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Core
Handling Missing and Corrupted Data
Missing data has three mechanisms (MCAR, MAR, MNAR) and the mechanism decides whether dropping rows is safe or biased and which imputation is valid. Beyond filling values, missingness itself is often a feature, and naive imputation is a classic source of leakage. Applied AI interviews probe it because how you handle gaps quietly determines whether your model is biased before training even starts.
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