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About the Application

The Online Payments Fraud Detection application predicts fraudulent transactions in online payment systems using advanced machine learning techniques. With the growing risk of online payment fraud, this model helps financial institutions and e-commerce platforms identify suspicious transactions in real-time.

Features

  1. Real-Time Fraud Detection
  2. Detects fraud during transaction processing by analyzing transaction features like amount, origin, and time.

  3. Provides immediate feedback to help prevent fraudulent activities.

  4. Interactive Interface

  5. Powered by Streamlit, users can easily input transaction details to get predictions.

  6. Designed for accessibility and user-friendliness, ensuring a seamless experience.

  7. Comprehensive Feature Engineering

  8. Implements advanced preprocessing steps to clean and optimize transaction data.
  9. Uses dimensionality reduction techniques like Principal Component Analysis (PCA) to improve model performance.

How It Works

The application leverages the XGBoost machine learning model to detect fraudulent transactions based on transaction attributes. Here’s how it operates:

  • Data Preprocessing: Transaction data undergoes cleaning and feature scaling to ensure consistency.
  • Feature Selection: PCA reduces input dimensions, retaining only the most critical features.
  • Prediction: The trained XGBoost model evaluates transaction details to classify them as genuine or fraudulent.

This approach ensures accurate predictions while maintaining computational efficiency.

Key Highlights

  • Machine Learning Backbone: Powered by XGBoost, renowned for its accuracy and efficiency in handling imbalanced datasets.
  • AUC-ROC Performance: Achieves a score of 0.9556, demonstrating its reliability in fraud detection.
  • Seamless Integration: Can be embedded into existing payment systems for real-time fraud analysis.