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Coffee Recommendation System
Customers stuck to the same drinks; new menu items were risky and often wasted.
Challenge
The shop needed a way to recommend new drinks to customers and predict product adoption to reduce waste and increase trials.
Solution
Created an ML-powered recommendation engine (collaborative filtering + flavor vectors) with a REST API and Jinja2-powered UI to serve personalized suggestions and adoption predictions.
Tech Stack
Python • Flask • scikit-learn • HTML/CSS/JS • Jinja2
Impact
35 percent more customers trying new drinks • 22 percent higher repeat purchases • Reduced waste
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