Your question is Handling Missing Data. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How would you handle missing data in a dataset, and what techniques would you use to minimize its impact on your model?
Answer for a supervised ML setting, where some features are partially missing and the target is already labeled. Focus on practical preprocessing choices, leakage prevention, and how you would validate whether imputation helps or hurts.
