You build a model, run the evaluations, and hit a 95% accuracy on your test set. You deploy it to production feeling like a genius, only to watch it fail miserably on real-world data. We’ve all been there. When a model explodes in production after perfect local testing, the culprit is rarely the algorithm itself. Most of the time, it’s a silent architecture flaw introduced during the very first steps of preprocessing: Data Leakage. In this article, we will discover how one of the most common mistakes in handling Missing Values and oversampling implicitly corrupts your test data, and how to build a bulletproof, leak-free pipeline using Scikit-Learn.

The common error is preprocessing before splitting

Let’s look at a classic approach to data preparation.…