Nahom Agize

Enthusiastic Computer Science student with a 4.0 GPA, hands-on IT support experience, and strong programming skills in C, Python, and SQL. Recognized for problem-solving, clear communication, and delivering efficient technical solutions.

AI-Powered Recommendation System (Python, Pandas, Flask, Hash Maps)

• Engineered an Amazon-style product recommendation engine integrating content-based and collaborative filtering techniques.
• Designed efficient data pipelines to ingest, clean, and normalize large e-commerce datasets (CSV/JSON), with optimized in-memory storage using hash maps for product and user lookups.
• Implemented content-based filtering to recommend products by analyzing features such as category, tags, and price similarity.
• Built collaborative filtering algorithms leveraging user-purchase overlap and Jaccard similarity, enhancing personalization by modeling user-to-user relationships.
• Developed an interactive search + filter interface, and extended functionality into a Flask API for real-world integration and scalable deployment.
• Produced detailed technical documentation and modular codebase, demonstrating software engineering best practices (docstrings, modularity, version control).