This project is a production-grade Credit Card Fraud Detection System designed and implemented by Anshuman Sinha. It leverages advanced machine learning techniques to identify fraudulent transactions in real-time, helping financial institutions reduce losses and enhance security.
Built with scalability, explainability, and robustness in mind, the system integrates thorough data preprocessing, state-of-the-art machine learning models, and comprehensive evaluation metrics to ensure reliable fraud detection performance.
-
End-to-End Industrial Pipeline:
From raw data ingestion through preprocessing, feature engineering, model training, and deployment-ready prediction capabilities. -
Robust Handling of Imbalanced Data:
Utilizes class weighting, careful evaluation metrics, and domain-specific threshold tuning to ensure sensitive detection of rare fraudulent events. -
Advanced Machine Learning Models:
Employs a blend of Logistic Regression, Random Forest, and XGBoost models optimized for fraud detection on large-scale transactional data. -
Model Explainability:
Incorporates feature importance analysis and probability calibration to provide high transparency and trustworthiness critical in regulated financial environments. -
Production-Ready Deployment:
Designed with a Streamlit-based interactive front-end for easy demonstration and integration, supported by serialized model and preprocessing artifacts for consistent inference. -
Performance & Efficiency:
Models have been optimized for fast training and prediction, leveraging parallel processing and tuned hyperparameters suited for enterprise-grade throughput.
- Programming Language: Python (3.9+)
- Data Processing: Pandas, NumPy
- Modeling Frameworks: scikit-learn, XGBoost
- Model Explainability: Feature importance, calibration techniques
- Deployment: Streamlit web app with joblib-serialized artifacts
- Optimization: Optuna hyperparameter tuning for model efficiency
This system empowers banks, payment gateways, and fintech companies to:
- Detect and prevent fraudulent credit card transactions proactively
- Reduce financial risk and loss due to fraud
- Improve customer trust through reliable transaction security
- Streamline fraud investigation workflows with explainable models
Anshuman Sinha
Email: anshumansinhadto@gmail.com
This repository represents a fully-realized industrial data science project, illustrating practical skills and a production mindset critical in today’s data-driven financial services industry.