A Data-Driven Framework for Holistic Student Performance Evaluation and Industry Readiness Using Machine Learning

The growing disparity between higher education outcomes and rapidly evolving industry requirements highlights the urgent need for an integrated, data-driven evaluation approach. Traditional assessment models focus primarily on academic performance, neglecting critical employability skills such as communication, adaptability, and problem-solving. This study introduces a novel multi-tier evaluation framework leveraging machine learning (ML) techniques to holistically assess student performance and predict industry readiness. Using a decade-long dataset (2015–2025) comprising 750 undergraduate students from Tamil Nadu, India, the proposed system integrates cognitive, behavioural, and technical skill indicators. Decision tree classifiers, particularly the J48 model, are utilized for predictive modelling, supported by clustering algorithms for deeper analysis. The model achieved a notable accuracy of 86%, significantly outperforming traditional evaluation methods. The findings demonstrate how AI-driven predictive analytics can bridge the gap between academia and industry by enabling timely interventions and personalized development plans. This research contributes a scalable, explainable, and context-sensitive solution for workforce preparedness in the Indian higher education landscape.