Data Quality Over Model Selection: Why Preprocessing Matters More Than Algorithms

This article challenges the common misconception that better algorithms drive model performance, revealing instead that data quality and preprocessing are the true drivers of improvement. Through practical examples of handling missing values, removing outliers, and encoding categorical features, it demonstrates that investing in data preparation often yields far greater returns than model complexity. For ML practitioners, this reframes development priority: optimize your data pipeline before optimizing your algorithm selection.