The Preprocessing Survival Guide for New Data Scientists / ML Engineers

This post breaks down five common preprocessing mistakes junior data scientists make — from blindly filling nulls to mishandling outliers. It emphasizes the importance of understanding the meaning behind the data, not just applying standard tools and techniques. With real-world examples and practical advice, it serves as a hands-on survival guide for anyone transitioning from classroom data to messy production environments.