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What is Handling Missing Data

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Answer

Handling missing data involves deciding how to deal with gaps in a dataset where some values were never recorded or collected.
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