Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sourav, Mayur Kumar; Gupta, Varuna Title A name given to the resource Comparative analysis of machine learning algorithms for predicting student success and enhancing their educational outcomes Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived AIP Conference Proceedings;Volume;3297;Issue;1;Article No.;286502; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0286502" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0286502</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022837333?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022837333?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sourav M.K., Christ University, Bangalore, India; Gupta V., Christ University, Bangalore, India Description An account of the resource The primary objective of this study is to predict the performance of students and evaluate the efficacy of various machine learning algorithms in predicting student success based on their marks and grades (academic factors). Through a comprehensive review of literature and experimentation, this research compares the performance of different machine learning models, including but not limited to decision trees, random forests, support vector machines, logistic regression, and neural networks. The evaluation metrics considered in this comparative analysis include accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Fourteen experiments have been performed and preliminary results suggest that performances of students on the basis of academic factors might be predictable and by understanding the strengths and weaknesses of student's educational outcomes and foster student achievement can be improved. Through extensive experimentation and comparative analysis, XGBoost(ExtremeGradient Boosting) and AdaBoost demonstrated as the most effective predictive models to analyze the students' performance. 2025 Author(s). Publisher An entity responsible for making the resource available American Institute of Physics Relation A related resource ISSN: 0094243X; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online