{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data manipulation and analysis\n",
    "import pandas as pd\n",
    "import time\n",
    "import numpy as np\n",
    "pd.set_option('display.max_columns', None)  # Show all columns\n",
    "# Suppress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "# Visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Machine Learning Libraries\n",
    "import xgboost as xgb\n",
    "# from tensorflow import keras  # Neural Network\n",
    "\n",
    "# Sample more heavily from the edge fraud cases\n",
    "# from sklearn.utils import resample\n",
    "\n",
    "# from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import lightgbm as lgb\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "# Metrics for model evaluation\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, precision_recall_curve, accuracy_score, f1_score, auc, roc_curve, precision_score, recall_score, average_precision_score\n",
    "# Data preprocessing\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler  # Feature scaling\n",
    "from sklearn.preprocessing import LabelEncoder    # Categorical encoding\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# Load the dataset\n",
    "# Assuming your dataset is in CSV format and in the same directory as the notebook\n",
    "df = pd.read_csv('PS_20174392719_1491204439457_log.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Info:\n",
      "--------------------------------------------------\n",
      "<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": [
    "# Display basic information about the dataset\n",
    "print(\"Dataset Info:\")\n",
    "print(\"-\" * 50)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First few rows of the dataset:\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "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>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>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>M1979787155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>M2044282225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C553264065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C38997010</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</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>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>M1230701703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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       170136.0       160296.36   \n",
       "1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n",
       "2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n",
       "3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n",
       "4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n",
       "\n",
       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
       "0  M1979787155             0.0             0.0        0               0  \n",
       "1  M2044282225             0.0             0.0        0               0  \n",
       "2   C553264065             0.0             0.0        1               0  \n",
       "3    C38997010         21182.0             0.0        1               0  \n",
       "4  M1230701703             0.0             0.0        0               0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\nFirst few rows of the dataset:\")\n",
    "print(\"-\" * 50)\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Basic statistics of numerical columns:\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "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>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.433972e+02</td>\n",
       "      <td>1.798619e+05</td>\n",
       "      <td>8.338831e+05</td>\n",
       "      <td>8.551137e+05</td>\n",
       "      <td>1.100702e+06</td>\n",
       "      <td>1.224996e+06</td>\n",
       "      <td>1.290820e-03</td>\n",
       "      <td>2.514687e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.423320e+02</td>\n",
       "      <td>6.038582e+05</td>\n",
       "      <td>2.888243e+06</td>\n",
       "      <td>2.924049e+06</td>\n",
       "      <td>3.399180e+06</td>\n",
       "      <td>3.674129e+06</td>\n",
       "      <td>3.590480e-02</td>\n",
       "      <td>1.585775e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.560000e+02</td>\n",
       "      <td>1.338957e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.390000e+02</td>\n",
       "      <td>7.487194e+04</td>\n",
       "      <td>1.420800e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.327057e+05</td>\n",
       "      <td>2.146614e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.350000e+02</td>\n",
       "      <td>2.087215e+05</td>\n",
       "      <td>1.073152e+05</td>\n",
       "      <td>1.442584e+05</td>\n",
       "      <td>9.430367e+05</td>\n",
       "      <td>1.111909e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.430000e+02</td>\n",
       "      <td>9.244552e+07</td>\n",
       "      <td>5.958504e+07</td>\n",
       "      <td>4.958504e+07</td>\n",
       "      <td>3.560159e+08</td>\n",
       "      <td>3.561793e+08</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
       "count  6.362620e+06  6.362620e+06   6.362620e+06    6.362620e+06   \n",
       "mean   2.433972e+02  1.798619e+05   8.338831e+05    8.551137e+05   \n",
       "std    1.423320e+02  6.038582e+05   2.888243e+06    2.924049e+06   \n",
       "min    1.000000e+00  0.000000e+00   0.000000e+00    0.000000e+00   \n",
       "25%    1.560000e+02  1.338957e+04   0.000000e+00    0.000000e+00   \n",
       "50%    2.390000e+02  7.487194e+04   1.420800e+04    0.000000e+00   \n",
       "75%    3.350000e+02  2.087215e+05   1.073152e+05    1.442584e+05   \n",
       "max    7.430000e+02  9.244552e+07   5.958504e+07    4.958504e+07   \n",
       "\n",
       "       oldbalanceDest  newbalanceDest       isFraud  isFlaggedFraud  \n",
       "count    6.362620e+06    6.362620e+06  6.362620e+06    6.362620e+06  \n",
       "mean     1.100702e+06    1.224996e+06  1.290820e-03    2.514687e-06  \n",
       "std      3.399180e+06    3.674129e+06  3.590480e-02    1.585775e-03  \n",
       "min      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
       "25%      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
       "50%      1.327057e+05    2.146614e+05  0.000000e+00    0.000000e+00  \n",
       "75%      9.430367e+05    1.111909e+06  0.000000e+00    0.000000e+00  \n",
       "max      3.560159e+08    3.561793e+08  1.000000e+00    1.000000e+00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\nBasic statistics of numerical columns:\")\n",
    "print(\"-\" * 50)\n",
    "display(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isFraud\n",
       "0    6354407\n",
       "1       8213\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#EDA\n",
    "# Check class imbalance for fraud detection\n",
    "df['isFraud'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the value counts\n",
    "fraud_counts = df['isFraud'].value_counts()\n",
    "\n",
    "# Define labels and colors\n",
    "labels = ['Non-Fraud (0)', 'Fraud (1)']\n",
    "colors = ['skyblue', 'salmon']\n",
    "\n",
    "# Create the pie chart\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(fraud_counts.values, labels=labels, colors=colors, \n",
    "        autopct=lambda p: f'{p:.2f}%\\n({int(p * sum(fraud_counts.values) / 100):,})',\n",
    "        startangle=90, textprops={'fontsize': 12, 'weight': 'bold'})\n",
    "\n",
    "# Add title\n",
    "plt.title('Class Distribution: Fraud vs. Non-Fraud', fontsize=16)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fraud Distribution:\n",
      "isFraud\n",
      "0    99.870918\n",
      "1     0.129082\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Flagged Fraud Distribution:\n",
      "isFlaggedFraud\n",
      "0    99.999749\n",
      "1     0.000251\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nFraud Distribution:\")\n",
    "print(df['isFraud'].value_counts(normalize=True) * 100)\n",
    "print(\"\\nFlagged Fraud Distribution:\")\n",
    "print(df['isFlaggedFraud'].value_counts(normalize=True) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Transaction Types:\n",
      "type\n",
      "CASH_OUT    2237500\n",
      "PAYMENT     2151495\n",
      "CASH_IN     1399284\n",
      "TRANSFER     532909\n",
      "DEBIT         41432\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# TRANSACTION TYPE ANALYSIS\n",
    "print(\"\\nTransaction Types:\")\n",
    "print(df['type'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fraud Percentage by Transaction Type:\n",
      "type\n",
      "CASH_IN     0.000000\n",
      "CASH_OUT    0.183955\n",
      "DEBIT       0.000000\n",
      "PAYMENT     0.000000\n",
      "TRANSFER    0.768799\n",
      "Name: isFraud, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 1. Transaction Type and Fraud\n",
    "plt.figure(figsize=(12, 5))\n",
    "sns.countplot(x='type', hue='isFraud', data=df)\n",
    "plt.title('Transaction Types and Fraud')\n",
    "plt.yscale('log')  # Log scale because of class imbalance\n",
    "plt.legend(['Legitimate', 'Fraud'])\n",
    "plt.tight_layout()\n",
    "plt.savefig('transaction_type_fraud.png')\n",
    "plt.show()\n",
    "# 1. Check fraud distribution by transaction type\n",
    "fraud_by_type = df.groupby('type')['isFraud'].mean() * 100\n",
    "print(\"\\nFraud Percentage by Transaction Type:\")\n",
    "print(fraud_by_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>type</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>4116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>4097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       type  isFraud\n",
       "0  CASH_OUT     4116\n",
       "1  TRANSFER     4097"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# analysing fraud through transaction type\n",
    "fraud_df=df[df['isFraud']==1].groupby('type')['isFraud'].sum().reset_index()\n",
    "fraud_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(fraud_df['isFraud'],labels=fraud_df['type'], autopct='%1.1f%%', startangle=90)\n",
    "plt.title('Distribution of Fraud in Different transaction type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_cols=['amount','oldbalanceOrg','newbalanceOrig','newbalanceDest','oldbalanceDest','isFlaggedFraud','isFraud']\n",
    "corr_matrix=df[num_cols].corr()\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1)\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of selected features to analyze\n",
    "selected_features = ['amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest']\n",
    "\n",
    "# Create subplots (number of subplots is equal to the number of selected features)\n",
    "fig, axes = plt.subplots(1, len(selected_features), figsize=(20, 5))\n",
    "\n",
    "# Loop through each selected feature to create individual bar plots\n",
    "for i, feature in enumerate(selected_features):\n",
    "    sns.barplot(x=df['isFraud'], y=df[feature], hue=df['isFraud'], ax=axes[i], palette=\"Set2\")\n",
    "    axes[i].set_title(f'{feature} vs isFraud')  # Title for each subplot\n",
    "    axes[i].set_xlabel('isFraud') \n",
    "    axes[i].set_ylabel(feature)  \n",
    "\n",
    "# Add a title for the overall figure and adjust layout for better spacing\n",
    "plt.suptitle(\"Comparison of Selected Features with Target (Bar Plot)\", fontsize=16)\n",
    "plt.tight_layout()  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "# Add a small value to handle zeros\n",
    "non_zero_amounts = df['amount'][df['amount'] > 0]\n",
    "plt.hist(np.log10(non_zero_amounts + 1), bins=50, color='green', edgecolor='black')\n",
    "plt.title('Log-Scaled Distribution of Transaction Amounts')\n",
    "plt.xlabel('Log10(Amount + 1)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create log-scaled histogram for oldbalanceOrg\n",
    "plt.figure(figsize=(12, 8))\n",
    "# Add a small value to handle zeros\n",
    "non_zero_oldbalance = df['oldbalanceOrg'][df['oldbalanceOrg'] > 0]\n",
    "plt.hist(np.log10(non_zero_oldbalance + 1), bins=50, color='lightgreen', edgecolor='black')\n",
    "plt.title('Log-Scaled Distribution of Original Old Balance')\n",
    "plt.xlabel('Log10(oldbalanceOrg + 1)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create log-scaled histogram for newbalanceOrig\n",
    "plt.figure(figsize=(12, 8))\n",
    "non_zero_newbalanceOrig = df['newbalanceOrig'][df['newbalanceOrig'] > 0]\n",
    "plt.hist(np.log10(non_zero_newbalanceOrig + 1), bins=50, color='lightblue', edgecolor='black')\n",
    "plt.title('Log-Scaled Distribution of Original New Balance')\n",
    "plt.xlabel('Log10(newbalanceOrig + 1)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create log-scaled histogram for oldbalanceDest\n",
    "plt.figure(figsize=(12, 8))\n",
    "non_zero_oldbalanceDest = df['oldbalanceDest'][df['oldbalanceDest'] > 0]\n",
    "plt.hist(np.log10(non_zero_oldbalanceDest + 1), bins=50, color='salmon', edgecolor='black')\n",
    "plt.title('Log-Scaled Distribution of Destination Old Balance')\n",
    "plt.xlabel('Log10(oldbalanceDest + 1)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create log-scaled histogram for newbalanceDest\n",
    "plt.figure(figsize=(12, 8))\n",
    "non_zero_newbalanceDest = df['newbalanceDest'][df['newbalanceDest'] > 0]\n",
    "plt.hist(np.log10(non_zero_newbalanceDest + 1), bins=50, color='gold', edgecolor='black')\n",
    "plt.title('Log-Scaled Distribution of Destination New Balance')\n",
    "plt.xlabel('Log10(newbalanceDest + 1)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create comparison histogram for oldbalanceOrg vs newbalanceOrig\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# Filter out zero values\n",
    "non_zero_df = df[(df['oldbalanceOrg'] > 0) | (df['newbalanceOrig'] > 0)]\n",
    "\n",
    "# Create log transformed values for better visualization\n",
    "log_old = np.log10(non_zero_df['oldbalanceOrg'] + 1)\n",
    "log_new = np.log10(non_zero_df['newbalanceOrig'] + 1)\n",
    "\n",
    "# Calculate bin edges for both histograms to share\n",
    "min_val = min(log_old.min(), log_new.min())\n",
    "max_val = max(log_old.max(), log_new.max())\n",
    "bins = np.linspace(min_val, max_val, 50)\n",
    "\n",
    "# Plot both histograms on same axes\n",
    "plt.hist(log_old, bins=bins, alpha=0.6, label='oldbalanceOrg', color='darkgreen', edgecolor='black')\n",
    "plt.hist(log_new, bins=bins, alpha=0.6, label='newbalanceOrig', color='skyblue', edgecolor='black')\n",
    "\n",
    "plt.title('Comparison of Original Old Balance vs New Balance (Log-Scaled)')\n",
    "plt.xlabel('Log10(Balance + 1)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create comparison histogram for oldbalanceDest vs newbalanceDest\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# Filter out zero values\n",
    "non_zero_dest_df = df[(df['oldbalanceDest'] > 0) | (df['newbalanceDest'] > 0)]\n",
    "\n",
    "# Create log transformed values for better visualization\n",
    "log_old_dest = np.log10(non_zero_dest_df['oldbalanceDest'] + 1)\n",
    "log_new_dest = np.log10(non_zero_dest_df['newbalanceDest'] + 1)\n",
    "\n",
    "# Calculate bin edges for both histograms to share\n",
    "min_val_dest = min(log_old_dest.min(), log_new_dest.min())\n",
    "max_val_dest = max(log_old_dest.max(), log_new_dest.max())\n",
    "bins_dest = np.linspace(min_val_dest, max_val_dest, 50)\n",
    "\n",
    "# Plot both histograms on same axes\n",
    "plt.hist(log_old_dest, bins=bins_dest, alpha=0.6, label='oldbalanceDest', color='coral', edgecolor='black')\n",
    "plt.hist(log_new_dest, bins=bins_dest, alpha=0.6, label='newbalanceDest', color='purple', edgecolor='black')\n",
    "\n",
    "plt.title('Comparison of Destination Old Balance vs New Balance (Log-Scaled)')\n",
    "plt.xlabel('Log10(Balance + 1)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['balance_diff'] = df['newbalanceDest'] - df['oldbalanceOrg']\n",
    "sns.histplot(df['balance_diff'], bins=100, kde=True)\n",
    "plt.title('Distribution of Balance Difference (newbalanceDest - oldbalanceOrg)')\n",
    "plt.xlabel('Balance Difference')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['log_old_org'] = np.log10(df['oldbalanceOrg'] + 1)\n",
    "df['log_new_dest'] = np.log10(df['newbalanceDest'] + 1)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(data=df[df['isFraud'] == 0], x='log_old_org', color='green', label='oldbalanceOrg - Non-Fraud', alpha=0.4)\n",
    "sns.histplot(data=df[df['isFraud'] == 1], x='log_old_org', color='red', label='oldbalanceOrg - Fraud', alpha=0.6)\n",
    "plt.legend()\n",
    "plt.title('Old Balance Org (Log10) by Fraud Status')\n",
    "plt.xlabel('Log10(oldbalanceOrg + 1)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values before handling:\n",
      "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\n"
     ]
    }
   ],
   "source": [
    "# Handle missing values\n",
    "print(\"Missing values before handling:\")\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Zero values in numeric columns:\n",
      "                Zero Count  Percentage\n",
      "amount                  16        0.00\n",
      "oldbalanceOrg      2102449       33.04\n",
      "newbalanceOrig     3609566       56.73\n",
      "oldbalanceDest     2704388       42.50\n",
      "newbalanceDest     2439433       38.34\n"
     ]
    }
   ],
   "source": [
    "# Check for zero values in important numeric columns\n",
    "numeric_cols = ['amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest']\n",
    "print('\\nZero values in numeric columns:')\n",
    "zero_counts = {col: (df[col] == 0).sum() for col in numeric_cols}\n",
    "zero_percentages = {col: (count/len(df)*100).round(2) for col, count in zero_counts.items()}\n",
    "zero_df = pd.DataFrame({\n",
    "    'Zero Count': zero_counts,\n",
    "    'Percentage': zero_percentages\n",
    "})\n",
    "print(zero_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new features\n",
    "# Flag transactions where all balance is transferred out\n",
    "df['emptied_account'] = ((df['oldbalanceOrg'] > 0) & (df['newbalanceOrig'] == 0)).astype(int)\n",
    "# Flag suspicious patterns (like identical amounts)\n",
    "df['amount_equals_oldbalanceOrg'] = (df['amount'] == df['oldbalanceOrg']).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['hour'] = df['step'] % 24  # Extract hour from step (since 1 step = 1 hour)\n",
    "df['is_night'] = df['hour'].apply(lambda x: 1 if x < 6 else 0)  # Flag night transactions\n",
    "df['amount_ratio'] = df['amount'] / (df['oldbalanceOrg'] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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",
       "      <th>emptied_account</th>\n",
       "      <th>amount_equals_oldbalanceOrg</th>\n",
       "      <th>hour</th>\n",
       "      <th>is_night</th>\n",
       "      <th>amount_ratio</th>\n",
       "      <th>is_CASH_OUT</th>\n",
       "      <th>is_TRANSFER</th>\n",
       "      <th>is_DEBIT</th>\n",
       "      <th>is_PAYMENT</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>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>M1979787155</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>1</td>\n",
