Overview
Engineered a production-grade machine learning deployment pipeline predicting California housing prices using linear regression, complete with automated CI/CD infrastructure. Built a professional Flask web interface with a custom dark-themed design, containerized the application with Docker for environment portability, and wired up GitHub Actions to automatically build, containerize, and deploy to Heroku on every push to main — zero manual steps required.
GitHub: saideepa05/housing_deployment
What I Built
- Flask web application — responsive dark-themed UI with a
/predictREST endpoint, strict numeric input validation, and explicit feature mapping to the trained model - Linear regression model — retrained with scikit-learn to resolve version compatibility issues that would break inference in production
- Docker containerization — full Dockerfile packaging the app, model, and dependencies for consistent behaviour across development and production environments
- GitHub Actions CI/CD pipeline — automated workflow triggered on push to main: builds the container, authenticates with Heroku CLI, and deploys with zero-downtime releases
- Infrastructure debugging — resolved scikit-learn version mismatches and Heroku CLI ENOENT spawn errors that blocked the initial pipeline, resulting in a fully reliable deploy chain
Tech Stack
Python · Flask · scikit-learn · Pandas · NumPy · Docker · GitHub Actions · Heroku · HTML/CSS
Results
Delivered a fully automated deployment pipeline with zero manual intervention — a push to main triggers containerization and production release end-to-end. Resolved critical infrastructure blockers at every layer (model versioning, container builds, Heroku CLI integration) and implemented robust input validation, resulting in a production-ready application serving real-time housing price predictions.