Problem
Scenario
You are training a supervised learning model and notice that several input features have missing values. You need a practical way to prepare the data without distorting the signal or introducing leakage.
Question
How would you handle a dataset with missing values?
Example Dataset
Size·420K member sessions, 38 featuresTarget·Completed order in sessionFeatures·Numerical and categorical ecommerce behavior featuresMissingness·2% to 18% across key fields, with some informative nulls
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