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Design a Churn Model: Handle Missing Data and Justify

AmazonData Scientist · Technical Screen
MediumData ScientistTechnical ScreenMachine Learning

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This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Design a Churn Model: Handle Missing Data and Justify states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

Designing a churn-prediction model for a subscription product with messy real-world data.

Question

How would you handle missing values in the training data and justify your approach?

Given this churn-prediction problem, which ML algorithm would you choose and why?

Explain how Random Forest works, including voting, feature bagging, and depth control.

Define overfitting vs. underfitting and describe techniques to detect and mitigate each.

Hints

Discuss imputation, ensemble strengths, cross-validation, regularisation, bias-variance trade-off.