       "      <td>0.057834</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>M2044282225</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>1</td>\n",
       "      <td>0.087731</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C553264065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.994505</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C38997010</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.994505</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>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>C2048537720</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>M1230701703</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>1</td>\n",
       "      <td>0.280788</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
       "0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n",
       "1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n",
       "2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n",
       "3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n",
       "4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n",
       "\n",
       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \\\n",
       "0  M1979787155             0.0             0.0        0               0   \n",
       "1  M2044282225             0.0             0.0        0               0   \n",
       "2   C553264065             0.0             0.0        1               0   \n",
       "3    C38997010         21182.0             0.0        1               0   \n",
       "4  M1230701703             0.0             0.0        0               0   \n",
       "\n",
       "   emptied_account  amount_equals_oldbalanceOrg  hour  is_night  amount_ratio  \\\n",
       "0                0                            0     1         1      0.057834   \n",
       "1                0                            0     1         1      0.087731   \n",
       "2                1                            1     1         1      0.994505   \n",
       "3                1                            1     1         1      0.994505   \n",
       "4                0                            0     1         1      0.280788   \n",
       "\n",
       "   is_CASH_OUT  is_TRANSFER  is_DEBIT  is_PAYMENT  \n",
       "0            0            0         0           1  \n",
       "1            0            0         0           1  \n",
       "2            0            1         0           0  \n",
       "3            1            0         0           0  \n",
       "4            0            0         0           1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new binary columns for CASH_OUT and TRANSFER transactions\n",
    "df['is_CASH_OUT'] = (df['type'] == 'CASH_OUT').astype(int)\n",
    "df['is_TRANSFER'] = (df['type'] == 'TRANSFER').astype(int)\n",
    "df['is_DEBIT'] = (df['type'] == 'DEBIT').astype(int)\n",
    "df['is_PAYMENT'] = (df['type'] == 'PAYMENT').astype(int)\n",
    "# Display the first few rows to verify the new columns\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get only numeric columns - this automatically includes your type_encoded column\n",
    "numeric_cols = df.select_dtypes(include=['number']).columns\n",
    "corr = df[numeric_cols].corr()\n",
    "\n",
    "# Visualize the correlation matrix\n",
    "plt.figure(figsize=(14, 12))\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm', linewidths=0.5, annot_kws={\"size\": 8})\n",
    "plt.title('Correlation Matrix of Numeric Features')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unnecessary columns\n",
    "columns_to_drop = [\n",
    "    'isFlaggedFraud',\n",
    "    'nameOrig',    \n",
    "    'nameDest',\n",
    "    'type'\n",
    "]\n",
    "\n",
    "df = df.drop(columns=columns_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Count fraud cases by hour\n",
    "fraud_by_hour = df.groupby(['hour', 'isFraud']).size().unstack(fill_value=0)\n",
    "\n",
    "if 1 in fraud_by_hour.columns:\n",
    "    # Plot percentage of fraud transactions by hour\n",
    "    fraud_percentage = fraud_by_hour[1] / (fraud_by_hour[0] + fraud_by_hour[1]) * 100\n",
    "    \n",
    "    plt.subplot(2, 1, 1)\n",
    "    plt.bar(fraud_percentage.index, fraud_percentage.values, color='orangered')\n",
    "    plt.title('Percentage of Fraudulent Transactions by Hour', fontsize=14)\n",
    "    plt.xlabel('Hour of Day')\n",
    "    plt.ylabel('Fraud Percentage (%)')\n",
    "    plt.xticks(range(0, 24))\n",
    "    plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "    \n",
    "    # Annotate percentages\n",
    "    for i, v in enumerate(fraud_percentage):\n",
    "        plt.text(i, v + 0.1, f'{v:.2f}%', ha='center')\n",
    "    \n",
    "    # Plot count of fraud cases by hour\n",
    "    plt.subplot(2, 1, 2)\n",
    "    fraud_by_hour[1].plot(kind='bar', color='firebrick')\n",
    "    plt.title('Count of Fraudulent Transactions by Hour', fontsize=14)\n",
    "    plt.xlabel('Hour of Day')\n",
    "    plt.ylabel('Number of Fraudulent Transactions')\n",
    "    plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "    \n",
    "    # Annotate counts\n",
    "    for i, v in enumerate(fraud_by_hour[1]):\n",
    "        plt.text(i, v + 0.5, str(v), ha='center')\n",
    "else:\n",
    "    print(\"No fraud cases found in the dataset\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jvGfPnjmAFMHcKH0cJZa33nrrnFUfwc7Zueqqq3IguFjMvzPOOKPRTyKI58wee+yRj/E111yTS1FfcMEFOXAWAbWCWCv5iy++yCcXRKD+nXfeyXM1nncR/I95NK8nYMRzL07AqJnlOzvxnI1AZTwvY59RwSDmXpTVfvjhh3NAutzLeRdEIDUCql9//XU+kSEeyzjJZ3658MIL88kiEdCN16QI7scciTFHEDmC+fUVAeKYY7W9hsYJAhFkrit4HMc5XtfjNXCLLbaY5yzmhrx/FMRJU61bt0577713rfso9/cPAAAAAKC8CYJDE4kgTJTAjvLWEXyM8r4RMI2gTZTFnp0ImPz1r3/N2/32t7/NQZUoLRsBtB122KFquwi0/elPf0pXXnlluu2229If//jHqgzY448/PmcTNlRkQd5///3p3HPPTccee2wuxR5B3bPOOqvadpGB+/vf/z73JTL4Iui7+eabp7vuuisHcuojAjjzErick+jLEksskXbfffdq7REgjpLFkRH59ttv54BhBF1jLdraShLPi/Hjx+dg4sCBA6sC4IXgVbRFsGl2QeVC9myUx58XERiMeRTrKUcALUSmb8yJePwiw3J2Yvw1ReB5fp5EEIHlbbbZJo0ePbrOksoh1h6OzNc4YaBQhjmOZQRmY55HtmmIEwUi8B/Pi4IIOEfgPwJvhf4v6BMwIsAXl2I//elP8/MyTi6YX0HwUi7nXXDSSSflNcBDPC577bVXfizmhzgpI07wiMc15nRB7CPW047X3eL2eVWoIFBY27pYtMUJF1FyPF6nCqKCw+mnn171e1SAiMeoMd8/Cq878X6x884759ez2pT7+wcAAAAAUN4EwaEJ1ZUtWKy2DNNOgzt990PXlJbpukyKf5+mT9NPX/tpSq/VcicR8zskpVXTqvnXL9OX6db4d9P/yqnXZo1frJG2u3e7uRvMj/53/+effP4sV0fWelxKVWSIRjA6slAjEF4sMu8jIHjooYem4447LgdlI1AXmaRRwrply5b13m9k3EbZ7poZlZGBGyc2xD4WRPbss88+m4OthQB4IXAXAeHIKI1M1eL1yOdGBKQiENejR49q7REIjGBrZJ5GGerI5PzhD3+YA6fFAcL6eOONN/LlV7/6VVUAvBBUjoBgrBkd2emFwGFN0RYlsqMkeFOegBHBxQgA1lYKPLLEI/gX44wTF+KkiMigjZMfFnQ57zlVFygu512fNZlPOOGEnF0eGb2xv8g0ntfS4HWJ4GqcwBJZ4JHJXhCPW1QCiOzmQhA8xhCXmuJ5UHzbCPQXTnyJ52CobQ5HmfvCNsXXx7yP53q83sTzLNb2LtxPY7x/VL13pJRWOX+VNCKNqNZWq0Z8/3j75LdrvbrU3z8AAAAAgNIjCA6QUi6pG8HomlnMzz33XC49HwHcAw88sKo9MpQjiHzPPfdUa5/f2aQRrF4Q2bMRmK4taBnB2AhCRlniyLScWxGsj0ByrLVeLIKQUe44jmMEVKPEcQT+I6gbZcEj07QhCicJ1DyJIMpBR/nk2k4iqBnkjMvyyy+fGltk48axjiBrZNrGMa+ZmVyu5bwLIku/kKkf1Q8iwB/Z5S+99FLOZG+IUaNG5TLgda1RX3zyymWXXZazkWuK7OS4FPzf//1fXuYgFMYcz6WapkyZUm2b4tvHpRAQj+dEnHQQJeXrewwBAAAAAJiVIDjA/89ijkzcmmuFR5A7ygVHe3FGaKzDHZnSkU1aCIJHYLAQ/Aqx1nOIjO3i20bwrVCCeE7ZpLVlic6P7NlY8/vFF1/Mty2UsY77iOBjIUA6L+IkgVDzJII4rk8++WQOmEYp8oKuXbvmtYajpHPNzPH5eRJBHKPZifLjMe75uQ713IrAdqyrXMj8j4oDEfBeFMp51yXmdRyHOEFiTuvSz0mcgBGB9KiUUFup9uJKBxGAL56fIZ7zsfZ28XIExYHqwnErHMdi0RYVBuZU6SDGG8ssPPPMM9WWsgAAAAAAoGEEwYFF3jvvvJPXxo41jotLaheySSOIHSW8axOZtAVx+1tvvXWO62dHyfFCmeI5ZZPWlh06P7Jno1z4T37yk3T44YfnNYQjYBjB1kJAb15KNMdtY73eCMxGpnfNkwiiLfpbfCLAtttum/+PkwgKQfA4ztOmTavaJn4vnExQHLCMkwMKv8/pJILItq5LBB4j+zeC0YX+NKZYUz6y+qNEfMybCMZHNYJFpZx3bQr3VXjsG6JTp045EzxK9M9p/epY5zouNa233no5U7s2q666aj5xJtaEr+nvf/97Xs6gMccLAAAAAMD/CIIDi7zIVq4tizlEEDIC4IVM55oiCFYQweSDDjqo6vcIEMbvgwcPThtuuGFVeyEIOTfZpFHWe0Fkz0YWdgRfY23pQuA+gpsxhijBPS/rgUeWd2SODxw4cJbr4iSCKJNefJzqOolg9913z/dVU7du3ar9fvDBB+c12ut7EkF466230p577pkz0qPcfVMoDpLGPIlxHnLIIXkd83Iv5x2Pe80TS+IEiCgLH/cTwefCcyACxBHQLh7v3IiM+chaj+MS66oXnyASxzUy3ZdbbrnUEHvvvXd+/sRzqbBG+1NPPZWfi8XPhzhpoLbnwG9+85vcr5pzHAAAAACAMg6Cv/zyy7kUbJTLjS/H119//XTiiSfmrDiA+RkEjyBbbWtgR3uU895yyy3nGOSLwF0heBcKwcYond6rV69abxNB2Mg+j2zS4te2yAoeMWLEXL3e1TebNILdJ598cvr3v/+dy7PHa2whs3hOmbPF4gSBWJs8gqK1nUQQ93vFFVfUettC4DBcfvnlVSXkwz//+c/cvwhgrrTSSlXtxScGFJ9EUHxfhbZY27qmCFhG9nyMOda9btu2bWpqUQ491k6P7PB4PGOulXM57zhpI7L0Y23zyKj+6KOP8jyKkxNiHhTGFkHsCDKPHj06dejQoer2UbUgxNwNt99+e153PpxxxhlVz93YLu4jnotRkSEe67ivP/7xjzmAH/OrIeL5EtUOttlmm3T88cfnzPs4sSTm/KGHHlrtuTZs2LDUt2/ftPrqq+cA/H333Zf/zomTFDp37tygfgAAAAAAsJAEwaPMa3yhHiVZDzjggPzFdXxhHOu2RgAjSsgCzI3ZZZMOHz48ZyoXr8VcLILQsfby+eefny666KJq10Xp6gh6LbPMMvXuWwRiI5M2Ar3Rh0JANoJ6cd/77rvvPGfPzovISi8OnkbA/3vf+15VufU5iSzieG2OIH9tWetxzCOYHWtHz6lUe5wsUKxQmj5OQCgOgNaWTR0nERQHvGMt8Pfffz8HOot9/vnnOSgc/Y6M3drWw24qEfyODOUJEybkx7Ocy3nHe3lkQV9//fX5MYl5H4//pZdemk8GmJOaz9ff/va3VT8XguDhZz/7WT52V155ZVWmfJwsEXNgbvYzJ3FfUb0gTtCLfcXJDDvvvHMO5BefQBBtb7/9du5nZIXH3zYbbLBBXk89KhsAAAAAALAIBMEjsNS/f/+cWRjZZYUv4s8666wc5IjMq8g+K5RkBRZd1157bfrqq69y0DM8+OCDOfgZIsMygsx1ZZOGQpnz2kqhF9bvjqzViy++OGdmR/AsAulRqjoyQK+++ur8etQQkSUa62LHviJoG/2PIFrsKzJH5zV7tr7+8Ic/5MzUKN8er78F7777bl5vurbAeGRSx/Gv6/jFSQSxTWQL1wxIRwA1sp2XWGKJeve5S5cuuV833XRTPj6FjOkIrkbQvfix+eabb9JOO+2US7fHiVZ1lRlf0Cdg1HYyQxzDOJkggqqF68q5nHec3BaXOYmy94XS96HT4E75/zV+MWvAv+Y21eye0kq7f1dN4Nv0bXok/j3wyGz3Hfs4/7Pz0/mDz59jP9P2KbXfvn16++S3a796++3zBQAAAACARTgI/te//jVnTEUp0eJMtAhmRQA81kyNL+0jKA4s2iJgO3bs2Krf77///nwprLMcrxt1iQDs73//+xzAm91a2jfccEPOUr3xxhvza1BkKEcwPe4/spQbKvYfGdinnXZaDjxGVuzhhx+eA+/zM3u2WJxgdN555+VAewRSX3zxxZyVGkH3KOtcLEptR7ZrBF5riiB8ZLxGMLU2P/7xj9Pdd9+d1yCPwHMcrxkzZuTAfbQ/9thjeS3yhojy0zH+GEsEVl9//fV8csQRRxyR1l133artIlAfGc6HHXZYzv6PS0GcQBDlshvjBIwdd9wxZ9tvttlmOeAdJxnEsY/7iRMRCpTzBgAAAACAMgqCDx06NP9fvI5pQWHN0QjIABTW3a5PNmlofULr9FX6qvbs0ZoOSGnl9F357G/SN+ne+HfnvXPMJj38lcNTmrXi9Kz2TGnysMkNzp6dG5FJHlnTEfCM8ttRcjsCrlHWuVCGfE4iK/3hhx/OpZ7rOtkgMsr/9Kc/5XLUUbY9grdt2rTJpbsj6Dova4/XZZdddsknPkTGdARkI2M5grs1T5SKTP4QJamLy2eHqCwyr0Hw+pyAEfPs69W/Tm/884300FMPpZmTZ6ZmizdLi/3fYmmlXVdKp405LZ02+LT/3aBFSiv1Wym9+uyr6YUzXviuaZkWafHOi6erP706XT/4+tn28dRHTs3ZzLOz+CGLpw8e/CAdf9LxaeklllbOGwAAAACAhV5JBsGjzHCorVRt+/btc8ZeYZvaxFqvcSkorFUamWtRar0QmIlLZILGpaDQHpmKxVmPNdubTf2uVPCMyhmpMlWmFhXVD2Vd7dMrp6eKVJGaVzSfY3vcPu6nWfyraDbH9pmVM1P8i/YY67yOqSACY1HetnCsitvz2GbMmKv2CKTF/Ra3x/3m7aekeR5Tbe1xvOK4zal9Xh+nwpyZlzHVnEt1tTd07uW2qc1Kdu5Fe2H+leLci2NeeP7Oy5jKbe7FWuC/+93vZnk8IhO45vYRuC6U397s+s2q9f17Z3wv/Sv9K6154Zpznns/bJ5WS9+V3P46fZ1uqbwlDfnlkNnOvc4XdE7b37n93D1O+zRLH/zku6zs6HeUW4/xFObYq6++Otu5F0H9eZl7sdb57B6nOF5XXHFFPgEgto+s8Zh77bq3y5daxzR11rnXdq22+VLrHJta99zreH7HvI/i+Z7HUuNxWqzdYmnVfqvmx2nEsSOqxlR4HseYYimSyGivbY59+eWXjfK6l+fHlMqSfd0rHLNSfd0rfu0rtde9wuNR/LfL3I6pseZevu8pM0t27oX8GlOicy/3Y2qzkp17cT/F86/U5l7sY06v5bWNqbHmXrTHe2ipzr3cPrWiZOdezffcUpt78TgVz79Sm3vxL/4WKdm5V1GR3z9Kde4V5l+pzr283Ry+e2nKuVfz/aPU5l5t372U0twL8f5RqnNvTt+9NPXcK/772dwz9+pqXxTnXs3v/Upt7kV7fPdXqnNvdt+9lMLcm9N3L00994rnXynOvZrfvZTa3Kvru5dSmXuz++6lFOZePuZTm5Xs3Gte9Lfz/Jh78V4eaqteW6yick5bNIHIAH/iiSdyoLu2EquRwRiBjUKwqKZzzjknZwQCAAAAAAAAUF7ee++9vPToQpUJ3lCxfmqU9C2IMwPiDINY9zbOJCg1ccbCaqutlh+spZZaqqm7s9Bx/BrG8WsYx6/+HLuGcfwaxvFrGMevYRy/hnH8GsbxaxjHr2Ecv4Zx/BrG8WsYx69hHL/6c+waxvFrGMevYRy/hnH8Gsbxa5hSP36R3x3LvK6yyiqz3a4kg+CFdVTryvSOgx+lfOsS65gWr2UalllmmVTqYiKV4mRaWDh+DeP4NYzjV3+OXcM4fg3j+DWM49cwjl/DOH4N4/g1jOPXMI5fwzh+DeP4NYzj1zCOX/05dg3j+DWM49cwjl/DOH4N4/iV7/ErxJJnp/riciWisBZ4bet+f/TRR7kUem3rhQMAAAAAAACwaCvJIPjWW2+d/3/88cdnue6xxx6rtg0AAAAAAAAAlHQQfLvttktrrLFGuuuuu9KIESOq2qM8+kUXXZRatWqV+vXrl8pFlG4/++yzZynhztxx/BrG8WsYx6/+HLuGcfwaxvFrGMevYRy/hnH8GsbxaxjHr2Ecv4Zx/BrG8WsYx69hHL/6c+waxvFrGMevYRy/hnH8Gsbxa5hyOX4VlbF6eAl6+umn0w477JAWW2yxdMABB6S2bdum++67L40dOzYNHjw4nXTSSU3dRQAAAAAAAABKTMkGwcPf//73fKbB888/n6ZNm5bWX3/9dOKJJ6b999+/qbsGAAAAAAAAQAkq6SA4AAAAAAAAACz0a4IDAAAAAAAAQH0IggMAAAAAAABQNgTBAWhSDzzwQPrXv/7V1N0AAAAAAADKhCA4AE1qjz32SL/61a+auhssgsaPH58+/vjjNHPmzKbuCouoSZMmpWnTpjV1NwAAAACg7LRo6g4sSt5777106623pr/97W9p1KhR6euvv87tSy+9dFpzzTVTr1690o9//OO0+uqrN3VXKUPxJftrr72WWrRokdZff/1UUVFR63aRkTtixIjUr1+/Ru9jqXvkkUfSf//739S1a9e0zTbb5LaJEyemCy+8MA0dOjS1bNky7bzzzumEE05IrVu3buruloS///3vc7XdJ598Um3bTTfddAH2ikXFuHHj0tixY9Pmm2+emjX733l/N954Y7r88svT22+/nX9fYokl0l577ZUuu+yytOKKKzZhj0vL9ttvn/r27ZvfD1ZYYYWm7s5C6Y033khXXHFF+vTTT1Pv3r3T0UcfnefiQw89lE466aT8nhK/9+jRI2+38cYbN3WXS867776bbrnllvTXv/41jRw5Mn311Vf5mMVztXv37umHP/xh2nPPPev8uwYAAACARVNFZWVlZVN3YlFw5ZVXpkGDBqWpU6fm35dccsm01FJLVWWiRSAtRODs4osvzkE06u83v/lNGjZsWPrtb3/b1F0pCffcc0/6yU9+kr788sv8+yqrrJIuvfTSdOCBB86y7bnnnpvOO++8NGPGjCboaWmaPn162nXXXdPjjz+e4iUzvmg/7LDDciBt6623znOtIK7baqut0lNPPVUt6LaoimNQn8CE+Td733zzTfr1r3+d51783KFDh/x87tmzZ1N3raTEMXnhhRfS6NGjq9pOPfXUHACPebnGGmukZZZZJgciI7DWsWPH9NJLL6Xll1++Sftdas/fOHkqXgOPOOKItMMOOwg2zqWYd9///vfz33khjlsEwSNoG+8dceLUOuuskwPk77//fj4Z45VXXklrr712U3e9ZFx33XXp5JNPTlOmTKlzmziuceLU3XffnVZbbbVG7R8AAACzJiTUlgC46qqrNnXXgHp8Rx/fm8Z3WPE96sJIJngjBSAj22ettdbKgfD4AnmllVaqtk2UY3300UfTRRddlLf93ve+l/bZZ58m6/PC7rnnnku33XabIPj/z8SNL9ybN2+es/riBevJJ5/MVQeeffbZdP311zd1F0veHXfckR577LFcrSGyzSIjPObWsssum/7973/nL9779OmT/8g78cQTc7A8stYOP/zwpu56SYjATvpwstYAACF7SURBVJQ8jzlYU5xUEM/V+GM4MiGpbtttt02HHHJItcoM77zzTs4ojQzn4vPY4qSMeI85//zzm6i3pefFF1/MwcaCyPyObNsIPMbztkuXLlUnusQJaGeffXY+fldffXUT9rq0rLfeeumjjz5K999/f/rjH/+Y/z459NBD8+X//u//mrp7JS1ONpswYUKec7vsskt68MEH089+9rP0z3/+M2200Ub598LfgzfccEP66U9/mufhkCFDmrrrJSGOzzHHHJPn2cCBA/NJKnFiwVVXXZW/RLn33nvzyQPxHh3vudttt1169dVX84mm1M6XUVAeyuGLqKYWFYDis3GcKM68iQSOWM4lThp10ve8ef755/MJj5MnT84nMUfFpXgvpvrfKv4umT+VIOO52q5du2rtcYL48OHDcwJWfE7u3Llzk/VxYRCfPWpWourWrVtq27ZtU3etJH377bc5CfDmm2/O31vVJj7THXnkken4449XQbMB4vuY22+/PX+Xxf/cd999uQJzJHJE/C0utYlKzXGJanN8580330znnHNO/owRFYQjSTK+i4nKt/GddLw/h4hvxrFb6Cq4RiY4C9bmm29e2bFjx8rx48fPcduvvvqqskOHDvk21N8hhxxS2axZs6buRknYe++9K1u2bFn53HPPVbWNHTu2cquttsrH6OCDD66cOXNm1XXnnHOOY1fDlltumZ/DM2bMyL/H8Vp33XUrmzdvXnnDDTdU23bSpEmVyy+/fOV2223XRL0tLZdccknlYostVrnFFltUvvnmm7VuU1FRUdm/f/9G79vCII7NueeeW61t0003ze39+vWrHDZsWOV//vOfyltvvbWyffv2+bn75JNPNll/S03MvdNPP73q93i+xjEqfj0sFs/b//u//2vEHi4c8+/bb7+t/MMf/lC5/fbb59e9aI//+/TpU3n33Xfn65nV2muvXbnjjjtWa9thhx3yHIznbm3zb7XVVmvEHpa2+DslXtc+//zzau2fffZZbv/JT35S1XbPPffkeXn22Wc3QU9L29SpU/N7cefOnfPcq+3SqVOnyksvvbRyypQpTd3dhf7zR7w28j/33ntv5bHHHls5cODAykcffbTO7YYMGVK5zTbbNGrfSt0bb7xRud9++1V269Ytf14bM2ZMbn/66afze0Xh+bvOOutUvvTSS03d3YWOzx91i+8Kvv7661naH3zwwcoNN9ywau61bdu28ogjjqj84osvmqSfpSr+JjnqqKOqvjsI48aNy5+HC8cu5l/8v8wyy1T+7ne/a9L+lpo4Nuuvv37lVVddlf/mY96dd955lUsssUSeY/Ee8t///jfPx/3337/a33/xN8upp57a1N0tOfHZNv4uju8Aa/u7uUWLFpW77rpr5YgRI5q6qyVl4sSJlZtttll+Dsf7Q9++ffPfgD//+c/zJX6OtrgujmPEPeI21I+4R3XxGrfbbrtVvccW3mfjO6yPPvpolu3FPqqLv1PatWtXdeziEt8fvPPOO/lvlZVXXrlyzz33rHqOx7Zxm4WJTPBGEOswxxnGc3OmWJwFuvfee+eMIP4nMkXnRZy1wneiXHJk4W655ZZVbbHufJTrPvjgg/OxjWyC+F+J29pF9mhkCxTOdI/jFBln//nPf3KJ4GKLL7542nHHHXNlB1I67bTT8jrpMdfijNk4q+yUU04x1xpQ2eHll1/OZ+EVZ4vGmXhxFt6GG26YywfH/OS7KgSRiVsQZ3CHKFFdm2iPSiJUF5lm++23X77E+sxRCSMyb5944olcWSSqYkR1kah+UciuJ6X33nsvVw8pFhngcdzi/5riNTIqtPCdESNG5Eo2Mb+KLbfccvnvmqhOEK93IaonRbWWyA6P9xm+E8tlxPtBvHdEhnxUrYnM7+IlmSIzPP5WPP3003O1h3hOx2sn9WOlse/MnDkzv/499NBDVcckqqzEfIyslZpV0caMGZOzNvjOBx98kD+7Ff5uiay9eJ5Gtak4rvF5Y/fdd8/bxfM7sklff/31vOQVKVdWmRtxTAvbxmeTX/3qVwu4ZwuHyNKL99Izzzyzqi2et5F1Fs/nTp065SzwqIgWy9BFZnNUX5LR95342ySycAvfHcQxi+8M4nm82Wab5Z9jOaY4fpFJFX9DR6Uly1r9T7yeRYW9qKAUr3WxJFNUQmPuKilFdbP4/nmLLbbI30fHZ7h4/kYltPgbeptttkmffPJJriQ3ePDg/Bn4gAMOaOqul4RYgin+Xo73h3juRiZ9YVnJddddN7/+xfyMv2+iWmS8Bh500EFN3e2SEFmj8TdJfAcY7x9t2rSpdbuoJBJLcF522WX5/6ieBg1100035de/7t275/eP+A4rnp/x3XxUHY2Mb5UM6xbvBVEtLo5ZfLcSr3HxvXO8N0R1yDiOhbhmfB8Y78tR9eEXv/hFWmg0dRR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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with stacked layout (one graph per figure for maximum size)\n",
    "# First graph - Non-fraudulent transactions count\n",
    "plt.figure(1)\n",
    "fraud_by_hour = df.groupby(['hour', 'isFraud']).size().unstack(fill_value=0)\n",
    "\n",
    "fraud_by_hour[0].plot(kind='bar', color='forestgreen', figsize=(20, 10))\n",
    "plt.title('Count of Non-Fraudulent Transactions by Hour', fontsize=20)\n",
    "plt.xlabel('Hour of Day', fontsize=16)\n",
    "plt.ylabel('Number of Non-Fraudulent Transactions', fontsize=16)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.ticklabel_format(axis='y', style='scientific', scilimits=(0,0))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# Annotate counts (scientific notation for large numbers)\n",
    "for i, v in enumerate(fraud_by_hour[0]):\n",
    "    plt.text(i, v + v*0.01, f'{v:.1e}', ha='center', fontsize=12)\n",
    "\n",
    "plt.tight_layout(pad=1.1)\n",
    "\n",
    "# Second graph - Combined log scale view on a separate figure\n",
    "plt.figure(2, figsize=(20, 10))\n",
    "fraud_by_hour.plot(kind='bar', log=True, figsize=(20, 10))\n",
    "plt.title('Fraudulent vs Non-Fraudulent Transactions by Hour (Log Scale)', fontsize=20)\n",
    "plt.xlabel('Hour of Day', fontsize=16)\n",
    "plt.ylabel('Number of Transactions (Log Scale)', fontsize=16)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.legend(['Non-Fraud', 'Fraud'], fontsize=16)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "plt.tight_layout(pad=1.1)\n",
    "\n",
    "# Display the figures in fullscreen mode\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create hour risk categories based on fraud percentages\n",
    "def assign_hour_risk(hour):\n",
    "    if hour in [4, 5]:  # >20% fraud\n",
    "        return 3  # Very high risk\n",
    "    elif hour in [3, 6]:  # 10-20% fraud\n",
    "        return 2  # High risk\n",
    "    elif hour in [2, 7]:  # 3-10% fraud\n",
    "        return 1  # Medium risk\n",
    "    else:  # <1% fraud\n",
    "        return 0  # Low risk\n",
    "        \n",
    "df['hour_risk_level'] = df['hour'].apply(assign_hour_risk)\n",
    "# Drop unnecessary columns\n",
    "columns_to_drop1 = [\n",
    "    \"is_night\",\n",
    "    \"hour\",\n",
    "    \"step\",\n",
    "    \"amount_ratio\",\n",
    "    \"is_DEBIT\",\n",
    "    \"oldbalanceDest\",\n",
    "    \"newbalanceOrig\",\n",
    "    \"is_PAYMENT\",\n",
    "    \"amount\"\n",
    "]\n",
    "\n",
    "df = df.drop(columns=columns_to_drop1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_excel('transformed_fraud_dataset.xlsx', index=False)\n",
