5             0.996477   \n",
       "26  ca31e32302e2441eb86750ba278e8266             0.996477   \n",
       "25  4099effab4374f13848f4d145071538b             0.996341   \n",
       "24  ad4e066c7823465385ddf95a7840b5c9             0.996128   \n",
       "46  81a366f3a78d4681a7fd67f9d362fc66             0.996021   \n",
       "48  c9eef2dbeb3740d6833a516140d14b84             0.995869   \n",
       "3   170c3f0330ff4f088578214d24520ba4             0.995639   \n",
       "41  e9072235d36b44f3a302fc96fc57692f             0.995246   \n",
       "10  4b96c7fa65e84b1c8a50b4378e7bd428             0.994377   \n",
       "33  4d73c21871a6432ea062e9299eee4186             0.994211   \n",
       "47  62735d6c9b6b407db5c055b625f362f4             0.993347   \n",
       "38  319a54ad1da846d2948a067cb38fa149             0.984577   \n",
       "45  286ca764d9a24f208ee820bae13fe9dd             0.982483   \n",
       "17  d2bc15a641da40deb888d8ac399f065f             0.981277   \n",
       "9   4e3a042229b54cbdad021d6ff53144b8             0.981236   \n",
       "0   430c2b95d53748b0bdf1d5c5806a48ab             0.977193   \n",
       "36  b882dbb353de4a3da61513ebd7ff194e             0.976357   \n",
       "49  07d325b504c744c98fe7bfcd0cb04648             0.973881   \n",
       "28  27e861b0489d48a7976c129cbf89b5fb             0.972567   \n",
       "32  3ee64cf7d89a43a58382a1d7db218e1b             0.971655   \n",
       "44  3efc3e31bdc84816807f0a1b2babb89b             0.971592   \n",
       "22  dd76ffadff984dafb86edc3593e60921             0.971459   \n",
       "20  4d509909568e48c3b5cbfcc7dc95f4aa             0.970992   \n",
       "2   b30594db1c344a60b54fc0df175eb8c1             0.969200   \n",
       "37  71a79ce69548435bbad15363f4f12bd5             0.968741   \n",
       "27  9fce8ffacd3547e2882dee9dfbbc91a0             0.961664   \n",
       "31  986fa85a8cda41c98f6bd5dcea2882a2             0.961433   \n",
       "7   d2e4a4567fcc4c6bb3728de64cbf6fcb             0.961228   \n",
       "15  fa22db186d7948b5bd4923d745d28446             0.960957   \n",
       "14  68675d6c2c204d6092c7a916381ffee1             0.960890   \n",
       "34  c7843120ab124f698013f8ed92a01edf             0.960883   \n",
       "42  354d3ba70772407ca163af9d3aac365c             0.957638   \n",
       "18  51d6c289801849aeb016891c7d3de454             0.945718   \n",
       "6   9b80ed73736e46c4a22e9acbfd9bcb73             0.943625   \n",
       "12  4dccaf48ad10473fb4b7d618471bb584             0.926668   \n",
       "35  712f20e232344f2ab19ee7faa2ffb575             0.926627   \n",
       "8   7e9d7760e43541959251d6826da9d7e6             0.926627   \n",
       "40  c316caf4f479474887132332affc63a6             0.926484   \n",
       "16  ea6801fa4eec46a6bd106d02f4cdd820             0.926482   \n",
       "19  5558934ffe944da1947add435ab7b7bd             0.908931   \n",
       "13  3beb41195f58416f9aba00776cab682e             0.875494   \n",
       "\n",
       "    metrics.test_accuracy  metrics.test_precision  metrics.fit_time  \\\n",
       "29               0.987045                0.977226       3932.265649   \n",
       "1                0.986971                0.977093       4525.077519   \n",
       "5                0.987440                0.978124       1322.601321   \n",
       "21               0.987333                0.977920       2764.908997   \n",
       "4                0.987307                0.977902       3455.877894   \n",
       "30               0.987304                0.978195       1530.327455   \n",
       "23               0.988251                0.980064       1696.384142   \n",
       "43               0.986983                0.977651       2068.084902   \n",
       "11               0.983594                0.971240       1430.287556   \n",
       "39               0.989563                0.982887       1165.434837   \n",
       "26               0.989563                0.982887       1177.021646   \n",
       "25               0.986542                0.977205        342.781505   \n",
       "24               0.986309                0.976946       3610.768192   \n",
       "46               0.986493                0.977401       2679.185560   \n",
       "48               0.985975                0.976547       2831.913746   \n",
       "3                0.985698                0.976230       2760.819985   \n",
       "41               0.981356                0.968346        962.500906   \n",
       "10               0.980582                0.967679       2611.283167   \n",
       "33               0.980652                0.967963       2955.809634   \n",
       "47               0.980126                0.967759       1747.536740   \n",
       "38               0.972693                0.961720       1601.735905   \n",
       "45               0.967471                0.953893         96.656987   \n",
       "17               0.974224                0.967629        234.611847   \n",
       "9                0.963512                0.947644        484.041669   \n",
       "0                0.972456                0.968022       2831.329181   \n",
       "36               0.971964                0.967853       1805.255453   \n",
       "49               0.969091                0.964644        350.599318   \n",
       "28               0.957392                0.943919       1559.345654   \n",
       "32               0.957162                0.944285       2195.295126   \n",
       "44               0.953047                0.936844        252.544006   \n",
       "22               0.956685                0.943579       1520.494840   \n",
       "20               0.955763                0.942291       1051.924028   \n",
       "2                0.967869                0.966628       2125.369897   \n",
       "37               0.967443                0.966234       1708.297224   \n",
       "27               0.960518                0.959465        399.474642   \n",
