I developed a data processing system using Angular for the frontend and FastAPI for the backend. The system was designed to analyze socioeconomic factors and predict the best products from supplier catalogs. These catalogs were updated automatically via a scheduler, which constantly checked for new products and trained the model to calculate probabilities based on the latest data.

Frontend with Angular: Created an intuitive, user-friendly interface for viewing product recommendations and analyzing socioeconomic data.

Backend with FastAPI: Developed a scalable API to process large datasets and calculate probabilities for product recommendations based on socioeconomic factors.

Automated Catalog Updates: Implemented a scheduler to monitor and update supplier catalogs in real-time, ensuring that the product data used for recommendations was always current.

Product Training & Probability Calculation: Developed a system to train new products and compute their probabilities for being ideal choices based on various factors.

Data-Driven Insights: Provided detailed insights and predictions that helped users make informed decisions about which products would best suit their target demographics.

This project enabled the automatic and dynamic recommendation of products, increasing the efficiency of decision-making by continuously analyzing and adapting to new data, ultimately improving the relevance of product offerings.