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Construct a Churn-Prediction Pipeline Using Scikit-Learn

AppleData Scientist · Technical Screen
MediumData ScientistTechnical ScreenMachine Learning

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This question tests practical machine learning engineering skills, specifically the ability to construct an end-to-end classification pipeline for imbalanced tabular data. It evaluates knowledge of preprocessing, model validation, probability calibration, and production packaging — core competencies assessed in data scientist interviews.

Scenario

Building a churn-prediction pipeline for a subscription business using scikit-learn.

Question

Describe, step-by-step, how you would construct, train, validate, and evaluate a churn-prediction model in scikit-learn, including preprocessing, model choice, hyper-parameter tuning, and packaging the final pipeline for production.

Hints

Mention Pipeline, ColumnTransformer, GridSearchCV, cross-validation, joblib.