Assessment of Machine Learning-Based-Predictive-Model on Academic Performance Classification among Computer Science Students in Tai Solarin University of Education Ogun State Nigeria.

Abstract Accurately predicting students' academic performance is essential for improved educational outcomes and individualized learning. While helpful, traditional assessment techniques frequently overlook the complex variables affecting performance, such as engagement metrics and socioeconomic background. This study explores the development of a predictive model using an ensemble of machine learning algorithms to classify students' academic performance in higher institutions. By leveraging data collected from Department of Computer Science, Tai Solarin University of Education records, relevant features were selected using the mutual information method. The model was formulated and simulated using two machine learning algorithms such as Naïve Bayes (NB) and Decision Trees (DT) in the Google CoLaboratory environment. The model’s predictive accuracy was evaluated based on key performance metrics, including accuracy, precision, and F-measure. This study demonstrates the effectiveness of machine learning techniques in identifying a student’s performance early with NB having 100% accuracy, allowing for timely interventions and improved resource allocation. Moreover, it contributes to evidence-based decision-making in educational institutions, helping to optimize learning experiences and boost student retention rates. Keyword: Academic performance, Classification, machine learning, Naïve Bayes, Decision Trees