AI for Social Good: Identifying and Bridging Freelance Skill Gaps in Underprivileged Communities Using Machine Learning

This research proposes a machine learning–powered system for identifying and bridging digital skill gaps in underprivileged learner personas using public freelance job market data. The solution evaluates skill-job fit through Jaccard similarity and recommends targeted learning paths to improve freelance readiness. The frontend, developed in Streamlit, visualizes metrics such as fit score, skill coverage, and time-to-learn, offering an interactive and measurable framework for digital inclusion. The model shows clear improvements in simulated learner profiles, demonstrating scalable potential for skill development and economic upliftment.