       "31               0.962198                0.962906        832.906188   \n",
       "7                0.959105                0.957164        106.878506   \n",
       "15               0.962571                0.964068        882.601329   \n",
       "14               0.962149                0.963315       1516.290361   \n",
       "34               0.962106                0.963239       1828.247472   \n",
       "42               0.946203                0.936227        343.857012   \n",
       "18               0.951043                0.955898       1103.382241   \n",
       "6                0.950026                0.955863       1059.332208   \n",
       "12               0.930481                0.933790        594.229646   \n",
       "35               0.930457                0.933779        774.567491   \n",
       "8                0.930423                0.933716        993.347172   \n",
       "40               0.930334                0.933674        114.018331   \n",
       "16               0.930300                0.933610        134.590074   \n",
       "19               0.931635                0.952161        191.084571   \n",
       "13               0.913790                0.948111         45.455030   \n",
       "\n",
       "    metrics.score_time  \n",
       "29           53.885743  \n",
       "1            58.136928  \n",
       "5            25.432360  \n",
       "21           50.770707  \n",
       "4            61.626054  \n",
       "30           28.705132  \n",
       "23           34.837516  \n",
       "43           38.239337  \n",
       "11           20.295892  \n",
       "39           17.971000  \n",
       "26           17.902369  \n",
       "25            8.099001  \n",
       "24           51.348266  \n",
       "46           51.614022  \n",
       "48           42.740001  \n",
       "3            38.086013  \n",
       "41           20.113518  \n",
       "10           37.223000  \n",
       "33           41.306533  \n",
       "47           35.358992  \n",
       "38           35.115334  \n",
       "45            4.896000  \n",
       "17            7.388015  \n",
       "9            11.753666  \n",
       "0            45.532745  \n",
       "36           39.848334  \n",
       "49           10.409004  \n",
       "28           40.480753  \n",
       "32           41.968025  \n",
       "44            7.649999  \n",
       "22           36.016013  \n",
       "20           28.776346  \n",
       "2            38.491666  \n",
       "37           31.878004  \n",
       "27           12.866202  \n",
       "31           23.438103  \n",
       "7             5.484337  \n",
       "15           25.419847  \n",
       "14           37.712541  \n",
       "34           39.341335  \n",
       "42           12.145337  \n",
       "18           32.429000  \n",
       "6            31.936845  \n",
       "12           23.365666  \n",
       "35           25.459999  \n",
       "8            32.663670  \n",
       "40            6.900334  \n",
       "16            6.088358  \n",
       "19            9.629333  \n",
       "13            4.328245  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment_id = str(experiment.experiment_id)\n",
    "runs = mlflow.search_runs(experiment_ids=experiment_id)\n",
    "runs_df = runs[['run_id',\n",
    "                'metrics.test_recall', \n",
    "                'metrics.test_accuracy', \n",
    "                'metrics.test_precision', \n",
    "                'metrics.fit_time', \n",
    "                'metrics.score_time']]\n",
    "runs_sorted = runs_df.sort_values('metrics.test_recall', ascending=False)\n",
    "runs_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e415114a",
   "metadata": {},
   "source": [
    "From this set, the top 25 models will be selected for further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "609ff6e6",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Evaluation of Models\n",
    "\n",
    "In this stage, the models generated by the previous hyperparameter search will be tested and evaluated. The selected models will use the optimized hyperparameters found in the previous stage. Performance metrics such as accuracy, recall, precision and f1 will be calculated to assess the fraud detection capability of each model. This evaluation will enable an objective comparison of the models' performance and aid in selecting the most suitable model for bank fraud detection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "78a663ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc (search_dataset, hide ,x_lim1, x_lim2):\n",
    "    f = 23\n",
    "    fig, ax = plt.subplots(1, 2)\n",
    "    fig.set_size_inches(20,10)\n",
    "    for i in range(len(search_dataset)):\n",
    "        if (i+1) not in hide:\n",
    "            fpr, tpr, thresholds = roc_curve(y_test, search_dataset.iloc[i].y_probs[:, 1])\n",
    "            ax[0].plot(fpr, tpr, label='ROC curve - Model ' + str(search_dataset.index[i]))\n",
    "            ax[1].plot(fpr, tpr, label='ROC curve - Model ' + str(search_dataset.index[i]))\n",
    "    \n",
    "    \n",
    "    ax[0].plot([0, 1], [0, 1], 'k--', label='Random classifier') \n",
    "    ax[0].set_xlim([0.0, x_lim1]) #1.0\n",
    "    ax[0].set_ylim([0.0, 1.00])\n",
    "    ax[0].set_xlabel('False Positive Rate', fontsize = f)\n",
    "    ax[0].set_ylabel('True Positive Rate', fontsize = f)\n",
    "    ax[0].set_title('ROC curve', fontsize = f+5)\n",
    "    ax[0].legend(loc=\"lower right\",  prop={'size': 20})\n",
    "    \n",
    "    ax[1].plot([0, 1], [0, 1], 'k--') \n",
    "    ax[1].set_xlim([0.0, x_lim2]) #0.05\n",
    "    ax[1].set_ylim([0.8, 1.0]) \n",
    "    ax[1].set_xlabel('False Positive Rate', fontsize = f)\n",
    "    ax[1].set_ylabel('True Positive Rate', fontsize = f)\n",
    "    ax[1].set_title('ROC curve - Zoom', fontsize = f+5)\n",
