Handling Class Imbalance in Fraud Detection with scikit-learn Every fraud detection tutorial I've seen makes the same mistake. They train a model, print the accuracy score — 99.8% — and declare success. That model is useless. In a dataset where 0.17% of transactions are fraudulent, a model that predicts "legitimate" for every single transaction achieves 99.83% accuracy. It has never detected a single fraud case in its life. This is the class imbalance problem and it's the most important thing to understand before building any fraud detection system. In this tutorial I'll show you exactly how to handle it correctly using scikit-learn. By the end you'll have a working fraud detection pipeline that actually catches fraud. Prerequisites Python 3.8+ Basic understanding of classification pip installed The Dataset We'll use the Credit Card Fraud Detection dataset from Kaggle. It contains 284,807 transactions with only 492 fraud cases — a fraud rate of 0.17%. This is a real-world class imbalance problem.…