A MACHINE LEARNING BASED JOB MATCHING AND SKILL GAP DETECTION SYSTEM

This research proposes an innovative approach to assist individuals in aligning their skill sets with the currentlabor market demands by utilizing machine learning techniques for job forecasting and skill gap evaluation. Thesystem leverages classification algorithms such as Random Forest, Naive Bayes, and Support Vector Machine(SVM) to predict suitable job roles based on the user's existing skill set. Additionally, specialized modelsanalyze skill gaps and suggest targeted recommendations for additional skills required to secure high-demandjob opportunities. The system evaluates model performance using key metrics such as accuracy, precision,recall, and F1 score, ensuring reliable predictions and robust analysis. Through interactive visualizations, userscan understand their skill gaps and make informed decisions to enhance their career trajectories. The platformnot only predicts potential job matches but also provides a comprehensive skill development pathway,empowering users to reduce the gap between their current capabilities and market expectations. This system isdesigned to increase employability by equipping users with relevant insights, enabling continuous learning andcareer advancement. The initiative ultimately serves as a bridge between individual aspirations and industryneeds, fostering a more adaptive and skilled workforce