ML Engineer (personal project)
Loan Risk Prediction ML Pipeline (LightGBM + FLAML + MLflow)
End-to-end loan risk prediction pipeline: data ingestion, preprocessing, LightGBM training, FLAML hyperparameter tuning, MLflow tracking, Docker, and CI/CD retraining via GitHub Actions.
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83.78% accuracy with automated drift retraining; served as a REST API via MLflow + Docker.
Loan Risk Prediction ML Pipeline
83.78% accuracy on loan default risk, with automated drift retraining. An end-to-end pipeline that predicts loan application risk and covers the full workflow from data ingestion to a served, versioned model.
The problem
Loan default risk depends on many signals spread across separate datasets (loan details, payment history, and underwriting variables). A one-off notebook model goes stale as data drifts and is hard to serve or reproduce.
What I built
- Data pipeline: ingestion from multiple CSV sources, schema validation, missing-value handling, correlation pruning, and an 80/20 split.
- Modeling: LightGBM with hyperparameters tuned automatically by FLAML AutoML, evaluated on accuracy, precision, and recall with feature-importance analysis.
- Experiment tracking: MLflow logs model versions, hyperparameters, and metrics for reproducibility.
- Serving: the model is containerized with Docker and served as a REST API through the MLflow registry.
- Automated retraining: GitHub Actions retrains on fresh data monthly (with FLAML re-tuning) to handle drift, then rebuilds and pushes the container.
- Dashboard: a Streamlit app to visualize predictions and key metrics.
Stack
Python, LightGBM, FLAML AutoML, MLflow, Docker, GitHub Actions, Streamlit, with deploy options for AWS SageMaker, Databricks, Google Cloud, or Render.