Fraud detection is one of those problems that looks simple on the surface — classify transactions as “fraud” or “not fraud”. But once you look at real data, it becomes a completely different challenge. In this project, I built FraudShield, an end-to-end machine learning system to detect fraudulent credit card transactions using both supervised and unsupervised approaches, along with a live dashboard. 📊 The Problem The dataset I used contains over 284,000 transactions, but only: 👉 0.17% are fraud This creates a highly imbalanced dataset, where a model can achieve 99% accuracy just by predicting everything as “not fraud”. So the real question becomes: How do we detect fraud when it’s so rare? 🔍 Dataset Overview The dataset contains real-world credit card transactions made by European cardholders, anonymised using PCA transformation to protect sensitive information. It includes 284,807 transactions, of which only 492 are fraudulent (~0.17%), making it a highly imbalanced classification problem.…