Predict Customer Churn with Machine Learning Workflow

Q: Predict Customer Churn with Machine Learning Workflow

This question evaluates a data scientist's competency in end-to-end supervised machine learning workflows—covering feature engineering, model evaluation, handling class imbalance, and deployment—within the Machine Learning domain.

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Question

Predicting Monthly Churn: End-to-End Workflow

Scenario

A subscription platform wants to predict whether a customer will churn in the next month.

Assumption (for clarity): Define churn (y = 1) as a subscriber whose plan is not active by the end of the next 30 days (e.g., cancels or fails to renew). Features used to predict churn at time T must only use data available up to T.

Questions

Outline the end-to-end workflow—from feature engineering through model deployment—to build a churn predictor.
Which evaluation metrics would you prioritize and why?
How would you handle severe class imbalance during training?

Predict Customer Churn with Machine Learning Workflow

Predicting Monthly Churn: End-to-End Workflow

Scenario

Questions

Solution

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