AI-Driven Employability Prediction: Integrating Machine Learning and Educational Analytics for Student Placements

Abstract

This paper presents an Artificial Intelligence (AI) and Machine Learning (ML)–based framework for predicting student employability outcomes in higher education. The study aims to bridge the gap between academic learning and industry requirements by analyzing multiple features such as academic performance, technical skills, internships, and leadership experience. Using supervised learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), and XGBoost (XGB), the system forecasts suitable job roles and potential recruiting companies. Among these models, XGBoost achieved the highest performance, with an accuracy of 87.3% and an F1-score of 86.2%. The results demonstrate that combining technical proficiency with behavioral and academic features provides superior predictive capability. The proposed framework contributes to educational data analytics by enabling data-driven career guidance, curriculum design, and employability enhancement. This approach can assist institutions in aligning student competencies with evolving market needs, fostering better preparedness and placement efficiency in business and engineering education.