Build a SaaS churn prediction model
WHAT IT TESTS: end-to-end supervised modeling with a clear label. OUTLINE: define churn and the prediction window, engineer usage-trend and tenure features, try logistic regression then gradient-boosted trees, and evaluate on class-imbalanced metrics.
WHAT IT TESTS: whether you can frame churn as a supervised problem with a precise label, build informative features, pick suitable models, and evaluate honestly under class imbalance. ANSWER OUTLINE: define churn and the prediction horizon, engineer features from usage trends, recency-frequency, tenure, and support and billing signals, baseline with logistic regression then move to gradient-boosted trees, and evaluate with AUC, precision-recall, and a calibrated probability, watching for leakage.
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- #churn-prediction
- #machine-learning
- #feature-engineering
- #model-evaluation
- #class-imbalance
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