    "    ax[1].legend(loc=\"lower right\",  prop={'size': 20})\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c6bdbc",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center\">\n",
    "    <img src=\"Roc_curve.png\" alt=\"ROC\" width=\"25%\">\n",
    "</div>\n",
    "\n",
    "The Receiver Operating Characteristic (ROC) curve is a graphical representation that illustrates the performance of a binary classification model at different levels of sensitivity and specificity. It shows the relationship between the true positive rate (TPR) and the false positive rate (FPR) at various decision threshold settings. The closer the ROC curve is to the upper left corner of the graph, the better the model's performance, indicating a high ability to correctly distinguish between the positive and negative classes. The area under the ROC curve (AUC-ROC) is a common metric used to summarize the overall quality of the model, where a value close to 1 indicates a highly accurate and effective model. The ROC curve is a valuable tool for evaluating and comparing classification models in different scenarios and can assist in selecting the most suitable model for a specific classification task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7b5da24f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_params(params):\n",
    "    int_keys = ['max_depth', 'n_estimators']\n",
    "    bool_keys = ['bootstrap']\n",
    "\n",
    "    for key in int_keys:\n",
    "        if key in params:\n",
    "            params[key] = int(params[key])\n",
    "\n",
    "    for key in bool_keys:\n",
    "        if key in params:\n",
    "            if params[key].lower() == 'true':\n",
    "                params[key] = True\n",
    "            elif params[key].lower() == 'false':\n",
    "                params[key] = False\n",
    "    return params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bfe3f007",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_analysis (ids, x_train, y_train, x_test):\n",
    "    models = []\n",
    "    \n",
    "    for i in range(len(ids)):\n",
    "        run_info = mlflow.get_run(ids[i])\n",
    "        parameters = convert_params(run_info.data.params)\n",
    "        mlflow.end_run()\n",
    "        \n",
    "        rf = RandomForestClassifier(**parameters)\n",
    "        \n",
    "        t1 = datetime.now()\n",
    "        rf.fit(x_train,y_train)\n",
    "        t2 = datetime.now()\n",
    "        \n",
    "        fit_time = (t2-t1).total_seconds()\n",
    "        print('Model {} ({}) Trained. Elapsed time: {:.2f}'.format(i+1,ids[i],fit_time))   \n",
    "        \n",
    "        y_probs = rf.predict_proba(x_test)\n",
    "        fpr, tpr, thresholds = roc_curve(y_test, y_probs[:, 1])\n",
    "        y_pred = rf.predict(x_test)\n",
    "        lstMetrics = []\n",
    "        lstMetrics.append(metrics.accuracy_score(y_test, y_pred))\n",
    "        lstMetrics.append(metrics.precision_score(y_test, y_pred))\n",
    "        lstMetrics.append(metrics.recall_score(y_test, y_pred))\n",
    "        lstMetrics.append(metrics.f1_score(y_test, y_pred))\n",
    "        lstMetrics.append(fit_time)\n",
    "        \n",
    "        with mlflow.start_run(run_id=ids[i]):\n",
    "            model_path = \"{}/model\".format(ids[i])\n",
    "            mlflow.sklearn.log_model(rf, model_path)\n",
    "            mlflow.log_metric('fit_time_model', fit_time)\n",
    "            mlflow.log_metric('recall_score_model', lstMetrics[2])\n",
    "            mlflow.log_metric('precision_score_model', lstMetrics[1])\n",
    "            mlflow.log_metric('accuracy_score_model', lstMetrics[0])\n",
    "            mlflow.log_metric('f1_score_model', lstMetrics[3])\n",
    "        \n",
    "        model = {'run_id': ids[i],\n",
    "                 'fit_time': lstMetrics[4],\n",
    "                'recall_score': lstMetrics[2],\n",
    "                'precision_score': lstMetrics[1],\n",
    "                'accuracy_score': lstMetrics[0],\n",
    "                'f1_score': lstMetrics[3],\n",
    "                'roc_curve': [fpr, tpr, thresholds],\n",
    "                'y_pred': y_pred,\n",
    "                'y_probs': y_probs}\n",
    "        models.append(model)\n",
    "    return models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85608923",
   "metadata": {},
   "source": [
    "In the model_analysis function, an analysis of the models passed as parameters is performed using the test data. Firstly, the parameters of each model are retrieved based on their respective IDs. Next, the model is trained using the training data, and the training time is recorded. The trained model is stored in MLflow.\n",
    "\n",
    "Subsequently, predictions are made using the model, and various performance metrics, including accuracy, precision, recall, and F1 score, are calculated. Additionally, the ROC curve is computed to assess the model's performance at different probability thresholds. The predictions and probabilities are stored along with the model information.\n",
    "\n",
    "Finally, the function returns a list of models with their respective information, enabling a detailed analysis of each model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ad03f470",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 1 (eeb80c7c7f394119a8c7722b9b5d5958) Trained. Elapsed time: 5571.17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lucas\\anaconda3\\lib\\site-packages\\_distutils_hack\\__init__.py:33: UserWarning: Setuptools is replacing distutils.\n",
      "  warnings.warn(\"Setuptools is replacing distutils.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 2 (c8f35a2e4a8a44f29475d31e52ff971a) Trained. Elapsed time: 6013.35\n",
      "Model 3 (54ccd80a68dd4ae2a125bbe009b18ff7) Trained. Elapsed time: 1881.28\n",
