Authors: Chaitanya Madhav Mate, Sanil Nivrutti Shinde, Aakansha Ganesh Tambe, Niranjan Deepak Lahane, Kirti Patil
Abstract: This paper describes a "Dynamic Job Market Analysis Platform" that allows capturing real-time analytics and prediction of the employment fluctuations with the goal of benefiting universities students and employer. By utilizing machine learning models to forecast trends accurately, the platform fills this gap between student skill sets and the leading demands of the industry. It provides students with actionable insights to help them ensure their career paths are in line with market requirements, and it helps employers better understand trends in the workforce. The results highlight an accuracy of over 97% in prediction of the employment patterns, showcasing the ability of the platform to fuel data-based decision making. This project helps to improve employability and provides a better alignment between academia and market requirements by addressing issues, such as data imbalance and dynamic changes in the market. Future directions include expanding the scope of real-time data integration and refining prediction models for broader applicability.
DOI: https://doi.org/10.5281/zenodo.17985571
