What are the different strategies for handling missing data, and how do you decide which approach to use?

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Handling missing data is a critical preprocessing step that can significantly impact model performance. The best strategy depends on the amount of missing data, the pattern of missingness, and the underlying reasons data is missing. First, understand why data is missing.