      "Model 4 (d30ea74816e04f74a8cb3e41fe382e86) Trained. Elapsed time: 4137.40\n",
      "Model 5 (1b616d4629d24a1a8de93406dee31881) Trained. Elapsed time: 4413.81\n",
      "Model 6 (b4080d068de842ea865321414340c504) Trained. Elapsed time: 2130.55\n",
      "Model 7 (14502ec4b1dd454a98a84be5c05c0092) Trained. Elapsed time: 2216.57\n",
      "Model 8 (e032c7dc9b6948f18d972fb8103d5931) Trained. Elapsed time: 2925.61\n",
      "Model 9 (7c3cd99c737d4a639d511d17c42ad72a) Trained. Elapsed time: 2049.16\n",
      "Model 10 (8be8418856534cba8be3439a95ffd7a5) Trained. Elapsed time: 1658.77\n",
      "Model 11 (ca31e32302e2441eb86750ba278e8266) Trained. Elapsed time: 1730.72\n",
      "Model 12 (4099effab4374f13848f4d145071538b) Trained. Elapsed time: 482.21\n",
      "Model 13 (ad4e066c7823465385ddf95a7840b5c9) Trained. Elapsed time: 5009.47\n",
      "Model 14 (81a366f3a78d4681a7fd67f9d362fc66) Trained. Elapsed time: 3869.53\n",
      "Model 15 (c9eef2dbeb3740d6833a516140d14b84) Trained. Elapsed time: 4265.20\n",
      "Model 16 (170c3f0330ff4f088578214d24520ba4) Trained. Elapsed time: 3761.43\n",
      "Model 17 (e9072235d36b44f3a302fc96fc57692f) Trained. Elapsed time: 1402.97\n",
      "Model 18 (4b96c7fa65e84b1c8a50b4378e7bd428) Trained. Elapsed time: 3994.23\n",
      "Model 19 (4d73c21871a6432ea062e9299eee4186) Trained. Elapsed time: 4279.29\n",
      "Model 20 (62735d6c9b6b407db5c055b625f362f4) Trained. Elapsed time: 2650.42\n",
      "Model 21 (319a54ad1da846d2948a067cb38fa149) Trained. Elapsed time: 2408.11\n",
      "Model 22 (286ca764d9a24f208ee820bae13fe9dd) Trained. Elapsed time: 129.20\n",
      "Model 23 (d2bc15a641da40deb888d8ac399f065f) Trained. Elapsed time: 347.28\n",
      "Model 24 (4e3a042229b54cbdad021d6ff53144b8) Trained. Elapsed time: 687.47\n",
      "Model 25 (430c2b95d53748b0bdf1d5c5806a48ab) Trained. Elapsed time: 4135.85\n"
     ]
    }
   ],
   "source": [
    "ids = runs_sorted.iloc[:25]['run_id'].values\n",
    "\n",
    "mlist = model_analysis(ids, x_resampled, y_resampled, x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89048fb6",
   "metadata": {},
   "source": [
    "### Final Evaluation and Model Choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fcb2faf7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>run_id</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>recall_score</th>\n",
       "      <th>precision_score</th>\n",
       "      <th>accuracy_score</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>True Negative</th>\n",
       "      <th>False Negative</th>\n",
       "      <th>False Positive</th>\n",
       "      <th>True Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eeb80c7c7f394119a8c7722b9b5d5958</td>\n",
       "      <td>5571.173582</td>\n",
       "      <td>0.992966</td>\n",
       "      <td>0.047745</td>\n",
       "      <td>0.974914</td>\n",
       "      <td>0.091109</td>\n",
       "      <td>1858502</td>\n",
       "      <td>47867</td>\n",
       "      <td>17</td>\n",
       "      <td>2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c8f35a2e4a8a44f29475d31e52ff971a</td>\n",
       "      <td>6013.351638</td>\n",
       "      <td>0.992966</td>\n",
       "      <td>0.050161</td>\n",
       "      <td>0.976182</td>\n",
       "      <td>0.095498</td>\n",
       "      <td>1860923</td>\n",
       "      <td>45446</td>\n",
       "      <td>17</td>\n",
       "      <td>2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54ccd80a68dd4ae2a125bbe009b18ff7</td>\n",
       "      <td>1881.281638</td>\n",
       "      <td>0.992139</td>\n",
       "      <td>0.054304</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>0.102971</td>\n",
       "      <td>1864608</td>\n",
       "      <td>41761</td>\n",
       "      <td>19</td>\n",
       "      <td>2398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d30ea74816e04f74a8cb3e41fe382e86</td>\n",
       "      <td>4137.396019</td>\n",
       "      <td>0.992553</td>\n",
       "      <td>0.051335</td>\n",
       "      <td>0.976765</td>\n",
       "      <td>0.097622</td>\n",
       "      <td>1862036</td>\n",
       "      <td>44333</td>\n",
       "      <td>18</td>\n",
       "      <td>2399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1b616d4629d24a1a8de93406dee31881</td>\n",
       "      <td>4413.810955</td>\n",
       "      <td>0.993380</td>\n",
       "      <td>0.049088</td>\n",
       "      <td>0.975625</td>\n",
       "      <td>0.093553</td>\n",
       "      <td>1859858</td>\n",
       "      <td>46511</td>\n",
       "      <td>16</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b4080d068de842ea865321414340c504</td>\n",
       "      <td>2130.549004</td>\n",
       "      <td>0.992966</td>\n",
       "      <td>0.051086</td>\n",
       "      <td>0.976636</td>\n",
       "      <td>0.097172</td>\n",
       "      <td>1861789</td>\n",
       "      <td>44580</td>\n",
       "      <td>17</td>\n",
       "      <td>2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14502ec4b1dd454a98a84be5c05c0092</td>\n",
       "      <td>2216.567490</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>0.066931</td>\n",
       "      <td>0.982510</td>\n",
       "      <td>0.125386</td>\n",
       "      <td>1873009</td>\n",
       "      <td>33360</td>\n",
       "      <td>24</td>\n",
       "      <td>2393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>e032c7dc9b6948f18d972fb8103d5931</td>\n",
       "      <td>2925.612942</td>\n",
       "      <td>0.992553</td>\n",
       "      <td>0.049390</td>\n",
       "      <td>0.975800</td>\n",
       "      <td>0.094097</td>\n",
       "      <td>1860195</td>\n",
       "      <td>46174</td>\n",
       "      <td>18</td>\n",
       "      <td>2399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7c3cd99c737d4a639d511d17c42ad72a</td>\n",
       "      <td>2049.160167</td>\n",
       "      <td>0.993794</td>\n",
       "      <td>0.041079</td>\n",
