ProjectMatch — AI-Powered Project Recommendation System

Students entering technical field often struggle to identify project's that align with their current skill sets and areas of interest. ProjectMatch is a web application that automates this discovery process using a content-based machine learning recommendation engine. Users input their known skills and preferred domains through an interactive tag-based interface, and the system computes the relevance of each project in its 40-item dataset using TF-IDF vectorization combined with cosine similarity scoring.

TF-IDF rewards rare and domain-specific skills as stronger match signals, while cosine similarity quantifies the directional alignment between a student's profile vector and each project's feature vector. Results are filterable by difficulty level — Beginner, Intermediate, or Advanced — and are ranked by a displayed match percentage, ensuring transparency in the recommendation logic. The backend is built with Flask and optionally integrates with a MySQL database to persist student profiles, recommendation history, and star-rating feedback. The system lays the groundwork for future enhancements including collaborative filtering from collected ratings, semantic skill matching via word embeddings, and a user-submitted project contribution workflow.