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What are best practices for Handling Missing Data

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When working with Handling Missing Data, start with a clear goal, test on real data early, keep the approach as simple as possible at first, and follow established practices from the AI community rather than guessing.
Real-world example A healthcare dataset with some missing age values might use the average patient age to fill in the gaps before training a model.

Common follow-ups: What is AI Data Preparation & Feature Engineering, How is Handling Missing Data evaluated in practice, What tools are commonly used for Handling Missing Data

AI Data Preparation & Feature Engineering topics: Data Cleaning for AI Models Handling Missing Data Feature Scaling & Normalization
All 7 Handling Missing Data questions