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Emotional intelligence (EI) has been increasingly recognized as a critical factor influencing academic success among college students. This study investigates the impact of EI components—self-awareness, self regulation, motivation, empathy, and social skills—on academic achievements using the XGBoost algorithm, a powerful machine learning technique known for its high predictive accuracy. The primary objective of this work is to determine the relative importance of EI traits in predicting
academic performance and to develop a robust model that can identify key emotional intelligence factors contributing to student success. A dataset comprising EI assessments and academic records of college students is analyzed, with XGBoost employed for feature importance analysis and predictive modeling. The findings reveal which EI dimensions most significantly influence academic outcomes, providing actionable insights for educators and policymakers to enhance student support programs. This research contributes to the growing body of literature on EI in education by leveraging advanced machine learning to quantify its impact, offering a data-driven approach to improving academic interventions.
Keywords – Emotional Intelligence, Academic Achievement, XGBoost, Machine Learning, Predictive Modeling, Student Performance