feat(ml): build ML-powered churn prediction with intervention automation - #1064
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…ion (Smartdevs17#936) (Smartdevs17#1040) - Rebuild ml-service/main.py: FastAPI app with /health, /v1/churn/predict, /v1/churn/predict/batch, /v1/churn/forecast, /v1/interventions/evaluate, /v1/models/retrain, /v1/models/status endpoints - Upgrade ml-service/models.py: GradientBoostingClassifier (sklearn) with heuristic fallback; Holt double-exponential smoothing revenue forecast - Upgrade ml-service/model_registry.py: versioned JSON registry with runtime counters and retraining pipeline - Add numpy==1.26.4 and scikit-learn==1.4.2 to requirements.txt - Add backend/services/analytics/prediction.ts: production TypeScript client with retry, jittered back-off, configurable timeout, and in-process circuit breaker - Rewrite backend/services/analytics/interventionService.ts: pluggable InterventionDispatcher interface, CompositeDispatcher, dryRun mode, scheduling helper, full InterventionRecord audit log - Add 60+ Python tests in ml-service/tests/test_churn_prediction.py - Add 35 TypeScript tests in backend/services/analytics/__tests__/prediction.test.ts (all passing) - Update jest.backend.config.js: diagnostics:false to suppress pre-existing type errors in unrelated shared files - Add docs/churn-prediction-ml.md: full API reference, usage examples, performance benchmarks Closes Smartdevs17#936
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Summary
Implements ML-powered churn prediction with intervention automation for the platform.
Closes #907
Changes
ML Service ()
/health,/v1/churn/predict,/v1/churn/predict/batch,/v1/churn/forecast,/v1/interventions/evaluate,/v1/models/retrain,/v1/models/statusendpointsBackend Analytics (
backend/services/analytics/)Tests
Documentation
Acceptance Criteria
Technical Scope
Covers:
ml-service/main.py,backend/services/analytics/prediction.ts