       "      <td>0.970617</td>\n",
       "      <td>0.078896</td>\n",
       "      <td>1850298</td>\n",
       "      <td>56071</td>\n",
       "      <td>15</td>\n",
       "      <td>2402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8be8418856534cba8be3439a95ffd7a5</td>\n",
       "      <td>1658.774874</td>\n",
       "      <td>0.989657</td>\n",
       "      <td>0.072100</td>\n",
       "      <td>0.983859</td>\n",
       "      <td>0.134408</td>\n",
       "      <td>1875585</td>\n",
       "      <td>30784</td>\n",
       "      <td>25</td>\n",
       "      <td>2392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ca31e32302e2441eb86750ba278e8266</td>\n",
       "      <td>1730.720227</td>\n",
       "      <td>0.990484</td>\n",
       "      <td>0.049329</td>\n",
       "      <td>0.975817</td>\n",
       "      <td>0.093978</td>\n",
       "      <td>1860232</td>\n",
       "      <td>46137</td>\n",
       "      <td>23</td>\n",
       "      <td>2394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4099effab4374f13848f4d145071538b</td>\n",
       "      <td>482.209913</td>\n",
       "      <td>0.993794</td>\n",
       "      <td>0.056910</td>\n",
       "      <td>0.979139</td>\n",
       "      <td>0.107655</td>\n",
       "      <td>1866564</td>\n",
       "      <td>39805</td>\n",
       "      <td>15</td>\n",
       "      <td>2402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ad4e066c7823465385ddf95a7840b5c9</td>\n",
       "      <td>5009.465160</td>\n",
       "      <td>0.993380</td>\n",
       "      <td>0.051305</td>\n",
       "      <td>0.976732</td>\n",
       "      <td>0.097570</td>\n",
       "      <td>1861971</td>\n",
       "      <td>44398</td>\n",
       "      <td>16</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>81a366f3a78d4681a7fd67f9d362fc66</td>\n",
       "      <td>3869.531529</td>\n",
       "      <td>0.991312</td>\n",
       "      <td>0.047848</td>\n",
       "      <td>0.975010</td>\n",
       "      <td>0.091290</td>\n",
       "      <td>1858690</td>\n",
       "      <td>47679</td>\n",
       "      <td>21</td>\n",
       "      <td>2396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>c9eef2dbeb3740d6833a516140d14b84</td>\n",
       "      <td>4265.200782</td>\n",
       "      <td>0.992966</td>\n",
       "      <td>0.046540</td>\n",
       "      <td>0.974232</td>\n",
       "      <td>0.088914</td>\n",
       "      <td>1857201</td>\n",
       "      <td>49168</td>\n",
       "      <td>17</td>\n",
       "      <td>2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>170c3f0330ff4f088578214d24520ba4</td>\n",
       "      <td>3761.426711</td>\n",
       "      <td>0.988002</td>\n",
       "      <td>0.053740</td>\n",
       "      <td>0.977956</td>\n",
       "      <td>0.101936</td>\n",
       "      <td>1864321</td>\n",
       "      <td>42048</td>\n",
       "      <td>29</td>\n",
       "      <td>2388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>e9072235d36b44f3a302fc96fc57692f</td>\n",
       "      <td>1402.967768</td>\n",
       "      <td>0.980968</td>\n",
       "      <td>0.034249</td>\n",
       "      <td>0.964949</td>\n",
       "      <td>0.066187</td>\n",
       "      <td>1839511</td>\n",
       "      <td>66858</td>\n",
       "      <td>46</td>\n",
       "      <td>2371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4b96c7fa65e84b1c8a50b4378e7bd428</td>\n",
       "      <td>3994.227549</td>\n",
       "      <td>0.994621</td>\n",
       "      <td>0.033160</td>\n",
       "      <td>0.963272</td>\n",
       "      <td>0.064181</td>\n",
       "      <td>1836277</td>\n",
       "      <td>70092</td>\n",
       "      <td>13</td>\n",
       "      <td>2404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4d73c21871a6432ea062e9299eee4186</td>\n",
       "      <td>4279.286942</td>\n",
       "      <td>0.988002</td>\n",
       "      <td>0.037650</td>\n",
       "      <td>0.968007</td>\n",
       "      <td>0.072536</td>\n",
       "      <td>1845331</td>\n",
       "      <td>61038</td>\n",
       "      <td>29</td>\n",
       "      <td>2388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>62735d6c9b6b407db5c055b625f362f4</td>\n",
       "      <td>2650.419579</td>\n",
       "      <td>0.983037</td>\n",
       "      <td>0.036764</td>\n",
       "      <td>0.967365</td>\n",
       "      <td>0.070877</td>\n",
       "      <td>1844116</td>\n",
       "      <td>62253</td>\n",
       "      <td>41</td>\n",
       "      <td>2376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>319a54ad1da846d2948a067cb38fa149</td>\n",
       "      <td>2408.112351</td>\n",
       "      <td>0.983864</td>\n",
       "      <td>0.031401</td>\n",
       "      <td>0.961551</td>\n",
       "      <td>0.060860</td>\n",
       "      <td>1833018</td>\n",
       "      <td>73351</td>\n",
       "      <td>39</td>\n",
       "      <td>2378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>286ca764d9a24f208ee820bae13fe9dd</td>\n",
       "      <td>129.202968</td>\n",
       "      <td>0.983037</td>\n",
       "      <td>0.022634</td>\n",
       "      <td>0.946228</td>\n",
       "      <td>0.044249</td>\n",
       "      <td>1803770</td>\n",
       "      <td>102599</td>\n",
       "      <td>41</td>\n",
       "      <td>2376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>d2bc15a641da40deb888d8ac399f065f</td>\n",
       "      <td>347.283866</td>\n",
       "      <td>0.978072</td>\n",
       "      <td>0.035294</td>\n",
       "      <td>0.966120</td>\n",
       "      <td>0.068129</td>\n",
       "      <td>1841752</td>\n",
       "      <td>64617</td>\n",
       "      <td>53</td>\n",
       "      <td>2364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4e3a042229b54cbdad021d6ff53144b8</td>\n",
       "      <td>687.466933</td>\n",
       "      <td>0.976003</td>\n",
