Data Preprocessing.

Preprocessing prepares data for modeling by handling missing values, removing duplicates, correcting errors, encoding categories, scaling features, and more. It's often the most time-consuming part of ML projects but is crucial for model performance. Techniques include imputation, normalization, one-hot encoding, and outlier handling.