Design a Churn Model: Handle Missing Data and Justify

Quick Overview

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.

Design a Churn Model: Handle Missing Data and Justify

Company: Amazon

Role: Data Scientist

Category: Machine Learning

Difficulty: medium

Interview Round: Technical Screen

##### 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.

Quick Answer: 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.

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Aug 4, 2025, 10:55 AM
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Design a Churn Model: Handle Missing Data and Justify

Churn Prediction on Messy Subscription Data

Context

You are building a binary churn-prediction model for a subscription product. Historical customer-level data contains usage/activity, billing/payments, support interactions, demographics, and plan details. The data is messy: many fields have missing values, there is class imbalance (churn is rarer than non-churn), and features are time-dependent. We aim to predict whether a customer will churn in the next period (e.g., next 30 days) using only information available up to a cutoff date.

Assumptions:

  • Binary target: churn = 1 if a customer cancels or fails to renew in the next period; 0 otherwise.
  • Temporal validation is required (train on earlier periods, validate on later periods).
  • Some missingness is likely not at random (e.g., missing usage could reflect inactivity).

Tasks

  1. How would you handle missing values in the training data and justify your approach?
  2. Given this churn-prediction problem, which ML algorithm would you choose and why?
  3. Explain how Random Forest works, including voting, feature bagging, and depth control.
  4. Define overfitting vs. underfitting and describe techniques to detect and mitigate each.

Clarifying Questions to Ask Guidance

  • Clarify the task, data shape, labels, constraints, and evaluation metric.
  • State assumptions behind the math or modeling technique you choose.
  • Connect theory to practical training, debugging, and deployment implications.

What a Strong Answer Covers Guidance

  • Correct definitions and formulas where the prompt requires them.
  • A practical explanation of how the method behaves on real data.
  • Trade-offs, failure modes, diagnostics, and mitigation strategies.
  • Evaluation choices that match the product or modeling objective.

Follow-up Questions Guidance

  • How would noisy labels, class imbalance, or distribution shift affect the answer?
  • What would you monitor after deployment?
  • Which baseline would you compare against first?
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