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shahviransh/
fraud-detection

Name: fraud-detection
Availability: InStock

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E-Commerce Fraud Detection Model

Model Description

This is an ensemble fraud detection system trained on 1.47M e-commerce transactions with a 5.01% fraud rate.

Architecture

Weighted Ensemble Strategy (70%-30%)

Stage 1 - Recall Specialists (70% weight): Logistic Regression + Random Forest
Stage 2 - Precision Specialists (30% weight): Neural Network + XGBoost

Performance Metrics

Model	Accuracy	Precision	Recall	F1-Score	AUC-ROC
Logistic Regression	0.5723	0.0988	0.9273	0.1786	0.8619
Random Forest	0.6203	0.1075	0.8999	0.1920	0.8712
Neural Network	0.9569	0.7013	0.2442	0.3623	0.8748
XGBoost	0.9558	0.6632	0.2389	0.3513	0.8459
Stacking Ensemble	0.8973	0.2640	0.5868	0.3642	0.8731

Key Features

52 engineered features including:
- Transaction patterns (amount, quantity, frequency)
- Customer behavior (account age, transaction history)
- Temporal features (time-based patterns)
- Risk indicators (unusual patterns, high-value flags)
- Interaction features (multi-dimensional risk signals)

Training

Resampling: ADASYN (1:1 balance)
GPU Acceleration: RAPIDS cuML, PyTorch, XGBoost
Threshold Optimization: F-beta score optimization
Validation: Stratified K-Fold Cross-Validation

Usage

python

### Usage

## Warning: Need GPU environment with CUDA installed

```python
import joblib
import numpy as np

# Load models
lr_model = joblib.load("lr_model.pkl")
rf_model = joblib.load("rf_model.pkl")
nn_model = joblib.load("nn_model.pkl")
xgb_model = joblib.load("xgb_model.pkl")
ensemble_model = joblib.load("ensemble_model.pkl")
scaler = joblib.load("scaler.pkl")

# Prepare your data
df = ...

X = df[df.columns.difference(['Is Fraudulent'])].copy()
y = df['Is Fraudulent'].copy()

# Predict with ensemble
fraud_proba = ensemble_model.predict_proba(X)[:, 1]
fraud_pred = ensemble_model.predict(X)

# Evaluate predictions
evaluate_models([lr_model, rf_model, nn_model, xgb_model, ensemble_model], X, y, ['Logistic Regression', 'Random Forest', 'Neural Network', 'XGBoost', 'Stacking Ensemble'])

License

MIT License

Contact

COMPSCI 4AL3 - Group 34

Viransh Shah (shahv47@mcmaster.ca) Ellen Xiong (xionge1@mcmaster.ca)

shahviransh/fraud-detection