       "      <td>0.026459</td>\n",
       "      <td>0.954497</td>\n",
       "      <td>0.051521</td>\n",
       "      <td>1819571</td>\n",
       "      <td>86798</td>\n",
       "      <td>58</td>\n",
       "      <td>2359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>430c2b95d53748b0bdf1d5c5806a48ab</td>\n",
       "      <td>4135.846948</td>\n",
       "      <td>0.973521</td>\n",
       "      <td>0.036793</td>\n",
       "      <td>0.967695</td>\n",
       "      <td>0.070906</td>\n",
       "      <td>1844769</td>\n",
       "      <td>61600</td>\n",
       "      <td>64</td>\n",
       "      <td>2353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              run_id     fit_time  recall_score  \\\n",
       "1   eeb80c7c7f394119a8c7722b9b5d5958  5571.173582      0.992966   \n",
       "2   c8f35a2e4a8a44f29475d31e52ff971a  6013.351638      0.992966   \n",
       "3   54ccd80a68dd4ae2a125bbe009b18ff7  1881.281638      0.992139   \n",
       "4   d30ea74816e04f74a8cb3e41fe382e86  4137.396019      0.992553   \n",
       "5   1b616d4629d24a1a8de93406dee31881  4413.810955      0.993380   \n",
       "6   b4080d068de842ea865321414340c504  2130.549004      0.992966   \n",
       "7   14502ec4b1dd454a98a84be5c05c0092  2216.567490      0.990070   \n",
       "8   e032c7dc9b6948f18d972fb8103d5931  2925.612942      0.992553   \n",
       "9   7c3cd99c737d4a639d511d17c42ad72a  2049.160167      0.993794   \n",
       "10  8be8418856534cba8be3439a95ffd7a5  1658.774874      0.989657   \n",
       "11  ca31e32302e2441eb86750ba278e8266  1730.720227      0.990484   \n",
       "12  4099effab4374f13848f4d145071538b   482.209913      0.993794   \n",
       "13  ad4e066c7823465385ddf95a7840b5c9  5009.465160      0.993380   \n",
       "14  81a366f3a78d4681a7fd67f9d362fc66  3869.531529      0.991312   \n",
       "15  c9eef2dbeb3740d6833a516140d14b84  4265.200782      0.992966   \n",
       "16  170c3f0330ff4f088578214d24520ba4  3761.426711      0.988002   \n",
       "17  e9072235d36b44f3a302fc96fc57692f  1402.967768      0.980968   \n",
       "18  4b96c7fa65e84b1c8a50b4378e7bd428  3994.227549      0.994621   \n",
       "19  4d73c21871a6432ea062e9299eee4186  4279.286942      0.988002   \n",
       "20  62735d6c9b6b407db5c055b625f362f4  2650.419579      0.983037   \n",
       "21  319a54ad1da846d2948a067cb38fa149  2408.112351      0.983864   \n",
       "22  286ca764d9a24f208ee820bae13fe9dd   129.202968      0.983037   \n",
       "23  d2bc15a641da40deb888d8ac399f065f   347.283866      0.978072   \n",
       "24  4e3a042229b54cbdad021d6ff53144b8   687.466933      0.976003   \n",
       "25  430c2b95d53748b0bdf1d5c5806a48ab  4135.846948      0.973521   \n",
       "\n",
       "    precision_score  accuracy_score  f1_score  True Negative  False Negative  \\\n",
       "1          0.047745        0.974914  0.091109        1858502           47867   \n",
       "2          0.050161        0.976182  0.095498        1860923           45446   \n",
       "3          0.054304        0.978112  0.102971        1864608           41761   \n",
       "4          0.051335        0.976765  0.097622        1862036           44333   \n",
       "5          0.049088        0.975625  0.093553        1859858           46511   \n",
       "6          0.051086        0.976636  0.097172        1861789           44580   \n",
       "7          0.066931        0.982510  0.125386        1873009           33360   \n",
       "8          0.049390        0.975800  0.094097        1860195           46174   \n",
       "9          0.041079        0.970617  0.078896        1850298           56071   \n",
       "10         0.072100        0.983859  0.134408        1875585           30784   \n",
       "11         0.049329        0.975817  0.093978        1860232           46137   \n",
       "12         0.056910        0.979139  0.107655        1866564           39805   \n",
       "13         0.051305        0.976732  0.097570        1861971           44398   \n",
       "14         0.047848        0.975010  0.091290        1858690           47679   \n",
       "15         0.046540        0.974232  0.088914        1857201           49168   \n",
       "16         0.053740        0.977956  0.101936        1864321           42048   \n",
       "17         0.034249        0.964949  0.066187        1839511           66858   \n",
       "18         0.033160        0.963272  0.064181        1836277           70092   \n",
       "19         0.037650        0.968007  0.072536        1845331           61038   \n",
       "20         0.036764        0.967365  0.070877        1844116           62253   \n",
       "21         0.031401        0.961551  0.060860        1833018           73351   \n",
       "22         0.022634        0.946228  0.044249        1803770          102599   \n",
       "23         0.035294        0.966120  0.068129        1841752           64617   \n",
       "24         0.026459        0.954497  0.051521        1819571           86798   \n",
       "25         0.036793        0.967695  0.070906        1844769           61600   \n",
       "\n",
       "    False Positive  True Positive  \n",
       "1               17           2400  \n",
       "2               17           2400  \n",
       "3               19           2398  \n",
       "4               18           2399  \n",
       "5               16           2401  \n",
       "6               17           2400  \n",
       "7               24           2393  \n",
       "8               18           2399  \n",
       "9               15           2402  \n",
       "10              25           2392  \n",
       "11              23           2394  \n",
       "12              15           2402  \n",
       "13              16           2401  \n",
       "14              21           2396  \n",
       "15              17           2400  \n",
       "16              29           2388  \n",
