Example Concept: Missing data can be categorized into three types: Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). For MCAR, simple imputation methods like mean or median can be used without introducing bias. For MAR, more sophisticated techniques such as multiple imputation or using models like k-nearest neighbors (KNN) are recommended. MNAR requires domain knowledge to address the underlying reasons for missingness. Always evaluate the impact of imputation on the dataset's distribution and model performance.