Missing Data Analysis and Imputation Cheat Sheet

Missing data is a pervasive challenge in data science and machine learning, arising in virtually every real-world dataset from sensor failures, survey non-response, data integration issues, or intentional omissions. Understanding the mechanism behind the missingness—whether data is Missing Completely At Random (MCAR), Missing At Random (MAR), or Missing Not At Random (MNAR)—is critical because it fundamentally determines which imputation strategies produce valid, unbiased results and which introduce systematic distortion. Rather than simply deleting incomplete observations, modern practitioners employ a sophisticated toolkit spanning statistical methods, machine learning models, and deep learning architectures to reconstruct missing values while preserving distributional properties and relationships. The key insight is that missing values themselves carry information: the pattern and location of missingness can be engineered as features, and the choice between deletion, simple imputation, or complex multivariate approaches must balance statistical rigor, computational efficiency, and the ultimate use case.