       "17              46           2371  \n",
       "18              13           2404  \n",
       "19              29           2388  \n",
       "20              41           2376  \n",
       "21              39           2378  \n",
       "22              41           2376  \n",
       "23              53           2364  \n",
       "24              58           2359  \n",
       "25              64           2353  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_mlist = pd.DataFrame(mlist)\n",
    "df_mlist.rename(index=lambda x: x + 1, inplace=True)\n",
    "\n",
    "for i in range(len(df_mlist)):\n",
    "    cm = confusion_matrix(y_test, df_mlist.iloc[i].y_pred)\n",
    "    df_mlist.loc[i+1, 'True Negative'] = cm[0][0]\n",
    "    df_mlist.loc[i+1, 'False Negative'] = cm[0][1]\n",
    "    df_mlist.loc[i+1, 'False Positive'] = cm[1][0]\n",
    "    df_mlist.loc[i+1, 'True Positive'] = cm[1][1]\n",
    "    \n",
    "df_mlist['True Negative'] = df_mlist['True Negative'].astype(int)\n",
    "df_mlist['False Negative'] = df_mlist['False Negative'].astype(int)\n",
    "df_mlist['False Positive'] = df_mlist['False Positive'].astype(int)\n",
    "df_mlist['True Positive'] = df_mlist['True Positive'].astype(int)\n",
    "display(df_mlist[['run_id','fit_time','recall_score','precision_score','accuracy_score','f1_score',\n",
    "                  'True Negative','False Negative','False Positive','True Positive']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "be63b6bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>run_id</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>recall_score</th>\n",
       "      <th>precision_score</th>\n",
       "      <th>accuracy_score</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>True Negative</th>\n",
       "      <th>False Negative</th>\n",
       "      <th>False Positive</th>\n",
       "      <th>True Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4b96c7fa65e84b1c8a50b4378e7bd428</td>\n",
       "      <td>3994.227549</td>\n",
       "      <td>0.994621</td>\n",
       "      <td>0.03316</td>\n",
       "      <td>0.963272</td>\n",
       "      <td>0.064181</td>\n",
       "      <td>1836277</td>\n",
       "      <td>70092</td>\n",
       "      <td>13</td>\n",
       "      <td>2404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8be8418856534cba8be3439a95ffd7a5</td>\n",
       "      <td>1658.774874</td>\n",
       "      <td>0.989657</td>\n",
       "      <td>0.07210</td>\n",
       "      <td>0.983859</td>\n",
       "      <td>0.134408</td>\n",
       "      <td>1875585</td>\n",
       "      <td>30784</td>\n",
       "      <td>25</td>\n",
       "      <td>2392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4099effab4374f13848f4d145071538b</td>\n",
       "      <td>482.209913</td>\n",
       "      <td>0.993794</td>\n",
       "      <td>0.05691</td>\n",
       "      <td>0.979139</td>\n",
       "      <td>0.107655</td>\n",
       "      <td>1866564</td>\n",
       "      <td>39805</td>\n",
       "      <td>15</td>\n",
       "      <td>2402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              run_id     fit_time  recall_score  \\\n",
       "18  4b96c7fa65e84b1c8a50b4378e7bd428  3994.227549      0.994621   \n",
       "10  8be8418856534cba8be3439a95ffd7a5  1658.774874      0.989657   \n",
       "12  4099effab4374f13848f4d145071538b   482.209913      0.993794   \n",
       "\n",
       "    precision_score  accuracy_score  f1_score  True Negative  False Negative  \\\n",
       "18          0.03316        0.963272  0.064181        1836277           70092   \n",
       "10          0.07210        0.983859  0.134408        1875585           30784   \n",
       "12          0.05691        0.979139  0.107655        1866564           39805   \n",
       "\n",
       "    False Positive  True Positive  \n",
       "18              13           2404  \n",
       "10              25           2392  \n",
       "12              15           2402  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mds = [18, 10, 12]\n",
    "mds = [n - 1 for n in mds]\n",
    "sel = df_mlist.iloc[mds,:][['run_id','fit_time','recall_score','precision_score','accuracy_score','f1_score',\n",
    "                  'True Negative','False Negative','False Positive','True Positive']]\n",
    "sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0d4b9ec3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hide = [] # to hide models\n",
    "plot_roc(df_mlist.iloc[mds,:], hide, 1.0, 0.115)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79a4c794",
   "metadata": {},
   "source": [
    "### Top-Performing Models\n",
    "\n",
    "Model <b>4b96c7fa65e84b1c8a50b4378e7bd428</b>: This model was chosen for having the lowest number of False Positives and the highest number of True Positives among all the models considered. This means it is very good at correctly identifying frauds. Although the precision of this model is not the highest among the models considered, its performance in terms of TP and FP is remarkable and could be more valuable in a fraud detection context, where correctly identifying frauds and minimizing incorrect fraud identifications are of great importance.\n",
    "\n",
    "Model <b>8be8418856534cba8be3439a95ffd7a5</b>: This model was chosen for having the highest precision and the highest F1-Score among all the models considered. High precision means that a large proportion of the fraud identifications made by the model are correct, while a high F1-Score indicates a good balance between precision and recall. Additionally, this model has a relatively low fit time, indicating good computational efficiency.\n",
    "\n",
