Online Payment Fraud Detection using Machine Learning

This project focuses on detecting fraudulent online payment transactions using machine learning techniques in Python. It involves collecting and preprocessing transaction data, applying feature engineering, and training various classification models to differentiate between legitimate and fraudulent transactions. Techniques such as logistic regression, decision trees, random forests, and deep learning models like neural networks can be explored to improve accuracy. The project also includes performance evaluation using metrics like accuracy, precision, recall, and F1-score. The ultimate goal is to develop a robust fraud detection system that can minimize financial losses and enhance transaction security in real-time.