Problem
Scenario
You are working on a supervised learning problem and find that parts of the dataset are incomplete, some labels or features look noisy, and the sample may not represent the population you care about.
Question
How would you handle missing, noisy, or biased data in your research?
Example Dataset
Size·120K customer sessions, 38 featuresTarget·Binary conversion after recommendationFeatures·Numeric behavior signals, categorical profile fields, channel metadata, historical aggregatesMissing data·5% to 35% missing by feature, plus weak labels and underrepresented channelsClass balance·18% positive
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