FraudShield AI: Machine Learning for Credit Card Fraud Detection Under Severe Class Imbalance

FraudShield AI is a machine-learning project designed to identify potentially fraudulent credit card transactions using artificial intelligence. Built with Python, scikit-learn, pandas, and Streamlit, the application uses a Random Forest classification model to analyze transaction data and distinguish between legitimate and potentially fraudulent activity. It features an interactive dashboard for exploring transaction records, checking individual transactions, visualizing model performance, and examining feature importance. Through this project, I gained practical experience in machine learning, data analysis, model evaluation, and application development while exploring the challenges of imbalanced datasets and responsible AI. FraudShield AI reflects my interest in combining artificial intelligence and cybersecurity to develop practical solutions for financial security.