Building a Recommendation Engine with Scikit-learn & FastAPI for Your Portfolio

#api #career #machinelearning #python

Want your portfolio to stand out in today's competitive tech hiring market?

A recommendation engine is more than just another machine learning project—it's an opportunity to demonstrate end-to-end development skills, from data preprocessing and model building to API deployment with FastAPI.

In this guide, you'll learn how to:
✅ Build an item-based recommendation engine using Scikit-learn
✅ Serve predictions with a high-performance FastAPI REST API
✅ Follow production-ready practices with model persistence and error handling
✅ Create a portfolio project that showcases both Machine Learning and MLOps capabilities

If you're aiming for Data Science, Machine Learning Engineer, or Backend AI roles, this project is a practical way to demonstrate real-world engineering skills—not just model accuracy.

💡 Save this post for your next portfolio project and share it with someone preparing for tech interviews.