14.6.3: Hands-On Lab Questions
- Page ID
- 58361
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Hands-On Lab Exercise
Lab: Exploring Advanced ML and Future Trends
Scenario:
InsureWell is an insurance company with 50,000 policy records: age, income, region, coverage_type, claim_history, premium, policy_tenure, churned (0/1). They want to predict customer churn and explain predictions to regulators.
Instructions:
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Ensemble Models (25 min): Train three models: (a) Single Decision Tree, (b) Random Forest (100 trees), (c) Gradient Boosting (XGBoost or sklearn). Compare accuracy, precision, recall, and F1 on a test set. Which ensemble method performs best?
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AutoML Exploration (15 min): Using H2O AutoML (or describe the process): run AutoML with max_models=10. Compare AutoML's best model to your manually trained models. Was AutoML's result better?
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Explainability (20 min): For the best model, apply SHAP: generate a summary plot showing feature importances. For one specific customer, create a force plot. Write a plain-English explanation suitable for a regulator.
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Future Trends Discussion (15 min): Assess feasibility of: (a) real-time churn prediction via edge analytics on mobile app usage, (b) federated learning across partner companies without sharing data, (c) data mesh across actuarial, claims, and marketing departments.
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Career Reflection (15 min): Based on chapter trends, write a personal development plan: what 3 skills should an aspiring data analyst invest in over the next 2 years?
Deliverables:
Model comparison table, AutoML results, SHAP visualizations + regulator explanation, future trends assessment, personal development plan.