Advanced Algorithms and Implementations for Talent Matching and Team Formation in Crowdsourced Engineering

This project addresses the significant problem of high failure rates (approximately 15.7%) in crowdsourced engineering and hackathon environments, which are primarily caused by static, manual team formation processes that result in poorly matched skills and ineffective collaboration. We propose TalentBridge, an automated platform that optimizes talent matching and team formation by leveraging real-time data and a Hybrid Machine Learning (ML) pipeline. The methodology involves: API integration with platforms like GitHub and LinkedIn to fetch live data; KeyBERT (NLP) for contextualized skill extraction from profiles; and K-Means Clustering for grouping participants into well-balanced teams with complementary skill sets. The implemented system reduces manual effort and errors, enhances team balance and fairness, and is expected to lead to improved match quality and higher success rates for projects. This scalable, data-driven coordination mechanism is a unique and necessary solution for fostering innovation in dynamic crowdsourced environments. ·