Understanding why data is missing is critical before performing imputation. Missing data can be categorized into three types: Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). Each type requires different imputation techniques. MCAR is the least common and assumes no pattern in missing data, MAR can be explained by other observed features, and MNAR involves missing data with a pattern, usually related to unobserved features.
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