Start by clarifying the business context and data schema, then frame the problem as a binary classification task with a clear time window. Outline an end-to-end pipeline from data preparation to model evaluation, emphasizing feature engineering and handling class imbalance.
Pro tip: Define churn precisely with the business team—e.g., no rides in 14 days—and align the prediction window with when interventions can actually be made. Use time-based validation to avoid leakage and ensure the model generalizes to future periods.
Confirm the definition of churn, prediction horizon, and available data fields. Understand how the model will be used to inform intervention strategies.
Create features from historical ride data, user demographics, and engagement metrics. Consider temporal patterns, frequency, recency, and monetary values.
Choose appropriate algorithms (e.g., logistic regression, gradient boosting) and handle class imbalance. Use time-based cross-validation to simulate real-world deployment.
Select metrics like AUC-ROC, precision-recall, or lift that align with business goals. Evaluate model performance on a holdout set and interpret results.
Discuss how to integrate the model into production, monitor performance over time, and retrain as needed to maintain accuracy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.