    "df.to_csv('transformed_fraud_dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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SKcdVq1aV8+fP271EXMHUxsQOHTokxYoVM+Xz8H0a2NAyoqFDh9q9FGTA/PnzpX379mbaeMOGDc02HUrhmYSrWR1wBi330564elEncY8xfa/UgAecQQNTEyZMkC1btpj711xzjXTt2tXc4Puee+45UwngaRETFRVlLuxogEqP05iYGJk1a1b8ay6cQbPFR40aZV5btWejBh91kJMO6QKQcZRUA7Dtin9yQai9e/eaD1VwDv0wnFKWzdGjR7N8Pbhy+oHp888/NwNj6tatK9mzZ0+wXwfHwPc1bdpUNm/eLB9//LHpw6k6d+5s+jfqBQA4a6iBDudKnEWuQ4AYxuUsmuWot8vRCz6PPvqomSwP3/H777+bNgYe3377rWkPpFmqenHngQcekDfffNOUycM5dBDQK6+8YvcyAL9FhiMAW+hJt77JjxkzxgQYtSxXyzlvu+02c+JGc27nZDhqz5vk3ko82/VfMhydIbWeRfo8askRgKyjPXL/97//mQsBWrKpGaoa5NBs5Ndee82UycP/WpWsWbOGgLIPPi/aR1WHqKm7777bnL/qeazS50x7dOrwQ/gu/byRVgzLAzKODEcAtnjvvfekTZs28SVhOqVarxIXKFDAlBzBGbRHI5xPyzN14u3cuXPtXgosoBdscuTIIXfeeWeC7donTttVMPzAOV588UVTEaB9xvS5a9KkiWl7oENICDb6J3JBfPcCq/dzo71wNejvoRmpJ06csGl1SKtatWqleKHcGxfLAWuQ4QjA1vJN7Wnk3TdFJ3FGRETYvTTgqhIUFCQHDhyI78WpGcg6cESnqsJ5tDfcp59+miRjVXs7Pvzww6bcGs6i/eK0tFrfK/VCnQaU4Z+8e3XCd2hvRr2Io70ctW+jZr/pMakX6zyvr3oxR3uRw3dphnhalS5dOlPXAlwNCDgCACyxYMECE+TYvn27TJ48WYoXLy7ffPONORm/8cYb7V4e0jH8hw+8zhYeHm56Nybu+6cfhHVC7oULF2xbG9I/0ECzbPLly5dgu05RDQ4ONmWe8C+8/vqmadOmmQE/ej6jAcf69evLTz/9FL+/X79+pupj0qRJtq4T1tMhbGPHjmWQDHAFAq/kmwDAir5U2pMqMd329ttv27ImXLkpU6aYEnnNTl29erVERkbGf1j2brIOIPNp4Di5PlUaxMifP78ta8KV0QDHd999l2S7BjWYbgxknU6dOpkeqprZqD1UtULHm06q1sFc8D9//vknF+qAK0TAEYAtNBOucuXKSbZXq1ZNRo8ebcuacOV0MqM+b5999pmEhITEb7/hhhtMk3X4Nu1VpLfE2+BMOszg6aefNj05NTtObzrwp3fv3gSpHGbp0qXJDnNq1qyZ2Qcg62gv1REjRphsRg0wehswYIA5LgEAlzA0BoAttHwzudIEnVStveTgLNoTTocZJKaTyE+ePGnLmpB22l2lZ8+eZhiF0kFOjz76qGTPnj3B46ZOnWrTCpEeb7zxhimf1g/HWnardPBI9+7dyTh2GM0W137HiUVHR5Nx46caN25ML2sftHv37jQ9rlSpUpm+FgBwCgKOAGxRsmRJWbRoUXyzbQ/dVqxYMdvWhStTpEgR0zw9cc+4hQsX0ofKARJPLb733nttWwsyLjQ01JT7aebxmjVrTPCievXqNMB3oAYNGsiYMWPkww8/TLBdM8rr1q1r27qQdqdPn07T4zz9OLVsF75Hz2+Sy/zXC3ae7fpvchcIAOBqRcARgC169eolzzzzjMnSaNGihdk2Z84c6du3rzz//PN2Lw9X8Hxquab24NQT7v3798vixYvlhRdekNdee83u5eEyvvjiC7uXgExQsWJFc4NzadC4VatWpv+mZqx63iuXL18uv//+u93LQxrkyZMn1RYVnoCVtj6A79L+1Ck9f9pn9YMPPmB6PAAkQsARgC369Okjx44dMw22o6Ki4ieral+cl156ye7lIZ1efPFFU7KpH4jPnz9vyqu1PFcDjk899ZTdy0MGMnO095/2W02u5yp80+23324y4/T11NuwYcNMoOr777+3bW1IH+2Dqxdv3nnnHTMoRrNVdWjFuHHjCCY7hPZS9Q5OtWvXzky8LV68uK3rQvrUrFkzybbZs2eb858tW7ZwwRwAkhHg1nc+ALDJ2bNnZePGjeZDlH548vSQgzNp8FhLq/V5rVq1Klf7HaZLly4mWPzkk0+a/nD6AUt7AXoyODSQBd+nvXA1UKxl1N7WrVtnsuUOHTpk29qAq13OnDlNxirtRpxLh+HpBZ0FCxbIQw89JP3795dChQrZvSxcwUVVTyuDxPRctkKFCubrt956Sx577DGTrQwgfchwBGArDUjVr1/f7mUgg06dOmXKwfLly2cCjR7Hjx83QytSOqGDb/nzzz/llVdeMV9PmzbNBBp16M9XX31lSjsJODqDBvy1j2NiOkE+rf3k4Ds0e1w//B4+fNh87S25YV0AMsf27dvl5ZdflilTppgLdBs2bCBw7GDt27c3WaqJkx10EKJW7Ozdu9fcp/IKuHKBGfheALhi586dM739GjVqZK4g6gmb9w3O0rVrV5MBl5iWAOo+OCdwrEFjNWPGDBNgzJYtmzkp37p1q93LQxppZqMOjUlMj1HvCwLwfUuWLDHvkVWqVDHBxWbNmsXfmjdvbvfygKuGtgDS1099n1yxYoWMHz+e81U/SHro1KlTgkE/WnWlr69cYAWsQYYjAFtoCcr8+fPlvvvuk6JFi6baUB2+b+nSpTJ8+PAk2/WkzZMxB2dMj9d+cRp01ICjJ4h84sQJ02MVzqAXczp37myycbyHck2YMIH+jQ7z6KOPSr169eSXX37hvdKP8Dw6j06G1/dBzTR+4IEHUi23hjNMnTrVtBnp1q2bOd/5559/TGaj3k/unBZA+hFwBGCL3377zXyA0ob4cL7IyMgEV4g9dAq59gKEM+jkeD3R1qv+pUqVMgFjT6l14n6A8F233nqrTJ8+XYYMGSKTJ0+OHzSipWNNmza1e3lIB80s1ufQ00sMzqPBf28XL140geTs2bMnCX7Adw0YMMDuJcBi+t6on0X0XEdL5PVcp3v37mZIFwBrMDQGgC3Kli0rv/76qykTg/Npad+1114rH374YYLtTzzxhKxdu9Y0VoczrFy5Unbv3i2tW7eO/0CsJ+R58+Y1LRAAZB3NUNXpt23btrV7KbhC999/f5oe98UXX2T6WoCrXXJ9jA8cOCA33XST3HLLLTJ06ND47fQfBzKOgCMAW/zvf/+TH374wQyj0B5xcLZFixaZshQdAKTlKJ4SzuXLl8vvv/8ujRs3tnuJSMFzzz0nb7zxhgku6tepocQIyFo6vOnVV1+VPn36mCxjHfzjTTNXAdgfxPr2229l3Lhxpr8jfFdgYGCyLQ08IRHdp1/rvzoMEUDGUFINwBbvvfee6S9WuHBhKVOmTJIPUfTAcRYtjdfef1qGooNiPCWcevJdsWJFu5eHVKxevdqUvnu+Tgk9x5xDPySNGDHCHIuarRoVFZVgv06PhzN4Bhd494zjA7F/+Pfff80AvcqVK5sgCJxn7ty58vnnn5ty+Ny5c5sBJPD95wxA1iHDEYAtBg0alOp+euUAwJXp37+/jB07Vp5//nmTHaeDm3bt2mX6Ouq+p59+2u4lIh1BqdSULl06y9aCK6MBqZMnTybIIH/44YfNBTlVqVIlmTlzphnaBd+3b98++fLLL00JvD6vOlRNJ1ZrD0AuzAFAQgQcAQCWcLlcsm3bNjPBUb/21qRJE9vWBVxtypcvLx988IG0b99ecubMKWvWrInftmTJEvPhGEDWuP766+WRRx6J7+U4Y8YMM9hJg1bax/rJJ5+UqlWrmosE8F1TpkwxQWIdLHLzzTfLvffea/7VdiR///23eQ7hLHos6pC8G2+80dz/6KOP5LPPPjPPpX6tvasBZAwBRwBAhmkQ45577jHZOInfVij7A7KWfgDeuHGjmTRetGhRM/SnTp06smPHDqldu7acOnXK7iUinTZs2JBseXyHDh1sWxPSJn/+/DJv3jzTg1M99thjcuTIETN9XOk+DUbu3LnT5pUiNcHBwdKvXz958cUXzYUcD20JRMDRmfSYfPvtt6Vdu3aybt06qVevnqkM0LJrbXXAICcg4+jhCMAW9BjzL48++qg5UdPAhgY4KCsC7FOiRAkzdVMDjprZqIObNOCoQ5zCwsLsXh7SQYPE2hdOPwx7ejcqz2ssF3N834ULFxJMu/3rr7/kwQcfjL9frlw5OXjwoE2rQ1rpc6ZZbxogvu++++Suu+4iA87hNMjvCRRrBqtmHg8ZMsT0kdcgJICMo0MxANt6OOrEWz1h02wb7W3UuXNn0zh94MCBdi8P6bR161ZzkqblYXny5DHN071vALKOBqh0Srx66qmn5LXXXjPDm7p3755g+Ah8X+/evaVs2bKmVUW2bNnkn3/+MSWdeoFHAx/wfdpnc+XKlebro0ePmudQB615aLCR90nf9+mnn5oLOdp/c8KECebi6m233WYuAiRuIwNnCA0NlfPnz5uvZ8+eLa1btzZf58uXz0weB5BxlFQDsAU9xvxLixYtpG/fvtK2bVu7lwIgEX1N1awqDTpqBgeco0CBAvLHH39IjRo1TFBq2bJlZsiIbtPSv9Qmy8M3DB06VN5//315/PHHzfOm5dTr16+P3z9y5Ej5+eefTcADzrrQqiW3X331lZw9e9acz95xxx3m4jmcQVtSaIWVXgB44403TMZj8eLFTVWA9lbdsmWL3UsEHI8MRwC20Cv6nn5G2rDZ01PslltuMWW5cBbNotIPv9oEXzM51q5dm+AGwN6hFZpFnjjYqB+QNWMHvktLpj394jT4uH///visuc2bN9u8OqSFXozr1auXTJ06VcLDw+X7779PsH/RokXStWtX29aHK6MXcLSyY8+ePfK///3PZMrdfffddi8L6TBq1CjTm1P7qX7yyScm2Kh+++03LqADFiHDEYAtNEPj66+/luuuu85Mh9NAozbinjhxogleafkYnENL4RPz9BtjaAzgmzSQpcMOtIccfFPjxo3NxZyOHTuawVwnTpyQV199VcaMGWMu7nhnysG59D0yKCjI7mUgg706NYDVp08fu5cCAD6DoTEAbO0xpgFHDTDee++9Mm7cODNA5tlnn7V7eUgnpmsCgPU0uHju3Dnz9euvv24uzmkQUicf6wU6OJuWbOq5j16AJdvY92k5/NKlS03vv5YtW5ogcXR0tHz88cemdF6/JuDo27Q3o2eI0+X6NHoPewJwZchwBOATFi9ebG70GAOArEGGozMdP37cTMf1TKqGs2jprQaLP//8c3PeowOAbr/9dgJVPm7hwoUm4K9BKj329HnTHo6afaxluU8//bT06NFDIiIi7F4qUqFBYg3uFypUyFTnJPc6SnUOYB0CjgAAy2zYsMFkqWoT7sSNuQH4FgKOQNYObxo7dqzp4ViqVCnZuHGjzJ0712Sswvc1a9ZMihUrJi+//LIZFPPee++Zi+SDBw82w2LgDPPnzzdDYjRIrF+npmnTplm2LsBfEXAEkGV+/PHHND+WAJWz7Nixw5TJr1u3Lr53o/JcOeYqMeB7CDj6pvRMudVBJPBtGpjSbEYdjqdDRbSFTM2aNSUkJMQcf1WrVrV7iUgDbWOwYMEC83xpv0YdeKjH32233Wb30nAFYmJizNCfBx54QEqUKGH3cgC/RQ9HAFlGy07SgjIG5+ndu7eULVvW9OXUf5ctWybHjh0zww7effddu5cHAI6RO3duu5cAC/Xr18/ctAcng2GcSwc26aR4pWXT2bJlk2uvvdbuZeEKaYbjO++8I927d7d7KYBfI+AIIMu4XC67l4BMon2o/vjjD3Myrj1x9KbTx9966y3T12j16tV2LxFAIloamC9fPruXgUS0Lxz8xxtvvGGe02+++cZkON53330EqhzcNubgwYPma63k2Lx5c/xQJ48aNWrYtDqkV4sWLUxZdZkyZexeCuC3KKkGAGSYDjBYtWqVyW4sX7686VPVvHlz2b59u1SvXt00yQeQeWhZAfg2DWxoafXkyZOlQoUK8s8//8T3k4Pv8wwYSe6js2c7FTrOMnr0aBk0aJB069ZN6tatK9mzZ0+wn/dKIOMIOAKwjV4V1pPt5IaMaFYcnEOb3mv5tJbN33PPPab06NVXX5UxY8bIypUrZf369XYvEfD7D8PeEn8w9p7EyQdiZ9EA1aRJk5J9r9QLPXCWM2fOyPjx403wUd8fGzRoYIaOPPfcc3YvDan4999/0/S40qVLZ/pakDnvm94IHgPWIOAIwBZaYtuuXTuT+aaBRy3rO3r0qOmJU6hQITOEBM4xc+ZM8zzqsINt27bJLbfcIlu2bDFN1idOnGjKVgBkjdmzZ5uecdoQv2HDhvFtD/QigG676aab7F4i0uiDDz6QV155RXr27Gku4Nx///0mc3z58uXyxBNPmAm5cC4dtDZu3DgTgDx8+LDdy0EqtAfnCy+8YM5TAQBpQ8ARgC2aNWsm11xzjSln0Ab5OqlRJzbq9EYdQJKeKZ3wTcePHzel1t6ZVQAyn/aH09dW7aPqTSesPvzww7Jx40bb1ob0qVy5sgwYMMD0/vOeKt6/f3/zGjtq1Ci7lwgLREdHm3Mg+C4d+HPgwAFzURxXF20N9Ouvv0rJkiXtXgrgOCnnEQNAJlqzZo0pwdVyBj2Ji4yMNG/kw4YNM4MM4HyatUqwEch6mgGXJ0+eJNv14s6uXbtsWROujJZRN2rUKH4yrpbjKh08MmHCBJtXh7TQgWpVq1aV06dPJ9l36tQpqVatmixZssSWtSHtyNG5eun7pl4UAJB+TKkGYAu9ku/pnaJXi/VDVZUqVcwH4j179ti9PKRBerJQp06dmqlrAXBJ/fr1TT84nYpbuHBhs+3QoUPSp08f0y8OzlGkSBGTyah94UqVKmUCUzVr1pSdO3cSAHGIkSNHSq9evSRXrlxJ9uk5zyOPPCLDhw83vZDh27iICgDpQ8ARgC1q165telBVrFhRmjZtasrDtIejfkDWckD4Pv2gBMD36DCKTp06mQCVpwRML+To6+306dPtXh7SQfvf6gRyfc/U/o3PPvusGSKzYsUKWo84hJbBv/322ynub926tbz77rtZuiZcGW0FdLmgo14gAADEoYcjAFvohyUtDWvevLlplN69e3f566+/zAdi/bCsGRwAgCujp3ezZs2STZs2mfuaQd6qVSsydBzG5XKZW3BwXI7Ad999F/9eqZlxoaGhdi8RlxEeHi7r16+XChUqJLtfB61pj7gLFy5k+dqQdlqVo9mql7vY2qNHjyxbE7KGd/9cAOlDwBEAAMBPXbx4UcLCwgg0AjYpX768vPfee9KxY8cUW47o9OMdO3Zk+dqQvoDjwYMHGRpzFSLgCFw5SqoB2EqzGzdv3hw/jbNgwYJ2LwlXSMv8Jk2aZPpxRkVFJdi3atUq29YFXG00I27w4MFmUrX2btyyZYv5oPTaa69JmTJl5MEHH7R7iUiHEydOyLhx4+Kni+sAEi2v1sFc8H3t2n0dY6YAAByvSURBVLUzx17btm1NtqM3zWrUKeS33HKLbetD2nDRBgDSjynVAGyh5dQ6ZbN48eKmh6PeihUrJvfee6+Z2ghn+eCDD8wHYB1QsXr1ajOYIn/+/CZj4+abb7Z7ecBV5c0335Qvv/xShg0blqDkVvvjjh071ta1IX3+/PNPKVu2rHmN1cCj3vRr3ab74PteffVV09dP+//pMfnDDz+Ym/Z1rFSpktn3yiuv2L1MXEZ6iwL37t1rLv7AN+nU6ZYtW8rWrVsv+9hPP/00fgAbgPShpBqALe666y4TmPrwww+lYcOGZtvixYuld+/eUqtWLdOnCs6h2amapXH33XcnKD3RYUD6YWrUqFF2LxG4amivOP2ApB+mvI9H7eeor7catIIzaG8/fc4++eQTCQoKMttiY2Pl8ccfN70c161bZ/cSkQb//vuvPPbYYzJz5sz4wJVmzLVp00Y++ugjE0CGf9Gp5GvWrKEM14dpVZWnJy6AzEHAEYAtsmfPbk68b7zxxgTbFyxYYMqOzp07Z9vakH7ZsmUz5X6lS5c2/Y10WIUO/tErx9dff70cO3bM7iUCV42IiAgTXNTj0TvguGHDBpN9fPbsWbuXiHQ8lxq00Ew4b9qKRC/OMWjEWTTYr0Ni9OOXBjny5s2bbGacVnxoz0A4F33/fN+zzz5rehwPHTrU7qUAfosejgBsoeW2yU36023JnYDDtxUpUsRkMmqAo1SpUrJkyRITcNy5c2e6y5AAZIz2+NOLN3o8Ju6zWrt2bdvWhfSrU6eOuZiTOOCo2/Q1Fs6i5zf169e/7PFLZhyQ+WJiYuTzzz+X2bNnS926dU0yhLfhw4fbtjbAXxBwBGBbT6PnnntOvvnmGxOsUjr9r0+fPqa5OpylRYsW8uOPP5pghvZy1KvGGtxYsWKFdO7c2e7lAVcVbWXQo0cP2bdvn+khplNwNSPu66+/lp9//tnu5eEy1q5dG//1008/bVqNaFacZosrvaCjZbhk5fgnLtIBWWP9+vXmoo7S4WreGBIEWIOSagBZRoNR3m/gWm4bGRlpMuKUTjfW0gYtM2KqsbNoUENvwcFx17G0B6enL84jjzySYHAFgMynGY6vv/66KenTEmr9UKWByNatW9u9NFyGltLqe+XlTtH1MdrPEf6FUlz/wPMIAGQ4AshCHTt2tHsJyMQPyN79prp27WpuAOzRuHFj00sVzqOtKAA4GxlyAEDAEUAW0inG8O9m+OPGjTO9xTx9qLS8Ol++fHYvDQAcI3HvTQDOQxGh72vevHmqgeE//vgjS9cD+CMCjgCADPvzzz+lQ4cOkitXLqlXr57Z9sEHH5iSzp9++kmaNGli9xIBv6aBfe1BVaBAATOYIrUPUTrgCb5L++Gmlb7uwr+QGecsp0+fNoEpHexUpUqV+O0bNmww08bhu2rVqpXgfnR0tBnYpL0dtQ8ygIyjhyOALHO5D8He+EDsLNWrV5eGDRvKJ598IkFBQWab9hZ7/PHHTS/HdevW2b1EwK999dVXpo2B9sH98ssvU32t5YOUb/NuT6ES93P0fm7p4eh/6P3n27p06WIuoj755JNy4cIFMy1+165d5hjV/tW333673UtEBg0cOND0Pn733XftXgrgeAQcAWTpB2KPY8eOyZtvvilt2rQxgSq1ePFimTlzpplSrVOO4RwRERHmqrBe4femk3H1CrKelAMA0mf27NnSr18/GTJkSIL3yldffdVsu+mmm+xeIizOjNuzZ4/JjPNcvINvKVKkiDlX1UDj+PHjTbsgDRDrOe6YMWNk9erVdi8RGbRt2zZp0KAByQ+ABSipBpBlvLNq9AqwltvqFWKPp59+WkaNGmU+YBFwdBadgKu9GxMHHHWbnpQDyDoaqDhw4IAUKlQowXa90KPbyIpzjmeeeUZGjx4tN954Y/w2vVCXLVs2efjhh+N75sJ5mXHafiS5zLiSJUvavVSk4tSpU/G9qWfMmGGeNz0e27dvL3369LF7ebCAXtQJDw+3exmAXyDgCMAWenX47bffTrK9bdu28uKLL9qyJqTP2rVrEwSLe/fuba4KX3/99WbbkiVL5KOPPpKhQ4fauErg6pNS8UpkZKSEhoZm+Xpw5bZv3y558uRJsj137twmWAVn9Tp+5ZVXzNfTpk0zx+nJkydNZpxWfFCK6wwaENaAlAYdNeCowWLP4DyCVM7SuXPnBPf1mNSLdStWrDDVVgAyjoAjAFvkz59ffvjhB3n++ecTbNdtug++T0ulE/cW69u3b5LH3XPPPXLXXXdl8eqAq48OalJ6XI4dO1Zy5MgRv0+zGjXgUblyZRtXiPSqX7++PPfcc/LNN99I4cKFzbZDhw6ZTCot+YNzkBnnP1nH3bp1M6+vpUqVkmbNmpnt+vqq/azhHHrhJnH/XK3U0Qqs1q1b27YuwJ8QcARgi0GDBslDDz0k8+bNk+uuu85sW7p0qTkJ/+yzz+xeHtJg586ddi8BgJcRI0aYf/UigJbheveA08zGMmXKmO1wjs8//1w6depkAhueUlvt8VexYkWZPn263ctDOpAZ5x90GJ6et+7evdsEpTxDnnTIz+DBg+1eHtLhiy++sHsJgN9jaAwA22iAUTNyPD2otGm6luZ6ApAAgPRr3ry5TJ06VfLmzWv3UmABPVWfNWuWbNq0Kf69slWrVqlOIofv+fjjj03rEU9mnA4X0WDVhx9+aI7XuXPn2r1EpECzjN944w3Jnj27+To1w4cPz7J1wRorV66M/yxSrVo1qV27tt1LAvwGAUcAQIZ5yoqaNm1q/i1fvrzdSwLg1c+R4BTgG4ENT2acBq/UL7/8Yi4ONGrUyO7lIZWLONp3U/up6tcp0ddZnTwOZzh8+LB07drVVFt5euVqX1V9jjUDuWDBgnYvEXA8Ao4Asszp06fT/NhcuXJl6lpgrf/973+mf5GetOngmOLFi5vgoycAqeV/ALLOuHHjTIn11q1bzX09BrX3mLaygDN6caaFVgXAd5EZB/gu7S++Y8cO+frrr03muNqwYYP06NFDKlSoIBMmTLB7iYDjEXAEkGW0dOhyWTb6kqSP0QEHcCad8Dd//nz5+eefZeLEieJyuXg+gSzUv39/E7x46qmnpGHDhmab9o4bNWqUPPvss6YhPnxX2bJl0/Q4fa/UD8vwXWTGAb49NGb27NlmOJe3ZcuWmSxkzXYEkDEMjQGQZehP5N/Onz8vCxcuNFmO+lxrf6prr702foIjgKzxySefmOFbd999d/y2Dh06SI0aNUwQkoCjb2Mgl3+e93AOBPgWvSAeEhKSZLtu030AMo4MRwC20SuHWvbnadRctWpVefDBB80VRziL9p7SAKOWpHh6OTZp0oShFYANNJtq+fLlSVoZbNmyRRo0aEDWhkPRjxMArHPbbbeZ90MtnS5WrJjZtm/fPunWrZs5f9XsZAAZE5jB7weAK7JixQrTH0V7jB0/ftzc9GsdNrJq1Sq7l4d00ump2qOqcuXK5qaBR4KNgD3uu+8+k+WY2JgxY8wHKTiL9herXr26REREmJtmqn7zzTd2LwsAHE3bjGh/+TJlypjPH3rTr3WbTo8HkHFkOAKwRePGjU3AUcv+goPjujvExMSYgQbak0oHkMA59K1k3bp1ppxa+zfq8xcaGmoyHbVvVa9evexeInDV0LJpDVKVLFlSrr/+erNt6dKlZjpu9+7dE5SQMajCt+nz89prr8mTTz4pN9xwg9mmrSs++ugjefPNN01PTgDAlZ+/zpkzJ77aSi+Yt2rVyu5lAX6DgCMAW2iWhpbgajacN50OV69ePdMPEM6kbysrV640V46//fZbhsYAWSy14RTeGFThjAEygwYNMoFib1999ZUMHDiQfo8AkAEabNTb4cOHk/Rt/Pzzz21bF+AvGBoDwBa5cuUy2TaJA4579uyRnDlz2rYuXBktg9fsRr1p9s2ZM2dMCaBmWmmWI4Csw3AK/3HgwAHTIzcx3ab7AABXRi/m6BA1TXQoWrQo/XGBTEDAEYAt7rrrLjMg5t13343/MLVo0SLp06dPgsmqcAYdRFG7dm0TXNTyaR0Yw/AfAMgYbT0yadIkefnllxNsnzhxYpKhQACAtBs9erR8+eWXpu8xgMxBwBGALTTQqFcStUxMezcq7Sv22GOPydChQ+1eHtJJh/5o1ioA+128eNE0vNdMx+TKxBjM5awMHL1Ap31xPT0c9eKclgBqIBIAcGWioqKSzSAHYB16OAKwlfZq3L59u/lap8Nly5bN7iXhCp08eVImT55snk/NVM2XL58JbBQuXFiKFy9u9/KAq4ZOov7999/ljjvuMMdf4jKxAQMG2LY2pJ/2xB0xYkSCoQbPP/+8ySoHAFyZfv36SY4cOcxgLgCZg4AjACDD1q5dKy1btpQ8efLIrl27ZPPmzVKuXDl59dVXTa9OnZgLIGtoO4Nff/01PiMO/k8rAx599FHzGgwASN5zzz0X/7Vm/+sArho1apibVlp5Gz58uA0rBPwLJdUAAEtO4O6//34ZNmxYgqE/7dq1k3vuucfWtQFXG80oZvjW1WXIkCHSpUsXAo4AkIrVq1cnuF+rVi3z7/r16xNsZ4AMYA0CjgCADFu+fLl8+umnyQY+Dh48aMuagKvVe++9Z0rFtCF+6dKl7V4OsgAFSwBwedrbGEDWIeAIAMiwsLAwOX36dJLtW7ZskYIFC9qyJuBqVa9ePTM4RtsaaF/cxGViOuQJAAAAyEwEHAEAGdahQwd5/fXX46emaimK9m7ULKvbb7/d7uUBV5W7775b9u3bZ8pskxsaAwAAAGQ2hsYAADLs1KlTZiLuihUr5MyZM1KsWDFTSn399dfLb7/9JtmzZ7d7icBVQ7MaFy9eLDVr1rR7Kcgi2rPz77//NlmtAAAAvoAMRwCAJVNxZ82aJYsWLTIfes+ePSt16tSRVq1a2b004KpTuXJluXDhgt3LAAAAwFWMDEcAgCXmzJljbocPHxaXy5Vg3+eff27buoCrze+//y6DBg2SwYMHS/Xq1ZP0cMyVK5dta0PmaNeunYwbN06KFi1q91IAAAAMAo4AgAzT4Ib2cNRhFfqBN3HPuGnTptm2NuBqExgYGP+197Gop3x6PzY21qaVIb1WrVplAsYaOFY//PCDfPHFF1K1alUZOHCghIaG2r1EAACAZBFwBABkmAYZhw0bJvfdd5/dSwGuevPnz091f9OmTbNsLciY+vXry4svvmiGb+3YsUOqVasmnTp1kuXLl0v79u1l5MiRdi8RAAAgWZcugQMAcIWioqKkUaNGdi8DwH8BRc1y/Oyzz0ywqkKFCmabTo4PCgqye3lIhy1btkitWrXM199//700adJExo8fL19++aVMmTLF7uUBAACkiIAjACDDHnroIfMhGID9NBDVpk0biYiIkNWrV0tkZGT8NPkhQ4bYvTykgxYieXrizp492/RqVCVLlpSjR4/avDoAAICUMaUaAJBhFy9elDFjxpgPxDVq1EgypGL48OG2rQ242rz55psyevRo6d69u3z33Xfx22+44QazD86hfXH1OWvVqpUplf/kk0/M9p07d0rhwoXtXh4AAECKCDgCADJs7dq18WV/69evT7Av8QAZAJlr8+bNpvQ2sdy5c8vJkydtWROujPZo7Natm0yfPl1eeeUVUx6vJk+eTBsLAADg0wg4AgAybO7cuXYvAcB/ihQpItu2bZMyZcok2L5w4UIpV66cbetC+mnG+Lp165Jsf+edd+jHCQAAfBo9HAEAAPxIr169pHfv3rJ06VKTYbx//3759ttv5YUXXpDHHnvM7uXBAuHh4UlaVwAAAPgSMhwBAAD8iE6m1kEjLVu2lPPnz5vy6rCwMBNwfOqpp+xeHi4jb968aW5Fcfz48UxfDwAAwJUIcOv4OwAAAPiVqKgoU1p99uxZqVq1quTIkcPuJSENvvrqqzQ/tkePHpm6FgAAgCtFwBEAAAAAAACAZSipBgAAAHzcxYsXTdaqt1y5ctm2HgAAgNQwNAYAAADwQefOnZMnn3xSChUqJNmzZzf9Hb1vAAAAvoqAIwAAAOCD+vbtK3/88Yd88sknZvDP2LFjZdCgQVKsWDH5+uuv7V4eAABAiujhCAAAAPigUqVKmcBis2bNTPn0qlWrpEKFCvLNN9/IhAkT5Ndff7V7iQAAAMkiwxEAAADwQcePH5dy5cqZrzXgqPfVjTfeKH/++afNqwMAAEgZAUcAAADAB2mwcefOnebrypUry6RJk8zXP/30k+TJk8fm1QEAAKSMkmoAAADAB40YMUKCgoLk6aefltmzZ8utt94qeuoeHR0tw4cPl969e9u9RAAAgGQRcAQAAAAc4N9//5WVK1eaPo41atSwezkAAAApIuAIAAAAAAAAwDLB1v0oAAAAAFZ5/fXXU93fv3//LFsLAABAepDhCAAAAPig2rVrJ7ivvRt1iExwcLCUL19eVq1aZdvaAAAAUkOGIwAAAOCDVq9enWTb6dOnpWfPntKpUydb1gQAAJAWZDgCAAAADrJu3TozsXrXrl12LwUAACBZgclvBgAAAOCLTp06ZW4AAAC+ipJqAAAAwAd98MEHCe5rYdKBAwfkm2++kZtvvtm2dQEAAFwOJdUAAACADypbtmyC+4GBgVKwYEFp0aKFvPTSS5IzZ07b1gYAAJAaAo4AAAAAAAAALEMPRwAAAAAAAACWoYcjAAAA4IM6deokAQEBaXrs1KlTM309AAAAaUWGIwAAAOCDcufOLXPmzJEVK1bEb1u5cqX88ccfkitXLrPfcwMAAPAlZDgCAAAAPqhw4cLSpUsXGT16tAQFBZltsbGx8vjjj5uA4zvvvGP3EgEAAJLF0BgAAADAB+lE6oULF0qlSpUSbN+8ebM0atRIjh07ZtvaAAAAUkNJNQAAAOCDYmJiZNOmTUm26zaXy2XLmgAAANKCkmoAAADAB91///3y4IMPyvbt26VBgwZm29KlS2Xo0KFmHwAAgK+ipBoAAADwQZrF+O6778r7778vBw4cMNuKFi0qvXv3lueffz6+ryMAAICvIeAIAAAA+LjTp0+bf3VYDAAAgK+jhyMAAADgw30cZ8+eLRMmTJCAgACzbf/+/XL27Fm7lwYAAJAiMhwBAAAAH/Tvv/9K27ZtZffu3RIZGSlbtmyRcuXKmZJqvT969Gi7lwgAAJAsMhwBAAAAH6SBxXr16smJEyckIiIifnunTp1kzpw5tq4NAAAgNUypBgAAAHzQggUL5K+//pLQ0NAE28uUKSP79u2zbV0AAACXQ4YjAAAA4KNTqmNjY5Ns37t3r+TMmdOWNQEAAKQFAUcAAADAB7Vu3VpGjhwZf1+HxuiwmAEDBki7du1sXRsAAEBqGBoDAAAA+CDNZGzTpo3o6frWrVtNP0f9t0CBAvLnn39KoUKF7F4iAABAsgg4AgAAAD4qJiZGJk6cKH///bfJbqxTp45069YtwRAZAAAAX0PAEQAAAHCw9u3by9ixY6Vo0aJ2LwUAAMCghyMAAADgYFpefeHCBbuXAQAAEI+AIwAAAAAAAADLEHAEAAAAAAAAYBkCjgAAAAAAAAAsQ8ARAAAAAAAAgGUIOAIAAAAAAACwDAFHAAAAwEenT8fExCTZrtt0n8fLL78s+fLly+LVAQAApCzA7Xa7U9kPAAAAwAZBQUFy4MABKVSoUILtx44dM9tiY2NtWxsAAEBqyHAEAAAAfJDmBQQEBCTZrgHH7Nmz27ImAACAtAhO06MAAAAAZInOnTubfzXY2LNnTwkLC4vfp1mNa9eulUaNGtm4QgAAgNQRcAQAAAB8SO7cueMzHHPmzCkRERHx+0JDQ+X666+XXr162bhCAACA1NHDEQAAAPBBgwYNkhdeeIHyaQAA4DgEHAEAAAAAAABYhqExAAAAgA86dOiQ3HfffVKsWDEJDg42U6u9bwAAAL6KHo4AAACAD9KBMbt375bXXntNihYtmuzEagAAAF9ESTUAAADgg3RgzIIFC6RWrVp2LwUAACBdKKkGAAAAfFDJkiXNpGoAAACnIeAIAAAA+KCRI0fKiy++KLt27bJ7KQAAAOlCSTUAAADgg/LmzSvnz5+XmJgYyZYtm4SEhCTYf/z4cdvWBgAAkBqGxgAAAAA+muEIAADgRGQ4AgAAAAAAALAMGY4AAACAD9q9e3eq+0uVKpVlawEAAEgPMhwBAAAAHxQYGCgBAQEp7o+Njc3S9QAAAKQVGY4AAACAD1q9enWC+9HR0Wbb8OHDZfDgwbatCwAA4HLIcAQAAAAc5JdffpF33nlH5s2bZ/dSAAAAkhWY/GYAAPD/9u6nJcouDAP4XWSEYBr2B4KIShlJU4IgaOmmTZvaRiupZUIhuAqXFgSu+wBS9AlM6B+1SCELo0hMQcFAyIURRlC8PA8UDDr2+jK85wi/Hwwzcx4X11KuOefcAZChSqUSk5OTqWMAANTkSDUAAGRodXW16ntxMOnz588xNDQU7e3tyXIBAPyNwhEAADLU0tKybmhMUToeOXIk7t+/nywXAMDfuMMRAAAy9OzZs3VTqw8cOBBtbW2xa5d9AwBAvhSOAAAAAEDd+GkUAAAy9enTpxgZGYkPHz6U30+ePBn9/f1x4sSJ1NEAAGoypRoAADI0NjZWFowTExPR3d1dvl69ehWdnZ0xPj6eOh4AQE2OVAMAQIZOnz4d58+fj+Hh4ar1wcHBePToUbx+/TpZNgCAzSgcAQAgQ3v27Inp6elob2+vWp+ZmSl3O37//j1ZNgCAzThSDQAAGSomUr9582bderF28ODBJJkAAP4NQ2MAACBDV69ejWvXrsXc3FycO3euXHv58mXcvn07bty4kToeAEBNjlQDAECGin/TiwnVd+/ejaWlpXLt8OHDMTAwENevX48dO3akjggAsCGFIwAAZO7r16/le1NTU+ooAAB/pXAEAAAAAOrGHY4AAJChL1++xK1bt+LJkyexvLwcv379qnq+srKSLBsAwGYUjgAAkKErV67E7Oxs9PX1xaFDh9zZCABsG45UAwBAhor7Gl+8eBE9PT2powAAbMnOrf05AADwf+jo6Ii1tbXUMQAAtswORwAAyNDk5GQMDg6W9zh2dXVFQ0ND1fO9e/cmywYAsBl3OAIAQIZaWlpidXU1ent7q9aL/QLFfY4/f/5Mlg0AYDMKRwAAyNDly5fLXY2jo6OGxgAA24oj1QAAkKHGxsaYmpqKSqWSOgoAwJYYGgMAABk6c+ZMLC4upo4BALBldjgCAECGHj58GENDQzEwMBCnTp1aNzSmu7s7WTYAgM0oHAEAIEM7d9Y+jGRoDACQM0NjAAAgQ/Pz86kjAAD8JwpHAADI0NGjR8v39+/fx8LCQvz48aNqh+Pv5wAAuVE4AgBAhubm5uLixYsxPT1dFoy/b0IqPhccqQYAcmVKNQAAZKi/vz+OHTsWy8vL0djYGO/evYvnz5+X06ufPn2aOh4AQE2GxgAAQIb2798fjx8/LqdRNzc3x8TERFQqlXLt5s2bMTU1lToiAMCG7HAEAIAMFUemm5qa/pSPS0tL5efi7saPHz8mTgcAUJs7HAEAIENdXV3x9u3b8lj12bNn486dO7F79+64d+9eHD9+PHU8AICaHKkGAIAMjY2Nxbdv3+LSpUsxOzsbFy5ciJmZmWhtbY0HDx5Eb29v6ogAABtSOAIAwDaxsrIS+/bt+zOpGgAgRwpHAAAAAKBuDI0BAAAAAOpG4QgAAAAA1I3CEQAAAACoG4UjAAAAAFA3CkcAAAAAoG4UjgAAAABA3SgcAQAAAIC6UTgCAAAAAFEv/wDWuUpxZxBMUgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get only numeric columns - this automatically includes your type_encoded column\n",
    "numeric_cols = df.select_dtypes(include=['number']).columns\n",
    "corr = df[numeric_cols].corr()\n",
    "\n",
    "# Visualize the correlation matrix\n",
    "plt.figure(figsize=(14, 12))\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm', linewidths=0.5, annot_kws={\"size\": 8})\n",
