M. Fatih Tüzen fills in the gaps:
Data preprocessing is a cornerstone of any data analysis or machine learning pipeline. Raw data rarely comes in a form ready for direct analysis — it often requires cleaning, transformation, normalization, and careful handling of anomalies. Among these preprocessing tasks, dealing with missing data stands out as one of the most critical and unavoidable challenges.
Missing values appear in virtually every domain: surveys may have skipped questions, administrative registers might contain incomplete records, and clinical trials can suffer from dropout patients. Ignoring these gaps or handling them naively does not just reduce the amount of usable information; it can also introduce bias, decrease statistical power, and ultimately compromise the validity of conclusions. In other words, missing data is not just an inconvenience — it is a methodological problem that demands rigorous attention.
Quite often, we gloss over what to do with missing data when explaining or working through the data science process, in part because it’s a hard problem. This post digs into the specifics of the matter, taking us through eight separate methods. H/T R-Bloggers.