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Why are missing values handled during data cleaning?

Answer and explanation

Correct answer: They can affect analysis results and model training.

Missing values indicate that some information is unavailable. If they are not handled, they can bias analysis results or prevent some models from being trained. Depending on the data and context, they may be removed, filled with suitable values, or marked as a separate category. Option B is incorrect because not all machine-learning algorithms handle missing values automatically and correctly.

Related tags

Missing ValuesData CleaningData PreprocessingData QualityMachine Learning

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