    "plt.title('')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zxhlnBMyAUOFlLScXzZ7wn9mjGTJaUuylvJFq1aq51Q7yV8uGNSNDy5EHDx5szZo1CzinlFeatVLS42hWi5abezQz3/u+/rO+NGNKs8Q0e0jfy6PZZSpup+fYm+E1bdo097xqNpZm0PjP3tH381Zr5ObmumP63Jux4s1G+SveLBXNsiq63FezubRk2j+PrVZJaGZZixYt3MocHfNolpT/77es9LPrd+pfq0MzwvRza3XPBx984Hu96OfWtWpVgz9vVUYohe+08kMzbkr6KGlVg1Kpeb8L7+fXLDDNUtMybf/fk14H3jWVddl6MJqJ9vHHH7vfl14nel2Lvpe+p643FJrVqNUiBx10kNvX7EG917WaQzOY9F7V+7qs9LrT70YrOjTLyZ+35Lo8P39pNPtQNOvMn95fmtWnWi8U5gMAILY0u14rCvz7UFo9XbSvHGr/VysKxD/1jfp4WsXg9WO9NDNeGkzVfvO+zp/Svaif59FsbM3G19hCM8u9VeOR6Pdo3KHVvHpONM7wp1n0mlFf0uOU9bnUSmalxNHsc++86Os0c15jG60o0ApyrWDR86TZ8Orj+4+nNLNe4yP93GVZWRKM+sriv3JfM9JFz7Fm2Hs0JvCe91deecW36iFafb6yjj+1WkWUitc/TZRWk2i1iLcqoqzUxy9pXKLUT8Hod+NfN0KvhfKMactKKxv0M2vs0KpVK99xjWX1c4RCqyn0utLzrutXGjONQx544AG3ikIrGPT8e6mm/ore30qZpfGk/1jWG996v9u9MTbRc++9ZvzHtlrVpRVbuveh3xmA8FUvx9cCqKSUF1X/2HXjWB36ohS8UIdYSz3V4dXy1GC++eYbt/WWDvvTMmndRA2Wg1W0hDPY1xxwwAHua5T+Rh0ldcr9O+aiZaHqPHrLzIvm+gw2GNDyYH8qKKbvofQ/3g3+YMtag3VEtOxY/OuEeAOookvWRTen/W+ae0vfvQFMUbphrvyays2pZdP+HXb/QUdp/NsVzc8a7Ka4NzjU9fsHLzzqJPrnHi0L5ZYN9trRcm/dtNdz5g0Q1BFV8EIBCy8woMGMls1rCbly54bSWfbynQZz4IEHlrrM2Z/SCBSl14vyNHs1O/7q9Vca73Wj10LRotsahOrn0JLwUGhgo9+jHlupAbRVIEOveeVQ1ocGS0rLFux37U95XfXhT0HLn376yS2tFz2uPrzgS6Qo36+uXwNxDXL0d0Z/rzRQ8L8JAQAAYqesfeVQ+7+62a+xgG4a68alAgpemiJN2NA4xBuLiDcuUICkLOMO9c/VV1XgQzf5NdEjEv0e3djWZDB/6osr+KIJQ97N1WD9x0iMO9SfVComj9d/V78qWL9Pz7v6hP7BoFB4P4cXdFH/Xc+XxpMa2wX7edTn1hhMz4dSjkarz1fW8acCAQoe6LWnlEW6vnAnVonSDfkHcvwphVowwcZr5RnTllVp43sFBzVBTYGxstINf6UiVlBQgTf9HdD3UAo4BS6UTk6p1pTSLVihb3/6G6AAWFEK5mmsoxR15R2blUSviX333df9HVAwUxMcvRTJ+juiDwDlk3ABjOzsbHdTSZ2fstA/9JJy+emfjH+edqCyUJ0LKW3miNfJ8mbElPY46vSV9BglBTBK6qwp6KFOiD68lSGa8aC6DOrwqEOsDo9/R7ksBdTUudOMfwVlNINJRca9zldpj+PNKvLnDVj8AwslzRwKxntO1AEL1gnzn50vCjQpIKHOqH72kp5vf971aIaZOlv+iu77X5M3c7+okjrdpSnpd+wFEPyLrKkDqOMaPOladB3qzOr3FuogQR3logPHsgj2uxYNCjQrSzf+VSfEGxyE8vorz3soHHqNaiDuFbhU0E/vH82q0gBRsw01SC7LoFBfq69TbRsFDvXa0s/sP3NybxQx1AxEBYr0OtD/dH1oUK7VQCr2p0FopIMmALC3xhyifodqa+kmmFZl6maUZkTrBh5QUZW1rxxq/9e7Yar3mG546nMFMHQDVRNbdFNZN+qXLFniVhCrn6JJLMGKe5c27ijaJ41Uv0cBFa1W10QU9SW9m8ql1U3YG+MOjXnE60uV5XkPhdcv9q7d+z2rEPtfrSRWW/39i1afL5TxpyZZKYChv9d6remmuMaRCsz4r7YvC9VDCXU8E2y8Vp4xbSTGJvqdKADlvaZCocfTSnB9iK5dq7K0QkOrdzRpTPtFJ3QFo6Cjar2oeLxWZCnIGKmfvyQaU6s2iAIx+v76EL1eNOFMK8gOP/zwiH9fIJEkVABDyxBVuKjoUs2/ioZ7s2g9KpClP6BamgdURmW94S+ldSL+qnBZaR1NpeApjQYnXpDxyiuvdEu71fHRe1YzslSAWDd3lbLpr+hrVfxOnVDN5NHSUy1Z1UwKDYC05NN/BlcoP6MnlJko3gBEnXJvVlUwClx416CbHJodpo9evXr95ffwCv0FW1UQ7GfyBlUlvTbC6QiW9XcsmhGmGVejR492gxfNvNNNdh3XLLtoCDYrTQNjLVvX71cdVA2KtWRbryFveXUovPdVJN5DJa2s0kwgLaP2p6XOWlatD900UFopPc9/FcDQ4ELvHc2QUmBIM6M0kNSAVK/fYGkawlH0eREF7RSIUlE/LTfXKiHNetINBX3o5sKLL75YpoEOAMR6zCH6H6diuZrtrP8pSrNy+eWXuwLGpfUHgMog1P6vfwBDfQB9rnGBZjorUKHHUT9Nx9TP0Q1w9XOC9Qv+qk/qrVyORL9HP6fuI2i1sR5XfXj1ZRVkUUFk/TxKTVQe4Yw79Lypj1iScAtSe7PevWCF16fTDXhNUCqN11+NVp8vlLGJVuKoz6xgwb333usmACnwrFTCGk/ubcF+H+UZ05a1/x2psYkCPnpPaqvXfVG6Zn3ovaEUUwqgaSKb0q+V5r777nMTyzRu0zhXWQJ0b0ATsz7//HNXqD4Sgj03Wj2mrAozZsxwAUpNDlNwUuMq9Qu0OibU4BaABAtgKFKufyrqvJT2TzkY/RP0v9Gjfwr6R6UbaUppAlRG3qBBS5lLon/G/rUUglFAQR19PY465SXN4AhG54LN9veuSed0g/jOO+9070vlPy16s1XLjsviueeecx093Xx+4oknrEGDBgHnlY+zvPR3ZNmyZa7zFey50M14zdxQPQ/vb45mE5UlACPq2Cl4oQ58WQIYaud9XVl4v4uSXhPhzMoq6ffvfY+is8b0N1edTqWS6t27txsQaeBTlhUne4PSFeh1p9ehZsz658wVzRIqKQhSUp7eoq8172craSZTae+hYDfG9HvXzKBBgwaV2E4zytTR9mqylOaf//ynG8TrBtsNN9wQMGgpWlvlr3gDomADAs3SK4kGJRrQ6kPpEzRo0OBFM58mT55cYu5gAIinMYfoxoduhno3aG677TYX0NCkg7L8bwcqsnD6v0ojpGCFVl54qyG8mexefTbd9PQCEMHSR5WlT+r1gyPR71GfX8EL3dBXkLJoP1YrPCI1liupf66/NbpxrL813vOuG93XX3+9RZJmvnupwY4//ni39b6fggWhroje232+sow/PQqKaTyiFRgKqHi1+2J5jyicMW2o/W+9trTiXGMT/T78aXzj1YL4Kxpn6LlS2icFVUpLuabXjsZ/f/Ue03tdwQv9npRyquiYO5Q6f6GO2TwKHJ1yyinuQ/R3SeNXBSX/9a9/EcAAyiEhinhr6Zg6LfqjESxvpDoQ6uhoBoX+oGjGVEl58fTHR/8sNeMWqKzUUdASWi3XDJbvVJ0ZzWAQL+dnMN4S7WA3clWkTzf0S6LZNEWpc6qBiW4KqLOvDpICKeqcBZsp7q2e+quibl7OWtXSKNrR080IdTzK8jil8WaWePl3iw5UFIi5++673cwe7zkN1la0lFaDO9009yioqtma+pm1ZLY0mgGi36t+z2UNYHi/S11TsOchnHR6et6DDdK8zmXR+iv6+XRs3rx5viXuoS63jiQF5xTEUAG7osEL/9ef/+oUDbIlWDE6PZ5XJNJ/Jo860BoY6XsVVdJrJBitThIVv/SWUgfjvS/9C/OVNNvKW8mjVVBFBx7e3wjxf82U9FilPTfe9/FokHXhhRe6gbf/z6IgoGZaea/rcNMdAEAsxhxJSUmujVbL6e+cgs6aTBVstSRQ2YTT/9X7Q31UzeTWDWz/wIXqrCn9qG6Oana8+h8lrZAINu7QDVrVDdANVq9WQzj9npLGHbp5WzR4ob6ed35vjTv0t0U3/TWhRSs1vOddz0Gw76lxnPpWQ4cODfk69DvTTWf9zfOeQ62Q1xhEYyxvdYY/3WtRP05jO+9vYbT6fGUZf/rzavXptafn2pvpHyvhjGlD6X/7B6KCBQMUvPevxVIaBUL0WtBr/q233iq1rQIm4hUhL4l3vQr4Fw1e6HXkBdP8x2bhjEuCjdn0f1//10eNGhVwXK8JTXiTklJnAyibhAhgqHjVk08+6ctX6M8rBqW8g1piqllT+gekfLNFKbe8ZqWqw1JSzkGgstDyU7nrrrt8qy1EnQy9P3TjWbOYSsoV6t1UV77T8ePHu1kTHn3t7bffXmraIc2a8DphXqdL1yLKgyrKMaobBZoBoQJq/tSp8joQf1Woy1uKrpsG/tekgcs111zjW4a9fft2C5dmkmmmkWp1+Kel07VpEKEUTVqKrMGQin5pdpIGDM8//3zANWlWjWbUaKDmnzdWgyvN6tCsD3WStES16A1v7Y8cOdKGDRvmAiUqmKbZQ2Wh/LP6UB5hPYZ/x/f11193M6BCXV6u14GCNv6F5LT8WwNAva40q6kob1aTgjB6bQULHESL97rRDX/VXvHo96XfsxdI8n/dKPepBtt6T3300Ue+43oN63VQlFY46XWhwZ+CXP6vZf0/Kvq6L41m9er7axCgmYPeIMafHk+zCxU08d5n/kvqiwacvOegaABLNwv0OvP4X7e3vL/orC7vBp1eT/7tNaOtaBBU7xO9/zWg1OvRf9aYZnTpf7v8VbE/AIinMYf+zqt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      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the style for nicer plots\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "\n",
    "\n",
    "# Create a figure with 1 row and 2 columns\n",
    "fig, axes = plt.subplots(1, 2, figsize=(16, 8))\n",
    "\n",
    "# 1. oldbalanceOrg boxplot\n",
    "sns.boxplot(x='isFraud', y='oldbalanceOrg', data=df, ax=axes[0], \n",
    "            palette=['skyblue', 'coral'])\n",
    "axes[0].set_title('oldbalanceOrg by Fraud Status', fontsize=16)\n",
    "axes[0].set_xlabel('Fraud (0=No, 1=Yes)', fontsize=14)\n",
    "axes[0].set_ylabel('oldbalanceOrg', fontsize=14)\n",
    "\n",
    "# 2. newbalanceDest boxplot\n",
    "sns.boxplot(x='isFraud', y='newbalanceDest', data=df, ax=axes[1], \n",
    "            palette=['skyblue', 'coral'])\n",
    "axes[1].set_title('newbalanceDest by Fraud Status', fontsize=16)\n",
    "axes[1].set_xlabel('Fraud (0=No, 1=Yes)', fontsize=14)\n",
    "axes[1].set_ylabel('newbalanceDest', fontsize=14)\n",
    "\n",
    "# Adjust layout and save\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>oldbalanceOrg</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>emptied_account</th>\n",
       "      <th>amount_equals_oldbalanceOrg</th>\n",
       "      <th>is_CASH_OUT</th>\n",
       "      <th>is_TRANSFER</th>\n",
       "      <th>hour_risk_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>170136.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21249.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41554.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362615</th>\n",
       "      <td>339682.13</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362616</th>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362617</th>\n",
       "      <td>6311409.28</td>\n",
       "      <td>6379898.11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362618</th>\n",
       "      <td>850002.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362619</th>\n",
       "      <td>850002.52</td>\n",
       "      <td>7360101.63</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6362620 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         oldbalanceOrg  newbalanceDest  isFraud  emptied_account  \\\n",
       "0            170136.00            0.00        0                0   \n",
       "1             21249.00            0.00        0                0   \n",
       "2               181.00            0.00        1                1   \n",
       "3               181.00            0.00        1                1   \n",
       "4             41554.00            0.00        0                0   \n",
       "...                ...             ...      ...              ...   \n",
       "6362615      339682.13       339682.13        1                1   \n",
       "6362616     6311409.28            0.00        1                1   \n",
       "6362617     6311409.28      6379898.11        1                1   \n",
       "6362618      850002.52            0.00        1                1   \n",
       "6362619      850002.52      7360101.63        1                1   \n",
       "\n",
       "         amount_equals_oldbalanceOrg  is_CASH_OUT  is_TRANSFER  \\\n",
       "0                                  0            0            0   \n",
       "1                                  0            0            0   \n",
       "2                                  1            0            1   \n",
       "3                                  1            1            0   \n",
       "4                                  0            0            0   \n",
       "...                              ...          ...          ...   \n",
       "6362615                            1            1            0   \n",
       "6362616                            1            0            1   \n",
       "6362617                            1            1            0   \n",
       "6362618                            1            0            1   \n",
       "6362619                            1            1            0   \n",
       "\n",
       "         hour_risk_level  \n",
       "0                      0  \n",
       "1                      0  \n",
       "2                      0  \n",
       "3                      0  \n",
       "4                      0  \n",
       "...                  ...  \n",
       "6362615                0  \n",
       "6362616                0  \n",
       "6362617                0  \n",
       "6362618                0  \n",
       "6362619                0  \n",
       "\n",
       "[6362620 rows x 8 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation of features with fraud:\n",
      "isFraud                        1.000000\n",
      "amount_equals_oldbalanceOrg    0.989029\n",
      "hour_risk_level                0.170055\n",
      "emptied_account                0.062091\n",
      "is_TRANSFER                    0.053869\n",
      "is_CASH_OUT                    0.011256\n",
      "oldbalanceOrg                  0.010154\n",
      "newbalanceDest                 0.000535\n",
      "Name: isFraud, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Show correlations with fraud\n",
    "fraud_correlations = df.corr()['isFraud'].sort_values(ascending=False)\n",
    "print(\"Correlation of features with fraud:\")\n",
    "print(fraud_correlations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape is :  (6362620, 7)\n",
      "\n",
      "y shape is :  (6362620,)\n"
     ]
    }
   ],
   "source": [
    "# X Data\n",
    "X = df.drop(['isFraud'], axis=1)\n",
    "print('X shape is : ' , X.shape)\n",
    "print()\n",
    "\n",
    "# y Data\n",
    "y = df['isFraud']\n",
    "print('y shape is : ' , y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape is  (4453834, 7)\n",
      "X_test shape is  (1908786, 7)\n",
      "y_train shape is  (4453834,)\n",
      "y_test shape is  (1908786,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, shuffle=True)\n",
    "\n",
    "# Splitted Data\n",
    "print('X_train shape is ' , X_train.shape)\n",
    "print('X_test shape is ' , X_test.shape)\n",
    "print('y_train shape is ' , y_train.shape)\n",
    "print('y_test shape is ' , y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------- XGBoost Model Performance ----------\n",
      "\n",
      "---------- Training vs Test Performance (Overfitting Analysis) ----------\n",
      "Metric                   Training                 Test           Difference\n",
      "--------------------------------------------------------------------------\n",
      "Accuracy             99.99946114%         99.99931894%          0.00014220%\n",
      "Precision            99.96508989%         99.83733225%          0.12775764%\n",
      "Recall               99.61732475%         99.63474026%         -0.01741551%\n",
      "F1 Score             99.79090434%         99.73593337%          0.05497096%\n",
      "ROC AUC              99.93566535%         99.92032587%          0.01533947%\n",
      "PR AUC               99.66588739%         99.63693768%          0.02894971%\n",
      "\n",
      "Overfitting Score: 0.03495741%\n",
      "Model shows good generalization (score < 2%)\n",
      "\n",
      "---------- Detailed Test Metrics ----------\n",
      "➤➤➤ Accuracy: 99.99931894 %\n",
      "➤➤➤ Precision: 99.83733225 %\n",
      "➤➤➤ Recall: 99.63474026 %\n",
      "➤➤➤ F1 Score: 99.73593337 %\n",
      "➤➤➤ ROC AUC: 99.92032587 %\n",
      "➤➤➤ PR AUC: 99.63693768 %\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906322\n",
      "           1       1.00      1.00      1.00      2464\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       1.00      1.00      1.00   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGBoost Model\n",
    "# Scale the features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Calculate class weights since the dataset is imbalanced\n",
    "fraud_ratio = len(y_train[y_train==1]) / len(y_train[y_train==0])\n",
    "scale_pos_weight = 1 / fraud_ratio\n",
    "\n",
    "# Initialize XGBoost model with basic parameters\n",
    "xgb_model = xgb.XGBClassifier(\n",
    "    scale_pos_weight=scale_pos_weight, # Helps address class imbalance\n",
    "    learning_rate=0.01, # Learning rate for the model\n",
    "    n_estimators=200, # Number of trees in the model\n",
    "    max_depth=5, # Maximum depth of the trees\n",
    "    min_child_weight=1, # Minimum sum of instance weights (hessians) needed in a child\n",
    "    gamma=0, # Minimum loss reduction required to make a split\n",
    "    subsample=0.8, # Subsample ratio of the training instance  Uses 80% of data and features for each tree\n",
    "    colsample_bytree=0.8, # Subsample ratio of columns when constructing each tree\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "# Simple fit without additional parameters\n",
    "xgb_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Evaluate on training data\n",
    "train_preds = xgb_model.predict(X_train_scaled)\n",
    "train_probs = xgb_model.predict_proba(X_train_scaled)[:, 1]\n",
    "\n",
    "# Evaluate on test data\n",
    "test_preds = xgb_model.predict(X_test_scaled)\n",
    "test_probs = xgb_model.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "# Calculate metrics for both sets (correctly multiply by 100)\n",
    "train_metrics = {\n",
    "    'Accuracy': accuracy_score(y_train, train_preds) * 100,\n",
    "    'Precision': precision_score(y_train, train_preds) * 100,\n",
    "    'Recall': recall_score(y_train, train_preds) * 100,\n",
    "    'F1 Score': f1_score(y_train, train_preds) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_train, train_probs) * 100,\n",
    "    'PR AUC': average_precision_score(y_train, train_probs) * 100\n",
    "}\n",
    "\n",
    "test_metrics = {\n",
    "    'Accuracy': accuracy_score(y_test, test_preds) * 100,\n",
    "    'Precision': precision_score(y_test, test_preds) * 100,\n",
    "    'Recall': recall_score(y_test, test_preds) * 100,\n",
    "    'F1 Score': f1_score(y_test, test_preds) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_test, test_probs) * 100,\n",
    "    'PR AUC': average_precision_score(y_test, test_probs) * 100\n",
    "}\n",
    "\n",
    "# Format metrics with 8 decimal places\n",
    "print(\"\\n---------- XGBoost Model Performance ----------\")\n",
    "print(\"\\n---------- Training vs Test Performance (Overfitting Analysis) ----------\")\n",
    "print(f\"{'Metric':<12} {'Training':>20} {'Test':>20} {'Difference':>20}\")\n",
    "print(\"-\" * 74)\n",
    "for metric in train_metrics.keys():\n",
    "    diff = train_metrics[metric] - test_metrics[metric]\n",
    "    print(f\"{metric:<12} {train_metrics[metric]:>19.8f}% {test_metrics[metric]:>19.8f}% {diff:>19.8f}%\")\n",
    "\n",
    "# Calculate overall overfitting score (average difference across metrics)\n",
    "overfitting_score = np.mean([train_metrics[m] - test_metrics[m] for m in train_metrics.keys()])\n",
    "print(f\"\\nOverfitting Score: {overfitting_score:.8f}%\")\n",
    "if overfitting_score > 5:\n",
    "    print(\"WARNING: Model shows signs of overfitting (score > 5%)\")\n",
    "elif overfitting_score < 2:\n",
    "    print(\"Model shows good generalization (score < 2%)\")\n",
    "else:\n",
    "    print(\"Model shows moderate overfitting\")\n",
    "\n",
    "# Display individual metrics with arrows for visual emphasis and 8 decimal places\n",
    "print(\"\\n---------- Detailed Test Metrics ----------\")\n",
    "for metric, value in test_metrics.items():\n",
    "    print(f'➤➤➤ {metric}: {value:.8f} %')\n",
    "\n",
    "# Print classification report\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, test_preds))\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(y_test, test_preds)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix - XGBoost')\n",
    "plt.ylabel('True Label')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class weights: {0: 1.0, 1: 769.8262374524056}\n"
     ]
    }
   ],
   "source": [
    "# LightGBM Model\n",
    "\n",
    "# 1. Feature Scaling\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# 2. Calculate class weights\n",
    "fraud_samples = sum(y_train == 1)\n",
    "non_fraud_samples = sum(y_train == 0)\n",
    "class_weight = {0: 1.0, 1: non_fraud_samples / fraud_samples}\n",
    "print(f\"Class weights: {class_weight}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(bagging_fraction=0.8, bagging_freq=5, class_weight=&#x27;balanced&#x27;,\n",
       "               feature_fraction=0.8, is_unbalance=True, learning_rate=0.01,\n",
       "               max_depth=7, metric=&#x27;auc&#x27;, n_estimators=200, objective=&#x27;binary&#x27;,\n",
       "               random_state=42, verbose=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(bagging_fraction=0.8, bagging_freq=5, class_weight=&#x27;balanced&#x27;,\n",
       "               feature_fraction=0.8, is_unbalance=True, learning_rate=0.01,\n",
       "               max_depth=7, metric=&#x27;auc&#x27;, n_estimators=200, objective=&#x27;binary&#x27;,\n",
       "               random_state=42, verbose=-1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LGBMClassifier(bagging_fraction=0.8, bagging_freq=5, class_weight='balanced',\n",
       "               feature_fraction=0.8, is_unbalance=True, learning_rate=0.01,\n",
       "               max_depth=7, metric='auc', n_estimators=200, objective='binary',\n",
       "               random_state=42, verbose=-1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. Model Training\n",
    "lgb_model = lgb.LGBMClassifier(\n",
    "    objective='binary',\n",
    "    metric='auc',\n",
    "    boosting_type='gbdt',\n",
    "    learning_rate=0.01,\n",
    "    num_leaves=31,\n",
    "    max_depth=7,\n",
    "    feature_fraction=0.8,\n",
    "    bagging_fraction=0.8,\n",
    "    bagging_freq=5,\n",
    "    verbose=-1,\n",
    "    is_unbalance=True,\n",
    "    class_weight='balanced',\n",
    "    random_state=42,\n",
    "    n_estimators=200\n",
    ")\n",
    "\n",
    "lgb_model.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------- LightGBM Model Evaluation ----------\n",
      "\n",
      "Classification Report (Test Set):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.96      0.98   1906351\n",
      "           1       0.03      1.00      0.06      2435\n",
      "\n",
      "    accuracy                           0.96   1908786\n",
      "   macro avg       0.52      0.98      0.52   1908786\n",
      "weighted avg       1.00      0.96      0.98   1908786\n",
      "\n",
      "\n",
      "---------- Training vs Test Performance (Overfitting Analysis) ----------\n",
      "Metric           Training         Test   Difference\n",
      "--------------------------------------------------\n",
      "Accuracy     96.09817070% 96.11941831% -0.02124761%\n",
      "Precision     3.21788382%  3.17169789%  0.04618592%\n",
      "Recall       100.00000000% 99.63039014%  0.36960986%\n",
      "F1 Score      6.23512844%  6.14768638%  0.08744206%\n",
      "ROC AUC      99.99321876% 99.92273862%  0.07048014%\n",
      "PR AUC       99.66835778% 99.37808337%  0.29027441%\n",
      "\n",
      "Overfitting Score: 0.14045746%\n",
      "Model shows good generalization (score < 2%)\n",
      "\n",
      "---------- Detailed Test Metrics ----------\n",
      "➤➤➤ Accuracy: 96.11941831 %\n",
      "➤➤➤ Precision: 3.17169789 %\n",
      "➤➤➤ Recall: 99.63039014 %\n",
      "➤➤➤ F1 Score: 6.14768638 %\n",
      "➤➤➤ ROC AUC: 99.92273862 %\n",
      "➤➤➤ PR AUC: 99.37808337 %\n"
     ]
    }
   ],
   "source": [
    "# 4. Model Evaluation\n",
    "\n",
    "# Evaluate on training data\n",
    "y_train_pred = lgb_model.predict(X_train_scaled)\n",
    "y_train_pred_proba = lgb_model.predict_proba(X_train_scaled)[:, 1]\n",
    "\n",
    "# Evaluate on test data\n",
    "y_pred_lgb = lgb_model.predict(X_test_scaled)\n",
    "y_pred_proba_lgb = lgb_model.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "print(\"\\n---------- LightGBM Model Evaluation ----------\")\n",
    "print(\"\\nClassification Report (Test Set):\")\n",
    "print(classification_report(y_test, y_pred_lgb))\n",
    "\n",
    "# Calculate metrics for both training and test sets\n",
    "train_metrics = {\n",
    "    'Accuracy': accuracy_score(y_train, y_train_pred) * 100,\n",
    "    'Precision': precision_score(y_train, y_train_pred) * 100,\n",
    "    'Recall': recall_score(y_train, y_train_pred) * 100,\n",
