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AI Data Preparation & Feature Engineering › Handling Missing Data

How does Handling Missing Data work

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Answer

Common approaches include removing rows with missing values, filling them in with reasonable estimates like the average, or using models designed to handle missing values directly.
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