Handling Missing Data

Real-world data is messy. Missing values are common and must be handled properly.

Types of Missing Data

Strategies for Missing Data

Key Takeaways

• Understand WHY data is missing
• Deletion is simple but loses data
• Mean/median imputation is most common
• For categorical: use mode
• Consider indicator variables to preserve missingness info
• Never use test data statistics for imputation!