Build ML-powered churn prediction with intervention automation

[Smartdevs17]

Context

Implement build ml-powered churn prediction with intervention automation for the platform.

Current Limitation

This functionality is currently missing or needs improvement.

Expected Outcome

• Production-ready implementation
• Comprehensive test coverage
• Documentation and examples
• Performance benchmarks where applicable

Acceptance Criteria

• Feature implemented with full functionality
• Unit tests added with >80% coverage
• Integration tests for critical paths
• No regression introduced
• Documentation updated
• Code review approved
• Performance benchmarks met

Technical Scope

ml-service/main.py, backend/services/analytics/prediction.ts

Labels

• drips-wave
• high
• 200-points

[Buluz244]

@Buluz244 has applied to work on this issue as part of the Stellar Wave Program's 8th wave.

I would like to work on this issue

ℹ️ Repo Maintainers: To accept this application, review their application or assign @Buluz244 to this issue.

[HEMOGEE]

@HEMOGEE has applied to work on this issue as part of the Stellar Wave Program's 8th wave.

I would like to work on this issue

ℹ️ Repo Maintainers: To accept this application, review their application or assign @HEMOGEE to this issue.

[retkatmun]

@retkatmun has applied to work on this issue as part of the Stellar Wave Program's 8th wave.

Hi Maintainer,

I’m a smart contract and full-stack developer and would love to contribute to this issue. I’m an experienced contributor with hands-on expertise in Rust, Soroban, Cairo, and TypeScript.

I’m passionate about building secure, scalable blockchain solutions, with a strong emphasis on writing clean and maintainable code. Based on the scope of this issue, I’m confident I can deliver a complete solution within 24 hours of assignment.

I’d greatly appreciate the opportunity to contribute and help move this forward.

ℹ️ Repo Maintainers: To accept this application, review their application or assign @retkatmun to this issue.

[Neziahtech]

@Neziahtech has applied to work on this issue as part of the Stellar Wave Program's 8th wave.

Please let me work on this issue; I can handle it.

ℹ️ Repo Maintainers: To accept this application, review their application or assign @Neziahtech to this issue.

[drips-wave]

Congratulations, @retkatmun! 🎉 Your application was accepted by the repo's maintainers, and the issue is due on August 31, 2026.

🧑‍💻 @retkatmun: Please resolve the issue such that the repo's maintainers have enough time to review your contribution before the due date. You'll earn Points for completing the issue on-time, which will make you eligible for a share of the Stellar Wave Program's reward pool.

Warning

When opening a PR, please link it to this issue to ensure it gets tracked accurately. Points are awarded when this issue is marked as completed by the maintainer.

🤠 Repo maintainers: Please keep an eye on the contributor's progress and review their work before the due date. You can manage this issue, including adjusting its complexity and points, here.

🌊 Happy Wave 🌊

[retkatmun]

Implementation complete 🎉

PR #1040 has been raised: #1040

Deliverables

Python ML Service

• Rebuilt ml-service/main.py — 7 production endpoints (/health, /v1/churn/predict, /v1/churn/predict/batch, /v1/churn/forecast, /v1/interventions/evaluate, /v1/models/retrain, /v1/models/status)
• Upgraded models.py — calibrated GradientBoostingClassifier (sklearn) + heuristic fallback; Holt double-exponential smoothing forecast with 95% confidence intervals
• Upgraded model_registry.py — versioned JSON registry, runtime counters, safe retraining pipeline
• Added ML dependencies to requirements.txt (numpy, scikit-learn)

TypeScript Backend

• New backend/services/analytics/prediction.ts — retry (3×, jittered back-off), 10s timeout, in-process circuit breaker, full camelCase ↔ snake_case mapping
• Rewritten backend/services/analytics/interventionService.ts — pluggable dispatcher interface, CompositeDispatcher, dryRun mode, scheduling helper, full audit log

Tests

• 60+ Python tests covering all endpoints, models, registry, and feature client
• 35/35 TypeScript tests passing (retry, circuit breaker, dryRun, dispatchers, end-to-end)
• No regressions — 55 existing backend tests still pass

Docs

• docs/churn-prediction-ml.md — full API reference, TypeScript usage examples, performance benchmarks