    "    'F1 Score': f1_score(y_train, y_train_pred) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_train, y_train_pred_proba) * 100,\n",
    "    'PR AUC': average_precision_score(y_train, y_train_pred_proba) * 100\n",
    "}\n",
    "\n",
    "test_metrics = {\n",
    "    'Accuracy': accuracy_score(y_test, y_pred_lgb) * 100,\n",
    "    'Precision': precision_score(y_test, y_pred_lgb) * 100,\n",
    "    'Recall': recall_score(y_test, y_pred_lgb) * 100,\n",
    "    'F1 Score': f1_score(y_test, y_pred_lgb) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_test, y_pred_proba_lgb) * 100,\n",
    "    'PR AUC': average_precision_score(y_test, y_pred_proba_lgb) * 100\n",
    "}\n",
    "\n",
    "print(\"\\n---------- Training vs Test Performance (Overfitting Analysis) ----------\")\n",
    "print(f\"{'Metric':<12} {'Training':>12} {'Test':>12} {'Difference':>12}\")\n",
    "print(\"-\" * 50)\n",
    "for metric in train_metrics.keys():\n",
    "    diff = train_metrics[metric] - test_metrics[metric]\n",
    "    print(f\"{metric:<12} {train_metrics[metric]:>11.8f}% {test_metrics[metric]:>11.8f}% {diff:>11.8f}%\")\n",
    "\n",
    "# Calculate overall overfitting score (average difference across metrics)\n",
    "overfitting_score = np.mean([train_metrics[m] - test_metrics[m] for m in train_metrics.keys()])\n",
    "print(f\"\\nOverfitting Score: {overfitting_score:.8f}%\")\n",
    "if overfitting_score > 5:\n",
    "    print(\"WARNING: Model shows signs of overfitting (score > 5%)\")\n",
    "elif overfitting_score < 2:\n",
    "    print(\"Model shows good generalization (score < 2%)\")\n",
    "else:\n",
    "    print(\"Model shows moderate overfitting\")\n",
    "\n",
    "# Display individual metrics with arrows for visual emphasis\n",
    "print(\"\\n---------- Detailed Test Metrics ----------\")\n",
    "for metric, value in test_metrics.items():\n",
    "    print(f'➤➤➤ {metric}: {value:.8f} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5. Visualization\n",
    "plt.figure(figsize=(8, 6))\n",
    "cm = confusion_matrix(y_test, y_pred_lgb)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix - LightGBM')\n",
    "plt.ylabel('True Label')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class weight ratio (non-fraud:fraud): 769.83:1\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Model\n",
    "\n",
    "# 1. Performance Tracking\n",
    "start_time = time.time()\n",
    "\n",
    "# 2. Feature Scaling\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# 3. Calculate class weights\n",
    "fraud_samples = sum(y_train == 1)\n",
    "non_fraud_samples = sum(y_train == 0)\n",
    "class_weight_ratio = non_fraud_samples / fraud_samples\n",
    "print(f\"Class weight ratio (non-fraud:fraud): {class_weight_ratio:.2f}:1\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Random Forest model...\n",
      "Training completed in 41.33 seconds\n"
     ]
    }
   ],
   "source": [
    "# 4. Model Training\n",
    "print(\"Training Random Forest model...\")\n",
    "rf_model = RandomForestClassifier(\n",
    "    n_estimators=100,\n",
    "    max_depth=10,\n",
    "    min_samples_split=10,\n",
    "    min_samples_leaf=4,\n",
    "    max_features='sqrt',\n",
    "    bootstrap=True,\n",
    "    class_weight='balanced',\n",
    "    n_jobs=-1,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "rf_model.fit(X_train_scaled, y_train)\n",
    "training_time = time.time() - start_time\n",
    "print(f\"Training completed in {training_time:.2f} seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions completed in 7.91 seconds\n",
      "\n",
      "---------- Random Forest Model Evaluation ----------\n",
      "\n",
      "Classification Report (Test Set):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906351\n",
      "           1       1.00      1.00      1.00      2435\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       1.00      1.00      1.00   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n",
      "\n",
      "---------- Training vs Test Performance (Overfitting Analysis) ----------\n",
      "Metric           Training         Test   Difference\n",
      "--------------------------------------------------\n",
      "Accuracy     99.99964076% 99.99947611%  0.00016465%\n",
      "Precision    99.98265094% 100.00000000% -0.01734906%\n",
      "Recall       99.74039460% 99.58932238%  0.15107222%\n",
      "F1 Score     99.86137584% 99.79423868%  0.06713716%\n",
      "ROC AUC      99.99999384% 99.89835514%  0.10163870%\n",
      "PR AUC       99.99539746% 99.59533927%  0.40005819%\n",
      "\n",
      "Overfitting Score: 0.11712031%\n",
      "Model shows good generalization (score < 2%)\n",
      "\n",
      "---------- Detailed Test Metrics ----------\n",
      "➤➤➤ Accuracy: 99.99947611 %\n",
      "➤➤➤ Precision: 100.00000000 %\n",
      "➤➤➤ Recall: 99.58932238 %\n",
      "➤➤➤ F1 Score: 99.79423868 %\n",
      "➤➤➤ ROC AUC: 99.89835514 %\n",
      "➤➤➤ PR AUC: 99.59533927 %\n"
     ]
    }
   ],
   "source": [
    "# 5. Model Evaluation\n",
    "# Evaluate on training data\n",
    "y_train_pred = rf_model.predict(X_train_scaled)\n",
    "y_train_pred_proba = rf_model.predict_proba(X_train_scaled)[:, 1]\n",
    "\n",
    "# Evaluate on test data\n",
    "y_pred_rdf = rf_model.predict(X_test_scaled)\n",
    "y_pred_proba_rdf = rf_model.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "prediction_time = time.time() - start_time - training_time\n",
    "print(f\"Predictions completed in {prediction_time:.2f} seconds\")\n",
    "\n",
    "print(\"\\n---------- Random Forest Model Evaluation ----------\")\n",
    "print(\"\\nClassification Report (Test Set):\")\n",
    "print(classification_report(y_test, y_pred_rdf))\n",
    "\n",
    "# Calculate metrics for both training and test sets\n",
    "train_metrics = {\n",
    "    'Accuracy': accuracy_score(y_train, y_train_pred) * 100,\n",
    "    'Precision': precision_score(y_train, y_train_pred) * 100,\n",
    "    'Recall': recall_score(y_train, y_train_pred) * 100,\n",
    "    'F1 Score': f1_score(y_train, y_train_pred) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_train, y_train_pred_proba) * 100,\n",
    "    'PR AUC': average_precision_score(y_train, y_train_pred_proba) * 100\n",
    "}\n",
    "\n",
    "test_metrics = {\n",
    "    'Accuracy': accuracy_score(y_test, y_pred_rdf) * 100,\n",
    "    'Precision': precision_score(y_test, y_pred_rdf) * 100,\n",
    "    'Recall': recall_score(y_test, y_pred_rdf) * 100,\n",
    "    'F1 Score': f1_score(y_test, y_pred_rdf) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_test, y_pred_proba_rdf) * 100,\n",
    "    'PR AUC': average_precision_score(y_test, y_pred_proba_rdf) * 100\n",
    "}\n",
    "\n",
    "print(\"\\n---------- Training vs Test Performance (Overfitting Analysis) ----------\")\n",
    "print(f\"{'Metric':<12} {'Training':>12} {'Test':>12} {'Difference':>12}\")\n",
    "print(\"-\" * 50)\n",
    "for metric in train_metrics.keys():\n",
    "    diff = train_metrics[metric] - test_metrics[metric]\n",
    "    print(f\"{metric:<12} {train_metrics[metric]:>11.8f}% {test_metrics[metric]:>11.8f}% {diff:>11.8f}%\")\n",
    "\n",
    "# Calculate overall overfitting score (average difference across metrics)\n",
    "overfitting_score = np.mean([train_metrics[m] - test_metrics[m] for m in train_metrics.keys()])\n",
    "print(f\"\\nOverfitting Score: {overfitting_score:.8f}%\")\n",
    "if overfitting_score > 5:\n",
    "    print(\"WARNING: Model shows signs of overfitting (score > 5%)\")\n",
    "elif overfitting_score < 2:\n",
    "    print(\"Model shows good generalization (score < 2%)\")\n",
    "else:\n",
    "    print(\"Model shows moderate overfitting\")\n",
    "\n",
    "# Display individual metrics with arrows for visual emphasis\n",
    "print(\"\\n---------- Detailed Test Metrics ----------\")\n",
    "for metric, value in test_metrics.items():\n",
    "    print(f'➤➤➤ {metric}: {value:.8f} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6. Visualization\n",
    "plt.figure(figsize=(8, 6))\n",
    "cm = confusion_matrix(y_test, y_pred_rdf)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix - Random Forest')\n",
    "plt.ylabel('True Label')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data: 4448056 non-fraud, 5778 fraud\n",
      "Class distribution ratio (non-fraud:fraud): 769.83:1\n",
      "\n",
      "Selected weight approach: {0: 1.0, 1: 769.8262374524056}\n"
     ]
    }
   ],
   "source": [
    "# 1. Performance Tracking\n",
    "start_time = time.time()\n",
    "\n",
    "# 2. Feature Scaling\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# 3. Class Weight Calculation\n",
    "fraud_samples = sum(y_train == 1)\n",
    "non_fraud_samples = sum(y_train == 0)\n",
    "print(f\"Training data: {non_fraud_samples} non-fraud, {fraud_samples} fraud\")\n",
    "print(f\"Class distribution ratio (non-fraud:fraud): {non_fraud_samples / fraud_samples:.2f}:1\")\n",
    "\n",
    "# Define different class weight options\n",
    "weight_options = {\n",
    "    'balanced': 'balanced',\n",
    "    'inverse': {\n",
    "        0: 1.0,\n",
    "        1: non_fraud_samples / fraud_samples\n",
    "    },\n",
    "    'sqrt': {\n",
    "        0: 1.0,\n",
    "        1: np.sqrt(non_fraud_samples / fraud_samples)\n",
    "    },\n",
    "    'custom': {\n",
    "        0: 1.0,\n",
    "        1: 2 * (non_fraud_samples / fraud_samples)\n",
    "    }\n",
    "}\n",
    "\n",
    "# Select weight approach\n",
    "selected_weight = weight_options['inverse']  # Choose from: 'balanced', 'inverse', 'sqrt', 'custom'\n",
    "print(f\"\\nSelected weight approach: {selected_weight}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Logistic Regression model...\n",
      "[LibLinear]iter  1 act 2.465e+06 pre 2.175e+06 delta 8.073e-02 f 6.166e+06 |g| 6.218e+07 CG   1\n",
      "cg reaches trust region boundary\n",
      "iter  2 act 5.243e+05 pre 4.466e+05 delta 1.306e-01 f 3.701e+06 |g| 1.502e+07 CG   2\n",
      "cg reaches trust region boundary\n",
      "iter  3 act 3.805e+05 pre 3.436e+05 delta 3.194e-01 f 3.177e+06 |g| 5.504e+06 CG   2\n",
      "cg reaches trust region boundary\n",
      "iter  4 act 6.092e+05 pre 5.362e+05 delta 8.973e-01 f 2.796e+06 |g| 2.523e+06 CG   2\n",
      "cg reaches trust region boundary\n",
      "iter  5 act 1.097e+06 pre 1.049e+06 delta 1.725e+00 f 2.187e+06 |g| 1.653e+06 CG   1\n",
      "iter  6 act 6.422e+05 pre 5.220e+05 delta 1.725e+00 f 1.089e+06 |g| 8.539e+05 CG   1\n",
      "iter  7 act 2.027e+05 pre 1.608e+05 delta 1.725e+00 f 4.473e+05 |g| 2.943e+05 CG   3\n",
      "iter  8 act 7.715e+04 pre 6.031e+04 delta 1.725e+00 f 2.447e+05 |g| 1.103e+05 CG   6\n",
      "iter  9 act 3.525e+04 pre 2.711e+04 delta 2.008e+00 f 1.675e+05 |g| 4.095e+04 CG   5\n",
      "iter 10 act 1.954e+04 pre 1.476e+04 delta 2.780e+00 f 1.323e+05 |g| 1.667e+04 CG   5\n",
      "iter 11 act 1.190e+04 pre 9.134e+03 delta 3.819e+00 f 1.127e+05 |g| 8.263e+03 CG   7\n",
      "iter 12 act 6.131e+03 pre 4.821e+03 delta 4.906e+00 f 1.008e+05 |g| 4.381e+03 CG   8\n",
      "iter 13 act 2.282e+03 pre 1.825e+03 delta 4.906e+00 f 9.468e+04 |g| 2.729e+03 CG   8\n",
      "iter 14 act 7.609e+02 pre 6.087e+02 delta 4.906e+00 f 9.240e+04 |g| 1.783e+03 CG   8\n",
      "iter 15 act 2.354e+02 pre 1.919e+02 delta 4.906e+00 f 9.164e+04 |g| 9.061e+02 CG   8\n",
      "iter 16 act 5.220e+01 pre 4.451e+01 delta 4.906e+00 f 9.141e+04 |g| 3.416e+02 CG   8\n",
      "iter 17 act 6.070e+00 pre 5.454e+00 delta 4.906e+00 f 9.135e+04 |g| 1.115e+02 CG   8\n",
      "iter 18 act 2.260e-01 pre 2.036e-01 delta 4.906e+00 f 9.135e+04 |g| 2.738e+01 CG   7\n",
      "Training completed in 8.32 seconds\n"
     ]
    }
   ],
   "source": [
    "# 4. Model Training\n",
    "print(\"\\nTraining Logistic Regression model...\")\n",
    "lr_model = LogisticRegression(\n",
    "    penalty='l2',\n",
    "    C=1.0,\n",
    "    class_weight=selected_weight,\n",
    "    solver='liblinear',\n",
    "    max_iter=1000,\n",
    "    verbose=1,\n",
    "    n_jobs=-1,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "lr_model.fit(X_train_scaled, y_train)\n",
    "training_time = time.time() - start_time\n",
    "print(f\"Training completed in {training_time:.2f} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions completed in 7.43 seconds\n",
      "\n",
      "---------- Logistic Regression Model Evaluation ----------\n",
      "\n",
      "Classification Report (Test Set):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906351\n",
      "           1       0.99      1.00      0.99      2435\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       0.99      1.00      1.00   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n",
      "\n",
      "---------- Training vs Test Performance (Overfitting Analysis) ----------\n",
      "Metric           Training         Test   Difference\n",
      "--------------------------------------------------\n",
      "Accuracy     99.99833851% 99.99811398%  0.00022453%\n",
      "Precision    99.08777969% 98.93920849%  0.14857120%\n",
      "Recall       99.63655244% 99.58932238%  0.04723006%\n",
      "F1 Score     99.36140835% 99.26320098%  0.09820737%\n",
      "ROC AUC      99.95103785% 99.96167577% -0.01063792%\n",
      "PR AUC       99.65451640% 99.59721978%  0.05729662%\n",
      "\n",
      "Overfitting Score: 0.05681531%\n",
      "Model shows good generalization (score < 2%)\n",
      "\n",
      "---------- Detailed Test Metrics ----------\n",
      "➤➤➤ Accuracy: 99.99811398 %\n",
      "➤➤➤ Precision: 98.93920849 %\n",
      "➤➤➤ Recall: 99.58932238 %\n",
      "➤➤➤ F1 Score: 99.26320098 %\n",
      "➤➤➤ ROC AUC: 99.96167577 %\n",
      "➤➤➤ PR AUC: 99.59721978 %\n"
     ]
    }
   ],
   "source": [
    "# 5. Model Evaluation\n",
    "# Evaluate on training data\n",
    "y_train_pred = lr_model.predict(X_train_scaled)\n",
    "y_train_pred_proba = lr_model.predict_proba(X_train_scaled)[:, 1]\n",
    "\n",
    "# Evaluate on test data\n",
    "y_pred_lr = lr_model.predict(X_test_scaled)\n",
    "y_pred_proba_lr = lr_model.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "prediction_time = time.time() - start_time - training_time\n",
    "print(f\"Predictions completed in {prediction_time:.2f} seconds\")\n",
    "\n",
    "print(\"\\n---------- Logistic Regression Model Evaluation ----------\")\n",
    "print(\"\\nClassification Report (Test Set):\")\n",
    "print(classification_report(y_test, y_pred_lr))\n",
    "\n",
    "# Calculate metrics for both training and test sets\n",
    "train_metrics = {\n",
    "    'Accuracy': accuracy_score(y_train, y_train_pred) * 100,\n",
    "    'Precision': precision_score(y_train, y_train_pred) * 100,\n",
    "    'Recall': recall_score(y_train, y_train_pred) * 100,\n",
    "    'F1 Score': f1_score(y_train, y_train_pred) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_train, y_train_pred_proba) * 100,\n",
    "    'PR AUC': average_precision_score(y_train, y_train_pred_proba) * 100\n",
    "}\n",
    "\n",
    "test_metrics = {\n",
    "    'Accuracy': accuracy_score(y_test, y_pred_lr) * 100,\n",
    "    'Precision': precision_score(y_test, y_pred_lr) * 100,\n",
    "    'Recall': recall_score(y_test, y_pred_lr) * 100,\n",
    "    'F1 Score': f1_score(y_test, y_pred_lr) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_test, y_pred_proba_lr) * 100,\n",
    "    'PR AUC': average_precision_score(y_test, y_pred_proba_lr) * 100\n",
    "}\n",
    "\n",
    "print(\"\\n---------- Training vs Test Performance (Overfitting Analysis) ----------\")\n",
    "print(f\"{'Metric':<12} {'Training':>12} {'Test':>12} {'Difference':>12}\")\n",
    "print(\"-\" * 50)\n",
    "for metric in train_metrics.keys():\n",
    "    diff = train_metrics[metric] - test_metrics[metric]\n",
    "    print(f\"{metric:<12} {train_metrics[metric]:>11.8f}% {test_metrics[metric]:>11.8f}% {diff:>11.8f}%\")\n",
    "\n",
    "# Calculate overall overfitting score (average difference across metrics)\n",
    "overfitting_score = np.mean([train_metrics[m] - test_metrics[m] for m in train_metrics.keys()])\n",
    "print(f\"\\nOverfitting Score: {overfitting_score:.8f}%\")\n",
    "if overfitting_score > 5:\n",
    "    print(\"WARNING: Model shows signs of overfitting (score > 5%)\")\n",
    "elif overfitting_score < 2:\n",
    "    print(\"Model shows good generalization (score < 2%)\")\n",
    "else:\n",
    "    print(\"Model shows moderate overfitting\")\n",
    "\n",
    "# Display individual metrics with arrows for visual emphasis\n",
    "print(\"\\n---------- Detailed Test Metrics ----------\")\n",
    "for metric, value in test_metrics.items():\n",
    "    print(f'➤➤➤ {metric}: {value:.8f} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6. Visualization\n",
    "plt.figure(figsize=(8, 6))\n",
    "cm = confusion_matrix(y_test, y_pred_lr)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix - Logistic Regression')\n",
    "plt.ylabel('True Label')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data: 4448056 non-fraud, 5778 fraud\n",
      "Class distribution ratio (non-fraud:fraud): 769.83:1\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 1. Performance Tracking\n",
    "start_time = time.time()\n",
    "\n",
    "# 2. Feature Scaling\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# 3. Class Distribution Analysis\n",
    "fraud_samples = sum(y_train == 1)\n",
    "non_fraud_samples = sum(y_train == 0)\n",
    "print(f\"Training data: {non_fraud_samples} non-fraud, {fraud_samples} fraud\")\n",
    "print(f\"Class distribution ratio (non-fraud:fraud): {non_fraud_samples / fraud_samples:.2f}:1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training k-Nearest Neighbors model...\n",
      "Training completed in 5.08 seconds\n"
     ]
    }
   ],
   "source": [
    "# 4. Model Training\n",
    "print(\"\\nTraining k-Nearest Neighbors model...\")\n",
    "knn_model = KNeighborsClassifier(\n",
    "    n_neighbors=5,          # Number of neighbors to consider\n",
    "    weights='distance',     # Weight points by inverse of their distance\n",
    "    algorithm='auto',       # Automatically choose the best algorithm\n",
    "    leaf_size=30,          # Leaf size passed to BallTree or KDTree\n",
    "    p=2,                   # Power parameter for the Minkowski metric (2 = Euclidean distance)\n",
    "    metric='minkowski',    # Distance metric to use\n",
    "    n_jobs=-1             # Use all available cores\n",
    ")\n",
    "\n",
    "knn_model.fit(X_train_scaled, y_train)\n",
    "training_time = time.time() - start_time\n",
    "print(f\"Training completed in {training_time:.2f} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions completed in 9772.89 seconds\n",
      "\n",
      "---------- k-Nearest Neighbors Model Evaluation ----------\n",
      "\n",
      "Classification Report (Test Set):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906351\n",
      "           1       1.00      1.00      1.00      2435\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       1.00      1.00      1.00   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n",
      "\n",
