Data Cleaning
Domain: Data & Datasets
🎯 Motivation
Fixing errors in data: removing duplicates, fixing labels, handling missing values, correcting formatting.
📋 Concrete Example
Cleaning: remove 500 duplicate entries, fix 200 mislabeled items, fill 50 missing values, standardize date formats. Clean data = better training.
🔗 Analogy
Washing vegetables before cooking - you remove dirt and bad spots. Data cleaning removes errors and inconsistencies before training.