Data Cleaning

Overview

Cleaning turns raw data into a consistent, model-ready table without leaking information from the future or the test set. The golden rule: every statistic used to clean (means, medians, modes, bounds, category maps) must be learned from the training split only, then applied to validation/test.

When to use

• Raw data has nulls, duplicates, mixed types, or junk categories.
• Before feature-engineering and modeling.
• After EDA flagged specific quality issues.

Workflow