      "---------- Training vs Test Performance (Overfitting Analysis) ----------\n",
      "Metric           Training         Test   Difference\n",
      "--------------------------------------------------\n",
      "Accuracy     99.99995509% 99.99910938%  0.00084571%\n",
      "Precision    100.00000000% 99.71217105%  0.28782895%\n",
      "Recall       99.96538595% 99.58932238%  0.37606356%\n",
      "F1 Score     99.98268998% 99.65070886%  0.33198112%\n",
      "ROC AUC      99.99999999% 99.79457681%  0.20542318%\n",
      "\n",
      "Overfitting Score: 0.24042851%\n",
      "Model shows good generalization (score < 2%)\n",
      "\n",
      "---------- Detailed Test Metrics ----------\n",
      "➤➤➤ Accuracy: 99.99910938 %\n",
      "➤➤➤ Precision: 99.71217105 %\n",
      "➤➤➤ Recall: 99.58932238 %\n",
      "➤➤➤ F1 Score: 99.65070886 %\n",
      "➤➤➤ ROC AUC: 99.79457681 %\n"
     ]
    }
   ],
   "source": [
    "# 5. Model Evaluation\n",
    "# Evaluate on training data\n",
    "y_train_pred = knn_model.predict(X_train_scaled)\n",
    "y_train_pred_proba = knn_model.predict_proba(X_train_scaled)[:, 1]\n",
    "\n",
    "# Evaluate on test data\n",
    "y_pred_knn = knn_model.predict(X_test_scaled)\n",
    "y_pred_proba_knn = knn_model.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "prediction_time = time.time() - start_time - training_time\n",
    "print(f\"Predictions completed in {prediction_time:.2f} seconds\")\n",
    "\n",
    "print(\"\\n---------- k-Nearest Neighbors Model Evaluation ----------\")\n",
    "print(\"\\nClassification Report (Test Set):\")\n",
    "print(classification_report(y_test, y_pred_knn))\n",
    "\n",
    "# Calculate metrics for both training and test sets\n",
    "train_metrics = {\n",
    "    'Accuracy': accuracy_score(y_train, y_train_pred) * 100,\n",
    "    'Precision': precision_score(y_train, y_train_pred) * 100,\n",
    "    'Recall': recall_score(y_train, y_train_pred) * 100,\n",
    "    'F1 Score': f1_score(y_train, y_train_pred) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_train, y_train_pred_proba) * 100\n",
    "}\n",
    "\n",
    "test_metrics = {\n",
    "    'Accuracy': accuracy_score(y_test, y_pred_knn) * 100,\n",
    "    'Precision': precision_score(y_test, y_pred_knn) * 100,\n",
    "    'Recall': recall_score(y_test, y_pred_knn) * 100,\n",
    "    'F1 Score': f1_score(y_test, y_pred_knn) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_test, y_pred_proba_knn) * 100\n",
    "}\n",
    "\n",
    "print(\"\\n---------- Training vs Test Performance (Overfitting Analysis) ----------\")\n",
    "print(f\"{'Metric':<12} {'Training':>12} {'Test':>12} {'Difference':>12}\")\n",
    "print(\"-\" * 50)\n",
    "for metric in train_metrics.keys():\n",
    "    diff = train_metrics[metric] - test_metrics[metric]\n",
    "    print(f\"{metric:<12} {train_metrics[metric]:>11.8f}% {test_metrics[metric]:>11.8f}% {diff:>11.8f}%\")\n",
    "\n",
    "# Calculate overall overfitting score (average difference across metrics)\n",
    "overfitting_score = np.mean([train_metrics[m] - test_metrics[m] for m in train_metrics.keys()])\n",
    "print(f\"\\nOverfitting Score: {overfitting_score:.8f}%\")\n",
    "if overfitting_score > 5:\n",
    "    print(\"WARNING: Model shows signs of overfitting (score > 5%)\")\n",
    "elif overfitting_score < 2:\n",
    "    print(\"Model shows good generalization (score < 2%)\")\n",
    "else:\n",
    "    print(\"Model shows moderate overfitting\")\n",
    "\n",
    "# Display individual metrics with arrows for visual emphasis\n",
    "print(\"\\n---------- Detailed Test Metrics ----------\")\n",
    "for metric, value in test_metrics.items():\n",
    "    print(f'➤➤➤ {metric}: {value:.8f} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6. Visualization\n",
    "plt.figure(figsize=(8, 6))\n",
    "cm = confusion_matrix(y_test, y_pred_knn)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix - k-Nearest Neighbors')\n",
    "plt.ylabel('True Label')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------- Decision Tree Model Performance ----------\n",
      "\n",
      "---------- Training vs Test Performance (Overfitting Analysis) ----------\n",
      "Metric                   Training                 Test           Difference\n",
      "--------------------------------------------------------------------------\n",
      "Accuracy             99.99398271%         99.99329417%          0.00068855%\n",
      "Precision            95.68822554%         95.35981125%          0.32841429%\n",
      "Recall               99.86154379%         99.58932238%          0.27222140%\n",
      "F1 Score             97.73035230%         97.42868622%          0.30166608%\n",
      "ROC AUC              99.99867954%         99.81102432%          0.18765522%\n",
      "PR AUC               99.87078692%         99.59165051%          0.27913640%\n",
      "\n",
      "Overfitting Score: 0.22829699%\n",
      "Model shows good generalization (score < 2%)\n",
      "\n",
      "---------- Detailed Test Metrics ----------\n",
      "➤➤➤ Accuracy: 99.99329417 %\n",
      "➤➤➤ Precision: 95.35981125 %\n",
      "➤➤➤ Recall: 99.58932238 %\n",
      "➤➤➤ F1 Score: 97.42868622 %\n",
      "➤➤➤ ROC AUC: 99.81102432 %\n",
      "➤➤➤ PR AUC: 99.59165051 %\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906351\n",
      "           1       0.95      1.00      0.97      2435\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       0.98      1.00      0.99   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Scale the features (optional for Decision Trees but keeping for consistency)\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Calculate class weights for imbalanced data\n",
    "fraud_ratio = len(y_train[y_train==1]) / len(y_train[y_train==0])\n",
    "class_weights = {0: 1, 1: 1/fraud_ratio}\n",
    "\n",
    "# Initialize Decision Tree model with parameters\n",
    "dt_model = DecisionTreeClassifier(\n",
    "    class_weight=class_weights,  # Handle class imbalance\n",
    "    max_depth=10,                # Control tree depth to prevent overfitting\n",
    "    min_samples_split=50,        # Minimum samples required to split a node\n",
    "    min_samples_leaf=20,         # Minimum samples required at a leaf node\n",
    "    criterion='gini',            # Use gini impurity\n",
    "    random_state=42              # For reproducibility\n",
    ")\n",
    "\n",
    "# Train the model\n",
    "dt_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Evaluate on training data\n",
    "train_preds = dt_model.predict(X_train_scaled)\n",
    "train_probs = dt_model.predict_proba(X_train_scaled)[:, 1]\n",
    "\n",
    "# Evaluate on test data\n",
    "test_preds = dt_model.predict(X_test_scaled)\n",
    "test_probs = dt_model.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "# Calculate metrics for both sets (as percentages)\n",
    "train_metrics = {\n",
    "    'Accuracy': accuracy_score(y_train, train_preds) * 100,\n",
    "    'Precision': precision_score(y_train, train_preds) * 100,\n",
    "    'Recall': recall_score(y_train, train_preds) * 100,\n",
    "    'F1 Score': f1_score(y_train, train_preds) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_train, train_probs) * 100,\n",
    "    'PR AUC': average_precision_score(y_train, train_probs) * 100\n",
    "}\n",
    "\n",
    "test_metrics = {\n",
    "    'Accuracy': accuracy_score(y_test, test_preds) * 100,\n",
    "    'Precision': precision_score(y_test, test_preds) * 100,\n",
    "    'Recall': recall_score(y_test, test_preds) * 100,\n",
    "    'F1 Score': f1_score(y_test, test_preds) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_test, test_probs) * 100,\n",
    "    'PR AUC': average_precision_score(y_test, test_probs) * 100\n",
    "}\n",
    "\n",
    "# Format metrics with 8 decimal places\n",
    "print(\"\\n---------- Decision Tree Model Performance ----------\")\n",
    "print(\"\\n---------- Training vs Test Performance (Overfitting Analysis) ----------\")\n",
    "print(f\"{'Metric':<12} {'Training':>20} {'Test':>20} {'Difference':>20}\")\n",
    "print(\"-\" * 74)\n",
    "for metric in train_metrics.keys():\n",
    "    diff = train_metrics[metric] - test_metrics[metric]\n",
    "    print(f\"{metric:<12} {train_metrics[metric]:>19.8f}% {test_metrics[metric]:>19.8f}% {diff:>19.8f}%\")\n",
    "\n",
    "# Calculate overall overfitting score (average difference across metrics)\n",
    "overfitting_score = np.mean([train_metrics[m] - test_metrics[m] for m in train_metrics.keys()])\n",
    "print(f\"\\nOverfitting Score: {overfitting_score:.8f}%\")\n",
    "if overfitting_score > 5:\n",
    "    print(\"WARNING: Model shows signs of overfitting (score > 5%)\")\n",
    "elif overfitting_score < 2:\n",
    "    print(\"Model shows good generalization (score < 2%)\")\n",
    "else:\n",
    "    print(\"Model shows moderate overfitting\")\n",
    "\n",
    "# Display individual metrics with arrows for visual emphasis and 8 decimal places\n",
    "print(\"\\n---------- Detailed Test Metrics ----------\")\n",
    "for metric, value in test_metrics.items():\n",
    "    print(f'➤➤➤ {metric}: {value:.8f} %')\n",
    "\n",
    "# Print classification report\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, test_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion matrix\n",
    "cm = confusion_matrix(y_test, test_preds)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix - Decision Tree')\n",
    "plt.ylabel('True Label')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Improved Outlier Handling\n",
    "def handle_outliers(df, method='winsorize', z_threshold=4, columns_to_handle=None):\n",
    "    \"\"\"\n",
    "    Enhanced outlier handling with column-specific treatment\n",
    "    \n",
    "    Parameters:\n",
    "    -----------\n",
    "    df : DataFrame\n",
    "        The input dataframe\n",
    "    method : str\n",
    "        The method to handle outliers ('remove', 'winsorize', 'log', or 'robust_scale')\n",
    "    z_threshold : float\n",
    "        Threshold for Z-score outlier detection\n",
    "    columns_to_handle : list\n",
    "        Specific columns to apply outlier handling to (if None, applies to all numeric columns)\n",
    "    \"\"\"\n",
    "    df_result = df.copy()\n",
    "    \n",
    "    # Select columns to process\n",
    "    cols_to_process = columns_to_handle if columns_to_handle else df.select_dtypes(include=np.number).columns\n",
    "    \n",
    "    if method == 'remove':\n",
    "        # Only calculate Z-scores for specified columns\n",
    "        z_scores = np.abs(stats.zscore(df[cols_to_process]))\n",
    "        outliers_z = (z_scores > z_threshold).any(axis=1)\n",
    "        df_result = df[~outliers_z]\n",
    "        print(f\"\\nRemoved {len(df) - len(df_result)} rows using Z-score method\")\n",
    "    \n",
    "    elif method == 'winsorize':\n",
    "        # Winsorization per column (capping extreme values)\n",
    "        for col in cols_to_process:\n",
    "            lower = df[col].quantile(0.01)  # Less aggressive lower bound\n",
    "            upper = df[col].quantile(0.99)  # Less aggressive upper bound\n",
    "            df_result[col] = np.clip(df[col], lower, upper)\n",
    "        print(f\"\\nWinsorized {len(cols_to_process)} columns at 1% and 99% percentiles\")\n",
    "    \n",
    "    elif method == 'log':\n",
    "        # Log transformation for positive-valued columns\n",
    "        for col in cols_to_process:\n",
    "            if df[col].min() >= 0:  # Only apply to non-negative columns\n",
    "                df_result[col] = np.log1p(df[col])\n",
    "        print(f\"\\nApplied log transformation to applicable columns\")\n",
    "    \n",
    "    elif method == 'robust_scale':\n",
    "        # Robust scaling (less sensitive to outliers)\n",
    "        from sklearn.preprocessing import RobustScaler\n",
    "        scaler = RobustScaler()\n",
    "        df_result[cols_to_process] = scaler.fit_transform(df[cols_to_process])\n",
    "        print(f\"\\nApplied robust scaling to {len(cols_to_process)} columns\")\n",
    "    \n",
    "    else:\n",
    "        raise ValueError(\"Method must be 'remove', 'winsorize', 'log', or 'robust_scale'\")\n",
    "        \n",
    "    return df_result\n",
    "\n",
    "# Feature-specific outlier handling\n",
    "def handle_outliers_by_feature(df):\n",
    "    \"\"\"\n",
    "    Apply different treatment to different columns based on their characteristics\n",
    "    \"\"\"\n",
    "    df_result = df.copy()\n",
    "    \n",
    "    # Amount-related columns (apply winsorization)\n",
    "    amount_cols = ['oldbalanceOrg', 'newbalanceDest']\n",
    "    for col in amount_cols:\n",
    "        lower = df[col].quantile(0.005)\n",
    "        upper = df[col].quantile(0.995)\n",
    "        df_result[col] = np.clip(df[col], lower, upper)\n",
    "    \n",
    "    # Add outlier flags as features (instead of removing them)\n",
    "    for col in amount_cols:\n",
    "        z_scores = np.abs(stats.zscore(df[col]))\n",
    "        df_result[f'{col}_is_outlier'] = (z_scores > 3).astype(int)\n",
    "    \n",
    "    return df_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape is :  (6362620, 7)\n",
      "\n",
      "y shape is :  (6362620,)\n",
      "Preprocessing complete: Added 2 outlier indicator features\n",
      "Original shape: (6362620, 7), New shape: (6362620, 9)\n",
      "X_train shape is  (4453834, 9)\n",
      "X_test shape is  (1908786, 9)\n",
      "y_train shape is  (4453834,)\n",
      "y_test shape is  (1908786,)\n"
     ]
    }
   ],
   "source": [
    "# X Data\n",
    "X = df.drop(['isFraud'], axis=1)\n",
    "print('X shape is : ', X.shape)\n",
    "print()\n",
    "y = df['isFraud']\n",
    "print('y shape is : ', y.shape)\n",
    "\n",
    "# Implement improved preprocessing pipeline\n",
    "def preprocess_data(X, y):\n",
    "    \"\"\"\n",
    "    Improved preprocessing pipeline for fraud detection that preserves\n",
    "    outlier information while limiting extreme values\n",
    "    \"\"\"\n",
    "    # Create a copy to avoid modifying originals\n",
    "    X_processed = X.copy()\n",
    "    \n",
    "    # Add outlier indicator features for important columns\n",
    "    numeric_cols = X.select_dtypes(include=np.number).columns\n",
    "    \n",
    "    # Add outlier indicators for financial columns\n",
    "    financial_cols = ['oldbalanceOrg', 'newbalanceDest']\n",
    "    for col in financial_cols:\n",
    "        if col in X.columns:\n",
    "            z_scores = np.abs(stats.zscore(X[col]))\n",
    "            X_processed[f'{col}_outlier'] = (z_scores > 3).astype(int)\n",
    "    \n",
    "    # Winsorize only the extreme outliers in amount columns (99.5% percentile)\n",
    "    for col in financial_cols:\n",
    "        if col in X.columns:\n",
    "            lower = X[col].quantile(0.005)\n",
    "            upper = X[col].quantile(0.995)\n",
    "            X_processed[col] = np.clip(X[col], lower, upper)\n",
    "    \n",
    "    print(f\"Preprocessing complete: Added {len(financial_cols)} outlier indicator features\")\n",
    "    print(f\"Original shape: {X.shape}, New shape: {X_processed.shape}\")\n",
    "    \n",
    "    return X_processed, y\n",
    "\n",
    "# Apply the improved preprocessing\n",
    "X_processed, y_processed = preprocess_data(X, y)\n",
    "\n",
    "# Split the processed data\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_processed, y_processed, test_size=0.3, random_state=42, shuffle=True\n",
    ")\n",
    "\n",
    "# Splitted Data\n",
    "print('X_train shape is ', X_train.shape)\n",
    "print('X_test shape is ', X_test.shape)\n",
    "print('y_train shape is ', y_train.shape)\n",
    "print('y_test shape is ', y_test.shape)\n",
    "# print(len(y_train[y_train==0]) / len(y_train[y_train==1]))\n",
    "\n",
    "# class_weights = {0: 1, 1: 50}\n",
    "class_weights = {0: 1, 1: len(y_train[y_train==0]) / len(y_train[y_train==1])}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training CatBoost model...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b349caa7eafb49ee9ec2f7aa004939d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\ttest: 0.9988206\tbest: 0.9988206 (0)\ttotal: 345ms\tremaining: 5m 44s\n",
      "Stopped by overfitting detector  (50 iterations wait)\n",
      "\n",
      "bestTest = 0.9995611658\n",
      "bestIteration = 12\n",
      "\n",
      "Shrink model to first 13 iterations.\n",
      "Training completed in 12.54 seconds\n"
     ]
    }
   ],
   "source": [
    "# 4. Model Training\n",
    "print(\"\\nTraining CatBoost model...\")\n",
    "start_time = time.time()  # Start timer (add this line if you haven't defined start_time)\n",
    "\n",
    "catboost_model = CatBoostClassifier(\n",
    "    iterations=1000,           # Number of trees\n",
    "    learning_rate=0.01,       # Learning rate\n",
    "    depth=6,                  # Depth of trees\n",
    "    l2_leaf_reg=3,           # L2 regularization coefficient\n",
    "    loss_function='Logloss',  # Loss function\n",
    "    eval_metric='AUC',        # Evaluation metric\n",
    "    random_seed=42,           # Random seed for reproducibility\n",
    "    class_weights=class_weights,  # Class weights for imbalanced data\n",
    "    verbose=100,              # Print progress every 100 iterations\n",
    "    early_stopping_rounds=50, # Early stopping rounds\n",
    "    use_best_model=True,     # Use best model found during training\n",
    "    task_type='CPU'          # Can change to 'GPU' if available\n",
    ")\n",
    "\n",
    "# Create eval set for early stopping\n",
    "eval_set = [(X_test, y_test)]\n",
    "\n",
    "# Train the model\n",
    "catboost_model.fit(\n",
    "    X_train, \n",
    "    y_train,\n",
    "    eval_set=eval_set,\n",
    "    plot=True  # Plot training progress\n",
    ")\n",
    "\n",
    "training_time = time.time() - start_time\n",
    "print(f\"Training completed in {training_time:.2f} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions completed in 19.61 seconds\n"
     ]
    }
   ],
   "source": [
    "# 5. Model Evaluation\n",
    "# Evaluate on training data\n",
    "y_train_pred = catboost_model.predict(X_train)\n",
    "y_train_pred_proba = catboost_model.predict_proba(X_train)[:, 1]\n",
    "\n",
    "# Evaluate on test data\n",
    "y_pred_cat = catboost_model.predict(X_test)\n",
    "y_pred_proba_cat = catboost_model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "prediction_time = time.time() - start_time - training_time\n",
    "print(f\"Predictions completed in {prediction_time:.2f} seconds\")\n",
    "\n",
    "# Calculate metrics for both training and test sets\n",
    "train_metrics = {\n",
    "    'Accuracy': accuracy_score(y_train, y_train_pred) * 100,\n",
    "    'Precision': precision_score(y_train, y_train_pred) * 100,\n",
    "    'Recall': recall_score(y_train, y_train_pred) * 100,\n",
    "    'F1 Score': f1_score(y_train, y_train_pred) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_train, y_train_pred_proba) * 100,\n",