    "Model <b>4099effab4374f13848f4d145071538b</b>: This model was chosen as it showcases an impressive balance across multiple metrics. Notably, it has the lowest fit time among the selected models, indicating excellent computational efficiency. This can be a critical factor in operational environments where time is a valuable resource. Additionally, the model presents a high number of True Positives and a relatively low number of False Negatives, indicating strong performance in correctly identifying frauds and minimizing missed frauds. Precision and F1-Score are reasonably high, indicating a good balance between precision and recall. Moreover, when compared to the other two selected models through the ROC curve, this model performed the best, further highlighting its strong performance in the fraud detection task.\n",
    "\n",
    "These choices reflect an attempt to balance computational efficiency and effectiveness in fraud detection, considering several important metrics. Each model presents strengths in different areas, and together they offer a robust set of tools for fraud detection."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e84f530",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Loading Top-Performing Models from MLflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d10073d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_mlflow_model(model_id, experiment_id):\n",
    "    with mlflow.start_run(run_id=model_id):\n",
    "        run_info = mlflow.get_run(model_id)\n",
    "        artifact_uri = run_info.info.artifact_uri\n",
    "        path = 'mlartifacts/' + experiment_id + '/'+ model_id + '/artifacts/' + model_id + '/model'\n",
    "        model = mlflow.sklearn.load_model(path)\n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "dd160eb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Accuracy Score  Precision Score  Recall Score  F1 Score\n",
      "       0.963272          0.03316      0.994621  0.064181\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_id = df_mlist.iloc[mds[0]].run_id\n",
    "experiment_id = str(experiment.experiment_id)\n",
    "\n",
    "model_metrics(load_mlflow_model(model_id, experiment_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "4ef8075f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Accuracy Score  Precision Score  Recall Score  F1 Score\n",
      "       0.983859           0.0721      0.989657  0.134408\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_id = df_mlist.iloc[mds[1]].run_id\n",
    "experiment_id = str(experiment.experiment_id)\n",
    "\n",
    "model_metrics(load_mlflow_model(model_id, experiment_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "678843d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Accuracy Score  Precision Score  Recall Score  F1 Score\n",
      "       0.979139          0.05691      0.993794  0.107655\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_id = df_mlist.iloc[mds[2]].run_id\n",
    "experiment_id = str(experiment.experiment_id)\n",
    "\n",
    "model_metrics(load_mlflow_model(model_id, experiment_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bd076d6",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Throughout this project, advanced machine learning techniques were employed to build effective models for bank fraud detection. Three models were selected, each showcasing unique strengths in different areas, providing a robust set of tools for the task.\n",
    "\n",
    "The model <b>4b96c7fa65e84b1c8a50b4378e7bd428</b> excels in correctly identifying frauds (highest number of True Positives). This capability is of paramount importance in a fraud detection context. However, it should be noted that this model also presents a relatively high number of False Negatives. This could lead to potential inconveniences for customers, as some legitimate transactions might be flagged as fraudulent. Despite this, the model's superior performance in maximizing True Positives and minimizing False Positives is invaluable.\n",
    "\n",
    "The model <b>8be8418856534cba8be3439a95ffd7a5</b> stands out with the highest precision and the highest F1-Score among all models considered. High precision indicates that a large proportion of fraud identifications made by this model are indeed correct, reducing the risk of false alarms and causing the least number of inconveniences to customers among the chosen models. However, it should be noted that this model allows for a slightly higher number of frauds to be classified as non-fraud, as indicated by a slight increase in the number of False Positives. The high F1-Score, however, indicates a good balance between precision (how many identified frauds are actual frauds) and recall (how many actual frauds are identified), which is an important attribute in the context of fraud detection.\n",
    "\n",
    "The model <b>4099effab4374f13848f4d145071538b</b> demonstrates an impressive balance across several key metrics. Most importantly, it has the shortest fit time among the selected models, highlighting its superior computational efficiency, an attribute that becomes increasingly important in real-world applications where time and computational resources are critical. Furthermore, the model presents a high number of True Positives and a relatively low number of False Negatives, indicating strong performance in correctly identifying frauds and minimizing missed frauds.\n",
    "\n",
    "The results of this project highlight the utility of machine learning techniques in the realm of bank fraud detection. This work demonstrates how different models can have their unique strengths in different areas, providing a robust set of tools for the task at hand. Even though there is a possibility that fraud patterns may change over time, this project serves as a solid foundation for understanding how to develop and evaluate fraud detection models. Through this study, valuable insights were gained into the application of machine learning in bank fraud detection."
   ]
  }
 ],
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