    "    'PR AUC': average_precision_score(y_train, y_train_pred_proba) * 100\n",
    "}\n",
    "\n",
    "test_metrics = {\n",
    "    'Accuracy': accuracy_score(y_test, y_pred_cat) * 100,\n",
    "    'Precision': precision_score(y_test, y_pred_cat) * 100,\n",
    "    'Recall': recall_score(y_test, y_pred_cat) * 100,\n",
    "    'F1 Score': f1_score(y_test, y_pred_cat) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_test, y_pred_proba_cat) * 100,\n",
    "    'PR AUC': average_precision_score(y_test, y_pred_proba_cat) * 100\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------- CatBoost Model Evaluation ----------\n",
      "\n",
      "Classification Report (Test Set):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906351\n",
      "           1       1.00      1.00      1.00      2435\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       1.00      1.00      1.00   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n",
      "\n",
      "---------- Training vs Test Performance (Overfitting Analysis) ----------\n",
      "Metric           Training         Test   Difference\n",
      "--------------------------------------------------\n",
      "Accuracy     99.99952850% 99.99947611%  0.00005239%\n",
      "Precision    100.00000000% 100.00000000%  0.00000000%\n",
      "Recall       99.63655244% 99.58932238%  0.04723006%\n",
      "F1 Score     99.81794538% 99.79423868%  0.02370670%\n",
      "ROC AUC      99.96100614% 99.95611658%  0.00488957%\n",
      "PR AUC       99.65746450% 99.59523736%  0.06222714%\n",
      "\n",
      "Overfitting Score: 0.02301764%\n",
      "Model shows good generalization (score < 2%)\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n---------- CatBoost Model Evaluation ----------\")\n",
    "print(\"\\nClassification Report (Test Set):\")\n",
    "print(classification_report(y_test, y_pred_cat))\n",
    "\n",
    "print(\"\\n---------- Training vs Test Performance (Overfitting Analysis) ----------\")\n",
    "print(f\"{'Metric':<12} {'Training':>12} {'Test':>12} {'Difference':>12}\")\n",
    "print(\"-\" * 50)\n",
    "for metric in train_metrics.keys():\n",
    "    diff = train_metrics[metric] - test_metrics[metric]\n",
    "    print(f\"{metric:<12} {train_metrics[metric]:>11.8f}% {test_metrics[metric]:>11.8f}% {diff:>11.8f}%\")\n",
    "\n",
    "# Calculate overall overfitting score (average difference across metrics)\n",
    "overfitting_score = np.mean([train_metrics[m] - test_metrics[m] for m in train_metrics.keys()])\n",
    "print(f\"\\nOverfitting Score: {overfitting_score:.8f}%\")\n",
    "if overfitting_score > 5:\n",
    "    print(\"WARNING: Model shows signs of overfitting (score > 5%)\")\n",
    "elif overfitting_score < 2:\n",
    "    print(\"Model shows good generalization (score < 2%)\")\n",
    "else:\n",
    "    print(\"Model shows moderate overfitting\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6. Visualization\n",
    "\n",
    "# Confusion Matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "cm = confusion_matrix(y_test, y_pred_cat)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix - CatBoost')\n",
    "plt.ylabel('True Label')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data: 4448056 non-fraud, 5778 fraud\n",
      "Class distribution ratio (non-fraud:fraud): 769.83:1\n",
      "\n",
      "Training k-Nearest Neighbors model...\n",
      "Training completed in 3.10 seconds\n"
     ]
    }
   ],
   "source": [
    "# 4. Performance Tracking\n",
    "start_time = time.time()\n",
    "\n",
    "# 5. Feature Scaling - KNN is distance-based so scaling is essential\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# 6. Class Distribution Analysis\n",
    "fraud_samples = sum(y_train == 1)\n",
    "non_fraud_samples = sum(y_train == 0)\n",
    "print(f\"Training data: {non_fraud_samples} non-fraud, {fraud_samples} fraud\")\n",
    "print(f\"Class distribution ratio (non-fraud:fraud): {non_fraud_samples / fraud_samples:.2f}:1\")\n",
    "\n",
    "# 7. KNN Model Training with Optimizations for Imbalanced Data\n",
    "print(\"\\nTraining k-Nearest Neighbors model...\")\n",
    "knn_outlier_model = KNeighborsClassifier(\n",
    "    n_neighbors=5,          # Start with a small number of neighbors as fraud cases are rare\n",
    "    weights='distance',     # Weight points by inverse of their distance - better for imbalanced data\n",
    "    algorithm='auto',       # Automatically choose the best algorithm\n",
    "    leaf_size=30,           # Leaf size passed to BallTree or KDTree\n",
    "    p=2,                    # Power parameter for the Minkowski metric (2 = Euclidean distance)\n",
    "    metric='minkowski',     # Distance metric to use\n",
    "    n_jobs=-1               # Use all available cores\n",
    ")\n",
    "\n",
    "knn_outlier_model.fit(X_train_scaled, y_train)\n",
    "training_time = time.time() - start_time\n",
    "print(f\"Training completed in {training_time:.2f} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions completed in 15257.49 seconds\n",
      "\n",
      "---------- k-Nearest Neighbors (with Outlier Detection) Model Evaluation ----------\n",
      "\n",
      "Classification Report (Test Set):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906351\n",
      "           1       1.00      1.00      1.00      2435\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       1.00      1.00      1.00   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 8. Model Evaluation\n",
    "# Evaluate on training data\n",
    "y_train_pred = knn_outlier_model.predict(X_train_scaled)\n",
    "y_train_pred_proba = knn_outlier_model.predict_proba(X_train_scaled)[:, 1]\n",
    "\n",
    "# Evaluate on test data\n",
    "y_pred_knn = knn_outlier_model.predict(X_test_scaled)\n",
    "y_pred_proba_knn = knn_outlier_model.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "prediction_time = time.time() - start_time - training_time\n",
    "print(f\"Predictions completed in {prediction_time:.2f} seconds\")\n",
    "\n",
    "print(\"\\n---------- k-Nearest Neighbors (with Outlier Detection) Model Evaluation ----------\")\n",
    "print(\"\\nClassification Report (Test Set):\")\n",
    "print(classification_report(y_test, y_pred_knn))\n",
    "\n",
    "# Calculate metrics for both training and test sets\n",
    "train_metrics = {\n",
    "    'Accuracy': accuracy_score(y_train, y_train_pred) * 100,\n",
    "    'Precision': precision_score(y_train, y_train_pred) * 100,\n",
    "    'Recall': recall_score(y_train, y_train_pred) * 100,\n",
    "    'F1 Score': f1_score(y_train, y_train_pred) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_train, y_train_pred_proba) * 100\n",
    "}\n",
    "\n",
    "test_metrics = {\n",
    "    'Accuracy': accuracy_score(y_test, y_pred_knn) * 100,\n",
    "    'Precision': precision_score(y_test, y_pred_knn) * 100,\n",
    "    'Recall': recall_score(y_test, y_pred_knn) * 100,\n",
    "    'F1 Score': f1_score(y_test, y_pred_knn) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_test, y_pred_proba_knn) * 100\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------- Training vs Test Performance (Overfitting Analysis) ----------\n",
      "Metric           Training         Test   Difference\n",
      "--------------------------------------------------\n",
      "Accuracy     99.99995509% 99.99916177%  0.00079332%\n",
      "Precision    100.00000000% 99.75318799%  0.24681201%\n",
      "Recall       99.96538595% 99.58932238%  0.37606356%\n",
      "F1 Score     99.98268998% 99.67118783%  0.31150214%\n",
      "ROC AUC      99.99999999% 99.79462915%  0.20537085%\n",
      "\n",
      "Overfitting Score: 0.22810838%\n",
      "Model shows good generalization (score < 2%)\n",
      "\n",
      "---------- Detailed Test Metrics ----------\n",
      "➤➤➤ Accuracy: 99.99916177 %\n",
      "➤➤➤ Precision: 99.75318799 %\n",
      "➤➤➤ Recall: 99.58932238 %\n",
      "➤➤➤ F1 Score: 99.67118783 %\n",
      "➤➤➤ ROC AUC: 99.79462915 %\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n---------- Training vs Test Performance (Overfitting Analysis) ----------\")\n",
    "print(f\"{'Metric':<12} {'Training':>12} {'Test':>12} {'Difference':>12}\")\n",
    "print(\"-\" * 50)\n",
    "for metric in train_metrics.keys():\n",
    "    diff = train_metrics[metric] - test_metrics[metric]\n",
    "    print(f\"{metric:<12} {train_metrics[metric]:>11.8f}% {test_metrics[metric]:>11.8f}% {diff:>11.8f}%\")\n",
    "\n",
    "# Calculate overall overfitting score (average difference across metrics)\n",
    "overfitting_score = np.mean([train_metrics[m] - test_metrics[m] for m in train_metrics.keys()])\n",
    "print(f\"\\nOverfitting Score: {overfitting_score:.8f}%\")\n",
    "if overfitting_score > 5:\n",
    "    print(\"WARNING: Model shows signs of overfitting (score > 5%)\")\n",
    "elif overfitting_score < 2:\n",
    "    print(\"Model shows good generalization (score < 2%)\")\n",
    "else:\n",
    "    print(\"Model shows moderate overfitting\")\n",
    "\n",
    "# Display individual metrics with arrows for visual emphasis\n",
    "print(\"\\n---------- Detailed Test Metrics ----------\")\n",
    "for metric, value in test_metrics.items():\n",
    "    print(f'➤➤➤ {metric}: {value:.8f} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6. Visualization\n",
    "# Confusion Matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "cm = confusion_matrix(y_test, y_pred_knn)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix - KNN (Do outlier handling)')\n",
    "plt.ylabel('True Label')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data: 4448056 non-fraud, 5778 fraud\n",
      "Class distribution ratio (non-fraud:fraud): 769.83:1\n",
      "\n",
      "Selected weight approach: {0: 1.0, 1: 769.8262374524056}\n",
      "\n",
      "Training Logistic Regression model...\n",
      "[LibLinear]iter  1 act 2.465e+06 pre 2.175e+06 delta 8.073e-02 f 6.166e+06 |g| 6.218e+07 CG   1\n",
      "cg reaches trust region boundary\n",
      "iter  2 act 5.250e+05 pre 4.472e+05 delta 1.307e-01 f 3.701e+06 |g| 1.502e+07 CG   2\n",
      "cg reaches trust region boundary\n",
      "iter  3 act 3.816e+05 pre 3.446e+05 delta 3.195e-01 f 3.176e+06 |g| 5.506e+06 CG   2\n",
      "cg reaches trust region boundary\n",
      "iter  4 act 6.105e+05 pre 5.368e+05 delta 8.932e-01 f 2.794e+06 |g| 2.527e+06 CG   2\n",
      "cg reaches trust region boundary\n",
      "iter  5 act 1.096e+06 pre 1.048e+06 delta 1.723e+00 f 2.184e+06 |g| 1.656e+06 CG   1\n",
      "iter  6 act 6.523e+05 pre 5.291e+05 delta 1.723e+00 f 1.088e+06 |g| 8.602e+05 CG   2\n",
      "iter  7 act 2.073e+05 pre 1.638e+05 delta 1.723e+00 f 4.360e+05 |g| 2.912e+05 CG   4\n",
      "iter  8 act 7.953e+04 pre 6.246e+04 delta 1.723e+00 f 2.288e+05 |g| 1.069e+05 CG   5\n",
      "iter  9 act 3.269e+04 pre 2.553e+04 delta 1.863e+00 f 1.492e+05 |g| 4.004e+04 CG   5\n",
      "iter 10 act 1.455e+04 pre 1.131e+04 delta 2.195e+00 f 1.165e+05 |g| 1.521e+04 CG   5\n",
      "iter 11 act 7.106e+03 pre 5.598e+03 delta 2.713e+00 f 1.020e+05 |g| 5.992e+03 CG   7\n",
      "iter 12 act 2.529e+03 pre 2.031e+03 delta 2.713e+00 f 9.489e+04 |g| 2.575e+03 CG   7\n",
      "iter 13 act 8.527e+02 pre 6.763e+02 delta 2.713e+00 f 9.236e+04 |g| 1.591e+03 CG   6\n",
      "iter 14 act 3.126e+02 pre 2.470e+02 delta 2.713e+00 f 9.151e+04 |g| 7.918e+02 CG   7\n",
      "iter 15 act 1.280e+02 pre 9.909e+01 delta 2.713e+00 f 9.120e+04 |g| 3.016e+02 CG   8\n",
      "iter 16 act 5.906e+01 pre 4.808e+01 delta 2.713e+00 f 9.107e+04 |g| 1.086e+02 CG   8\n",
      "iter 17 act 8.855e+00 pre 8.263e+00 delta 2.713e+00 f 9.101e+04 |g| 4.133e+01 CG   7\n",
      "iter 18 act 1.635e-01 pre 1.572e-01 delta 2.713e+00 f 9.100e+04 |g| 1.201e+01 CG   8\n",
      "Training completed in 7.70 seconds\n"
     ]
    }
   ],
   "source": [
    "# 1. Performance Tracking\n",
    "start_time = time.time()\n",
    "\n",
    "# 2. Feature Scaling - very important for Logistic Regression\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# 3. Class Weight Calculation\n",
    "fraud_samples = sum(y_train == 1)\n",
    "non_fraud_samples = sum(y_train == 0)\n",
    "print(f\"Training data: {non_fraud_samples} non-fraud, {fraud_samples} fraud\")\n",
    "print(f\"Class distribution ratio (non-fraud:fraud): {non_fraud_samples / fraud_samples:.2f}:1\")\n",
    "\n",
    "# Define different class weight options\n",
    "weight_options = {\n",
    "    'balanced': 'balanced',\n",
    "    'inverse': {\n",
    "        0: 1.0,\n",
    "        1: non_fraud_samples / fraud_samples\n",
    "    },\n",
    "    'sqrt': {\n",
    "        0: 1.0,\n",
    "        1: np.sqrt(non_fraud_samples / fraud_samples)\n",
    "    },\n",
    "    'custom': {\n",
    "        0: 1.0,\n",
    "        1: 2 * (non_fraud_samples / fraud_samples)\n",
    "    }\n",
    "}\n",
    "\n",
    "# Select weight approach\n",
    "selected_weight = weight_options['inverse']  # Choose from: 'balanced', 'inverse', 'sqrt', 'custom'\n",
    "print(f\"\\nSelected weight approach: {selected_weight}\")\n",
    "\n",
    "# 4. Model Training\n",
    "print(\"\\nTraining Logistic Regression model...\")\n",
    "lr_outlier_model = LogisticRegression(\n",
    "    penalty='l2',          # L2 regularization to prevent overfitting\n",
    "    C=1.0,                 # Inverse of regularization strength\n",
    "    class_weight=selected_weight,  # Handle class imbalance\n",
    "    solver='liblinear',    # Efficient solver for small datasets\n",
    "    max_iter=1000,         # Maximum iterations for convergence\n",
    "    verbose=1,             # Output training progress\n",
    "    n_jobs=-1,             # Use all available cores\n",
    "    random_state=42        # For reproducibility\n",
    ")\n",
    "\n",
    "lr_outlier_model.fit(X_train_scaled, y_train)\n",
    "training_time = time.time() - start_time\n",
    "print(f\"Training completed in {training_time:.2f} seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions completed in 6.78 seconds\n",
      "\n",
      "---------- Logistic Regression (with Outlier Handling) Model Evaluation ----------\n",
      "\n",
      "Classification Report (Test Set):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906351\n",
      "           1       1.00      1.00      1.00      2435\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       1.00      1.00      1.00   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n",
      "\n",
      "---------- Training vs Test Performance (Overfitting Analysis) ----------\n",
      "Metric           Training         Test   Difference\n",
      "--------------------------------------------------\n",
      "Accuracy     99.99916926% 99.99905699%  0.00011226%\n",
      "Precision    99.72284774% 99.67118783%  0.05165991%\n",
      "Recall       99.63655244% 99.58932238%  0.04723006%\n",
      "F1 Score     99.67968141% 99.63023829%  0.04944312%\n",
      "ROC AUC      99.95202126% 99.96174846% -0.00972720%\n",
      "PR AUC       99.65536377% 99.59652943%  0.05883434%\n",
      "\n",
      "Overfitting Score: 0.03292541%\n",
      "Model shows good generalization (score < 2%)\n",
      "\n",
      "---------- Detailed Test Metrics ----------\n",
      "➤➤➤ Accuracy: 99.99905699 %\n",
      "➤➤➤ Precision: 99.67118783 %\n",
      "➤➤➤ Recall: 99.58932238 %\n",
      "➤➤➤ F1 Score: 99.63023829 %\n",
      "➤➤➤ ROC AUC: 99.96174846 %\n",
      "➤➤➤ PR AUC: 99.59652943 %\n"
     ]
    }
   ],
   "source": [
    "# 5. Model Evaluation\n",
    "# Evaluate on training data\n",
    "y_train_pred = lr_outlier_model.predict(X_train_scaled)\n",
    "y_train_pred_proba = lr_outlier_model.predict_proba(X_train_scaled)[:, 1]\n",
    "\n",
    "# Evaluate on test data\n",
    "y_pred_lr = lr_outlier_model.predict(X_test_scaled)\n",
    "y_pred_proba_lr = lr_outlier_model.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "prediction_time = time.time() - start_time - training_time\n",
    "print(f\"Predictions completed in {prediction_time:.2f} seconds\")\n",
    "\n",
    "print(\"\\n---------- Logistic Regression (with Outlier Handling) Model Evaluation ----------\")\n",
    "print(\"\\nClassification Report (Test Set):\")\n",
    "print(classification_report(y_test, y_pred_lr))\n",
    "\n",
    "# Calculate metrics for both training and test sets\n",
    "train_metrics = {\n",
    "    'Accuracy': accuracy_score(y_train, y_train_pred) * 100,\n",
    "    'Precision': precision_score(y_train, y_train_pred) * 100,\n",
    "    'Recall': recall_score(y_train, y_train_pred) * 100,\n",
    "    'F1 Score': f1_score(y_train, y_train_pred) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_train, y_train_pred_proba) * 100,\n",
    "    'PR AUC': average_precision_score(y_train, y_train_pred_proba) * 100\n",
    "}\n",
    "\n",
    "test_metrics = {\n",
    "    'Accuracy': accuracy_score(y_test, y_pred_lr) * 100,\n",
    "    'Precision': precision_score(y_test, y_pred_lr) * 100,\n",
    "    'Recall': recall_score(y_test, y_pred_lr) * 100,\n",
    "    'F1 Score': f1_score(y_test, y_pred_lr) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_test, y_pred_proba_lr) * 100,\n",
    "    'PR AUC': average_precision_score(y_test, y_pred_proba_lr) * 100\n",
    "}\n",
    "\n",
    "print(\"\\n---------- Training vs Test Performance (Overfitting Analysis) ----------\")\n",
    "print(f\"{'Metric':<12} {'Training':>12} {'Test':>12} {'Difference':>12}\")\n",
    "print(\"-\" * 50)\n",
    "for metric in train_metrics.keys():\n",
    "    diff = train_metrics[metric] - test_metrics[metric]\n",
    "    print(f\"{metric:<12} {train_metrics[metric]:>11.8f}% {test_metrics[metric]:>11.8f}% {diff:>11.8f}%\")\n",
    "\n",
    "# Calculate overall overfitting score (average difference across metrics)\n",
    "overfitting_score = np.mean([train_metrics[m] - test_metrics[m] for m in train_metrics.keys()])\n",
    "print(f\"\\nOverfitting Score: {overfitting_score:.8f}%\")\n",
    "if overfitting_score > 5:\n",
    "    print(\"WARNING: Model shows signs of overfitting (score > 5%)\")\n",
    "elif overfitting_score < 2:\n",
    "    print(\"Model shows good generalization (score < 2%)\")\n",
    "else:\n",
    "    print(\"Model shows moderate overfitting\")\n",
    "\n",
    "# Display individual metrics with arrows for visual emphasis\n",
    "print(\"\\n---------- Detailed Test Metrics ----------\")\n",
    "for metric, value in test_metrics.items():\n",
    "    print(f'➤➤➤ {metric}: {value:.8f} %')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6. Visualization\n",
    "# Confusion Matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "cm = confusion_matrix(y_test, y_pred_lr)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix - Logistic Regression (Do outlier handling)')\n",
    "plt.ylabel('True Label')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape is :  (6362620, 7)\n",
      "\n",
      "y shape is :  (6362620,)\n",
      "Preprocessing complete: Added 2 outlier indicator features\n",
      "Original shape: (6362620, 7), New shape: (6362620, 9)\n",
      "\n",
      "Original class distribution in training set:\n",
      "isFraud\n",
      "0    99.87092\n",
      "1     0.12908\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Balanced class distribution after RUS:\n",
      "isFraud\n",
      "0    66.666667\n",
      "1    33.333333\n",
      "Name: proportion, dtype: float64\n",
      "Original training samples: 4453834\n",
      "After RUS: 17247\n"
     ]
    }
   ],
   "source": [
    "# X Data\n",
    "X = df.drop(['isFraud'], axis=1)\n",
    "print('X shape is : ', X.shape)\n",
    "print()\n",
    "y = df['isFraud']\n",
    "print('y shape is : ', y.shape)\n",
    "\n",
    "# Implement improved preprocessing pipeline\n",
    "def preprocess_data(X, y):\n",
    "    \"\"\"\n",
    "    Improved preprocessing pipeline for fraud detection that preserves\n",
    "    outlier information while limiting extreme values\n",
    "    \"\"\"\n",
    "    # Create a copy to avoid modifying originals\n",
    "    X_processed = X.copy()\n",
    "    \n",
    "    # Add outlier indicator features for important columns\n",
    "    numeric_cols = X.select_dtypes(include=np.number).columns\n",
    "    \n",
    "    # Add outlier indicators for financial columns\n",
    "    financial_cols = ['oldbalanceOrg', 'newbalanceDest']\n",
    "    for col in financial_cols:\n",
    "        if col in X.columns:\n",
    "            z_scores = np.abs(stats.zscore(X[col]))\n",
    "            X_processed[f'{col}_outlier'] = (z_scores > 3).astype(int)\n",
    "    \n",
    "    # Winsorize only the extreme outliers in amount columns (99.5% percentile)\n",
    "    for col in financial_cols:\n",
    "        if col in X.columns:\n",
    "            lower = X[col].quantile(0.005)\n",
    "            upper = X[col].quantile(0.995)\n",
    "            X_processed[col] = np.clip(X[col], lower, upper)\n",
    "    \n",
    "    print(f\"Preprocessing complete: Added {len(financial_cols)} outlier indicator features\")\n",
    "    print(f\"Original shape: {X.shape}, New shape: {X_processed.shape}\")\n",
    "    \n",
    "    return X_processed, y\n",
    "\n",
    "# Apply the improved preprocessing\n",
    "X_processed, y_processed = preprocess_data(X, y)\n",
    "\n",
    "# Split the processed data\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_processed, y_processed, test_size=0.3, random_state=42, shuffle=True, stratify=y_processed\n",
    ")\n",
    "\n",
    "# Print initial class distribution\n",
    "print(\"\\nOriginal class distribution in training set:\")\n",
    "print(pd.Series(y_train).value_counts(normalize=True) * 100)\n",
    "\n",
    "# Apply Random Under-Sampling to balance classes\n",
    "# You can adjust the sampling_strategy for different levels of undersampling\n",
    "rus = RandomUnderSampler(sampling_strategy=0.5, random_state=42)  # 1:2 ratio of minority:majority\n",
    "X_train_rus, y_train_rus = rus.fit_resample(X_train, y_train)\n",
    "\n",
    "# Print balanced class distribution\n",
    "print(\"\\nBalanced class distribution after RUS:\")\n",
    "print(pd.Series(y_train_rus).value_counts(normalize=True) * 100)\n",
    "print(f\"Original training samples: {X_train.shape[0]}\")\n",
    "print(f\"After RUS: {X_train_rus.shape[0]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training XGBoost model on RUS-balanced data...\n",
      "[0]\tvalidation_0-logloss:0.41013\n",
      "[100]\tvalidation_0-logloss:0.13930\n",
      "[199]\tvalidation_0-logloss:0.05416\n",
      "Training completed in 8.26 seconds\n"
     ]
    }
   ],
   "source": [
    "# 4. Model Training with XGBoost\n",
    "print(\"\\nTraining XGBoost model on RUS-balanced data...\")\n",
    "start_time = time.time()  # Start timer\n",
    "\n",
    "xgb_model = xgb.XGBClassifier(\n",
    "    learning_rate=0.01,         # Learning rate\n",
    "    n_estimators=200,           # Number of trees\n",
    "    max_depth=6,                # Maximum tree depth\n",
    "    min_child_weight=1,         # Minimum child weight\n",
    "    gamma=0,                    # Minimum loss reduction for split\n",
    "    subsample=0.8,              # Subsample ratio of training instances\n",
    "    colsample_bytree=0.8,       # Subsample ratio of columns\n",
    "    objective='binary:logistic', # Objective function\n",
    "    scale_pos_weight=1,         # No need for additional weighting since data is balanced\n",
    "    random_state=42             # Random seed\n",
    ")\n",
    "\n",
    "# Train the model on the balanced dataset\n",
    "xgb_model.fit(\n",
    "    X_train_rus, \n",
    "    y_train_rus,\n",
    "    eval_set=[(X_test, y_test)],\n",
    "    verbose=100\n",
    ")\n",
    "\n",
    "training_time = time.time() - start_time\n",
    "print(f\"Training completed in {training_time:.2f} seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions completed in 20.00 seconds\n",
      "\n",
      "---------- RUS+XGBoost Model Evaluation ----------\n",
      "\n",
      "Classification Report (Test Set):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906322\n",
      "           1       1.00      1.00      1.00      2464\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       1.00      1.00      1.00   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n",
      "\n",
      "---------- Training vs Test Performance (Overfitting Analysis) ----------\n",
      "Metric           Training         Test   Difference\n",
      "--------------------------------------------------\n",
      "Accuracy     99.99946114% 99.99931894%  0.00014220%\n",
      "Precision    99.96508989% 99.83733225%  0.12775764%\n",
      "Recall       99.61732475% 99.63474026% -0.01741551%\n",
      "F1 Score     99.79090434% 99.73593337%  0.05497096%\n",
      "ROC AUC      99.88084184% 99.88009483%  0.00074701%\n",
      "PR AUC       99.62010658% 99.63627372% -0.01616714%\n",
      "\n",
      "Overfitting Score: 0.02500586%\n",
      "Model shows good generalization (score < 2%)\n"
     ]
    }
   ],
   "source": [
    "# 5. Model Evaluation\n",
    "# Evaluate on training data (original unbalanced set)\n",
    "y_train_pred = xgb_model.predict(X_train)\n",
    "y_train_pred_proba = xgb_model.predict_proba(X_train)[:, 1]\n",
    "\n",
    "# Evaluate on test data\n",
    "y_pred_xgb = xgb_model.predict(X_test)\n",
    "y_pred_proba_xgb = xgb_model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "prediction_time = time.time() - start_time - training_time\n",
    "print(f\"Predictions completed in {prediction_time:.2f} seconds\")\n",
    "\n",
    "# Calculate metrics for both training and test sets\n",
    "train_metrics = {\n",
    "    'Accuracy': accuracy_score(y_train, y_train_pred) * 100,\n",
    "    'Precision': precision_score(y_train, y_train_pred) * 100,\n",
    "    'Recall': recall_score(y_train, y_train_pred) * 100,\n",
    "    'F1 Score': f1_score(y_train, y_train_pred) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_train, y_train_pred_proba) * 100,\n",
    "    'PR AUC': average_precision_score(y_train, y_train_pred_proba) * 100\n",
    "}\n",
    "\n",
    "test_metrics = {\n",
    "    'Accuracy': accuracy_score(y_test, y_pred_xgb) * 100,\n",
    "    'Precision': precision_score(y_test, y_pred_xgb) * 100,\n",
    "    'Recall': recall_score(y_test, y_pred_xgb) * 100,\n",
    "    'F1 Score': f1_score(y_test, y_pred_xgb) * 100,\n",
    "    'ROC AUC': roc_auc_score(y_test, y_pred_proba_xgb) * 100,\n",
    "    'PR AUC': average_precision_score(y_test, y_pred_proba_xgb) * 100\n",
    "}\n",
    "\n",
    "print(\"\\n---------- RUS+XGBoost Model Evaluation ----------\")\n",
    "print(\"\\nClassification Report (Test Set):\")\n",
    "print(classification_report(y_test, y_pred_xgb))\n",
    "\n",
    "print(\"\\n---------- Training vs Test Performance (Overfitting Analysis) ----------\")\n",
    "print(f\"{'Metric':<12} {'Training':>12} {'Test':>12} {'Difference':>12}\")\n",
    "print(\"-\" * 50)\n",
    "for metric in train_metrics.keys():\n",
    "    diff = train_metrics[metric] - test_metrics[metric]\n",
    "    print(f\"{metric:<12} {train_metrics[metric]:>11.8f}% {test_metrics[metric]:>11.8f}% {diff:>11.8f}%\")\n",
    "\n",
    "# Calculate overall overfitting score (average difference across metrics)\n",
    "overfitting_score = np.mean([train_metrics[m] - test_metrics[m] for m in train_metrics.keys()])\n",
    "print(f\"\\nOverfitting Score: {overfitting_score:.8f}%\")\n",
    "if overfitting_score > 5:\n",
    "    print(\"WARNING: Model shows signs of overfitting (score > 5%)\")\n",
    "elif overfitting_score < 2:\n",
    "    print(\"Model shows good generalization (score < 2%)\")\n",
    "else:\n",
    "    print(\"Model shows moderate overfitting\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6. Visualization\n",
    "# Confusion Matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "cm = confusion_matrix(y_test, y_pred_xgb)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix - RUS+XGBoost')\n",
    "plt.ylabel('True Label')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# warnings.filterwarnings('ignore')\n",
    "\n",
    "# def evaluate_model(model, X_test_scaled, y_test, model_name, threshold=0.5):\n",
    "#     \"\"\"Evaluate a model using various metrics\"\"\"\n",
    "#     start_time = time.time()\n",
    "    \n",
    "#     # Get predictions (handle different model types)\n",
    "#     try:\n",
    "#         y_pred_proba = model.predict_proba(X_test_scaled)[:, 1]\n",
    "#     except:\n",
    "#         # For models that might not have predict_proba\n",
    "#         y_pred_proba = model.predict(X_test_scaled)\n",
    "    \n",
    "#     y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "    \n",
    "#     # Calculate metrics\n",
    "#     accuracy = accuracy_score(y_test, y_pred)\n",
    "#     precision = precision_score(y_test, y_pred)\n",
    "#     recall = recall_score(y_test, y_pred)\n",
    "#     f1 = f1_score(y_test, y_pred)\n",
    "#     roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "    \n",
    "#     # Calculate precision-recall AUC\n",
    "#     precision_curve, recall_curve, _ = precision_recall_curve(y_test, y_pred_proba)\n",
    "#     pr_auc = auc(recall_curve, precision_curve)\n",
    "    \n",
    "#     # Confusion matrix\n",
    "#     cm = confusion_matrix(y_test, y_pred)\n",
    "    \n",
    "#     # Speed test\n",
    "#     inference_time = time.time() - start_time\n",
    "    \n",
    "#     # Return all metrics in a dictionary\n",
    "#     return {\n",
    "#         'model_name': model_name,\n",
    "#         'accuracy': accuracy,\n",
    "#         'precision': precision,\n",
    "#         'recall': recall,\n",
    "#         'f1_score': f1,\n",
    "#         'roc_auc': roc_auc,\n",
    "#         'pr_auc': pr_auc,\n",
    "#         'confusion_matrix': cm,\n",
    "#         'inference_time': inference_time,\n",
    "#         'y_pred': y_pred,\n",
    "#         'y_pred_proba': y_pred_proba\n",
    "#     }\n",
    "\n",
    "# # Use the models you've already trained (these variables should be defined in your environment)\n",
    "# # If any of these variables aren't defined, remove them from the dictionary\n",
    "# models_in_memory = {\n",
    "#     'XGBoost': {'model': xgb_model, 'threshold': 0.5},\n",
    "#     'LightGBM': {'model': lgb_model, 'threshold': 0.5},\n",
    "#     'Random Forest': {'model': rf_model, 'threshold': 0.5},\n",
    "#     'Logistic Regression (weighted)': {'model': lr_model, 'threshold': 0.5},\n",
    "#     'K-Nearest Neighbors': {'model': knn_model, 'threshold': 0.5},\n",
    "# }\n",
    "\n",
    "# # Remove any models that aren't actually defined in your environment\n",
    "# models = {}\n",
    "# for name, model_info in models_in_memory.items():\n",
    "#     if 'model' in model_info and model_info['model'] is not None:\n",
    "#         models[name] = model_info\n",
    "#         print(f\"Using {name} model from memory\")\n",
    "\n",
    "# # Evaluate all models\n",
    "# results = []\n",
    "# for name, model_info in models.items():\n",
    "#     model = model_info['model']\n",
    "#     threshold = model_info['threshold']\n",
    "#     print(f\"Evaluating {name} model...\")\n",
    "    \n",
    "#     eval_results = evaluate_model(model, X_test_scaled, y_test, name, threshold)\n",
    "#     results.append(eval_results)\n",
    "    \n",
    "#     print(f\"  - Accuracy: {eval_results['accuracy']:.8f}\")\n",
    "#     print(f\"  - Precision: {eval_results['precision']:.8f}\")\n",
    "#     print(f\"  - Recall: {eval_results['recall']:.8f}\")\n",
    "#     print(f\"  - F1 Score: {eval_results['f1_score']:.8f}\")\n",
    "#     print(f\"  - ROC AUC: {eval_results['roc_auc']:.8f}\")\n",
    "#     print(f\"  - PR AUC: {eval_results['pr_auc']:.8f}\")\n",
    "#     print(f\"  - Inference time: {eval_results['inference_time']:.8f} seconds\")\n",
    "#     print()\n",
    "\n",
    "# # Create DataFrame with all results\n",
    "# results_df = pd.DataFrame([{\n",
    "#     'Model': r['model_name'],\n",
    "#     'Accuracy (%)': r['accuracy'] * 100,\n",
    "#     'Precision (%)': r['precision'] * 100,\n",
    "#     'Recall (%)': r['recall'] * 100,\n",
    "#     'F1 Score (%)': r['f1_score'] * 100,\n",
    "#     'ROC AUC (%)': r['roc_auc'] * 100,\n",
    "#     'PR AUC (%)': r['pr_auc'] * 100,\n",
    "#     'Inference Time (s)': r['inference_time']\n",
    "# } for r in results])\n",
    "\n",
    "# # Display table of all metrics\n",
    "# print(\"\\n==== Model Comparison ====\")\n",
    "# print(results_df.to_string(index=False, float_format=lambda x: f\"{x:.8f}\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# # Plot metrics comparison\n",
    "# metrics_to_plot = ['Accuracy (%)', 'Precision (%)', 'Recall (%)', \n",
    "#                     'F1 Score (%)', 'ROC AUC (%)', 'PR AUC (%)']\n",
    "\n",
    "# plt.figure(figsize=(14, 10))\n",
    "# for i, metric in enumerate(metrics_to_plot):\n",
    "#     plt.subplot(2, 3, i+1)\n",
    "#     sns.barplot(x='Model', y=metric, data=results_df)\n",
    "#     plt.title(metric)\n",
    "#     plt.xticks(rotation=45)\n",
    "#     plt.ylim(0, 100)\n",
    "# plt.tight_layout()\n",
    "# plt.suptitle('Model Performance Comparison', fontsize=16)\n",
    "# plt.subplots_adjust(top=0.9)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# # Plot ROC curves for all models\n",
    "# plt.figure(figsize=(10, 8))\n",
    "# for r in results:\n",
    "#     # Calculate ROC curve\n",
    "#     fpr, tpr, _ = roc_curve(y_test, r['y_pred_proba'])\n",
    "#     label = f\"{r['model_name']} (AUC = {r['roc_auc']:.3f})\"\n",
    "#     plt.plot(fpr, tpr, label=label)\n",
    "\n",
    "# plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
    "# plt.xlabel('False Positive Rate')\n",
    "# plt.ylabel('True Positive Rate')\n",
    "# plt.title('ROC Curves for All Models')\n",
    "# plt.legend()\n",
    "# plt.grid(True, alpha=0.3)\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# # Plot Precision-Recall curves for all models\n",
    "# plt.figure(figsize=(10, 8))\n",
    "# for r in results:\n",
    "#     # Calculate Precision-Recall curve\n",
    "#     precision, recall, _ = precision_recall_curve(y_test, r['y_pred_proba'])\n",
    "#     label = f\"{r['model_name']} (AUC = {r['pr_auc']:.3f})\"\n",
    "#     plt.plot(recall, precision, label=label)\n",
    "\n",
    "# # Add the random baseline\n",
    "# no_skill = sum(y_test == 1) / len(y_test)\n",
    "# plt.plot([0, 1], [no_skill, no_skill], 'k--', label='Random')\n",
    "# plt.xlabel('Recall')\n",
    "# plt.ylabel('Precision')\n",
    "# plt.title('Precision-Recall Curves for All Models')\n",
    "# plt.legend()\n",
    "# plt.grid(True, alpha=0.3)\n",
    "# plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Plot confusion matrices for each model\n",
    "# num_models = len(results)\n",
    "# fig, axes = plt.subplots(nrows=(num_models+1)//2, ncols=2, figsize=(15, 4*((num_models+1)//2)))\n",
    "# axes = axes.flatten() if num_models > 1 else [axes]\n",
    "\n",
    "# for i, r in enumerate(results):\n",
    "#     if i < len(axes):\n",
    "#         cm = r['confusion_matrix']\n",
    "#         sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[i])\n",
    "#         axes[i].set_title(f'Confusion Matrix - {r[\"model_name\"]}')\n",
    "#         axes[i].set_ylabel('True Label')\n",
    "#         axes[i].set_xlabel('Predicted Label')\n",
    "\n",
    "# # Hide any unused subplots\n",
    "# for j in range(i+1, len(axes)):\n",
    "#     fig.delaxes(axes[j])\n",
    "\n",
    "# plt.tight_layout()\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# # Calculate inference time comparison\n",
    "# plt.figure(figsize=(10, 6))\n",
    "# sns.barplot(x='Model', y='Inference Time (s)', data=results_df)\n",
    "# plt.title('Model Inference Time Comparison')\n",
    "# plt.xticks(rotation=45)\n",
    "# plt.ylabel('Time (seconds)')\n",
    "# plt.grid(True, alpha=0.3)\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Find the best model for each metric\n",
    "# best_models = {}\n",
    "# for metric in metrics_to_plot:\n",
    "#     if 'Time' not in metric:\n",
    "#         best_idx = results_df[metric].idxmax()\n",
    "#         best_models[metric] = results_df.loc[best_idx, 'Model']\n",
    "#     else:\n",
    "#         best_idx = results_df[metric].idxmin()\n",
    "#         best_models[metric] = results_df.loc[best_idx, 'Model']\n",
    "\n",
    "# print(\"\\n==== Best Models ====\")\n",
    "# for metric, model in best_models.items():\n",
    "#     print(f\"Best for {metric}: {model}\")\n",
    "\n",
    "# # Print the overall recommendation\n",
    "# print(\"\\n==== Recommendation ====\")\n",
    "# # Count how many times each model is the best\n",
    "# model_counts = {}\n",
    "# for model in best_models.values():\n",
    "#     if model in model_counts:\n",
    "#         model_counts[model] += 1\n",
    "#     else:\n",
    "#         model_counts[model] = 1\n",
    "\n",
    "# # Find the model with the highest count\n",
    "# best_overall = max(model_counts.items(), key=lambda x: x[1])[0]\n",
    "\n",
    "# print(f\"Overall recommended model: {best_overall}\")\n",
    "# print(\"This model achieves the best balance of performance metrics.\")\n",
    "\n",
    "# # If fraud detection is the priority, emphasize recall and F1\n",
    "# print(\"\\nFor fraud detection specifically:\")\n",
    "# print(f\"Best model for recall (detecting frauds): {best_models['Recall (%)']}\")\n",
    "# print(f\"Best model for F1 score (balance): {best_models['F1 Score (%)']}\")\n",
    "# print(f\"Best model for PR AUC (overall ranking): {best_models['PR AUC (%)']}\")\n",
    "\n",
    "# # If interpretability is needed\n",
    "# interpretable_models = ['Logistic Regression', 'Logistic Regression (weighted)']\n",
    "# interpretable_present = [model for model in interpretable_models if model in models]\n",
    "# if interpretable_present:\n",
    "#     print(\"\\nIf interpretability is required, consider:\")\n",
    "#     for model in interpretable_present:\n",
    "#         print(f\"- {model}\")"
   ]
  }
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