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 Conference Papers 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/. Title A name given to the resource Advancing Predictive Analytics in E-Learning Platform: The Dominance of Blended Models in Enrollment Forecasts Subject The topic of the resource Blend Models; E-Learning Platform; IT Software Courses; Regression Models Description An account of the resource The rapid expansion of e-learning platforms has revolutionized the landscape of education, particularly highlighting the significance of online courses in contemporary learning environments. This research focuses on Udemy, a prominent online learning platform, and aims to enhance the predictability of course enrollments within its IT &amp; Software category. The study's central purpose is to leverage advanced machine learning techniques to predict course subscriber numbers, a crucial indicator of a course's popularity and success. Employing an extensive dataset from (Kaggle DB)Udemy, encompassing various course attributes such as ratings, reviews, and pricing, the study explores multiple machine learning models. These include Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and K-Nearest Neighbors Regression. A key innovation of this research is the application of ensemble methods, particularly a blended model approach, to integrate predictions from multiple models, thereby enhancing accuracy and reliability. The findings of this study are significant. The ensemble approach, notably the blended model, outperforms individual predictive models in accuracy. Among the single models, Gradient Boosting Regression shows the highest effectiveness in forecasting enrollments. The research highlights the vital role of course characteristics, including ratings and reviews, in determining course popularity. This study contributes to the field of e-learning by introducing a novel, data-driven approach to predict course enrollments. It offers valuable insights for educators, course creators, and platform developers, emphasizing the potential of machine learning in optimizing content strategy and marketing efforts in the digital education domain. The application of ensemble machine learning methods presents a new horizon in educational analytics, paving the way for more nuanced and effective strategies in online education delivery and promotion. 2024 IEEE. Creator An entity primarily responsible for making the resource Sumalatha K.N.; Anupama N.; Sasikumar P.; Sucharitha M.M.; Basha M.S.A. Source A related resource from which the described resource is derived 2024 2nd World Conference on Communication and Computing, WCONF 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/WCONF61366.2024.10692137" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/WCONF61366.2024.10692137</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207431046&amp;doi=10.1109%2FWCONF61366.2024.10692137&amp;partnerID=40&amp;md5=54203106c9d4812ff9b40f45656d9fab" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207431046&amp;doi=10.1109%2fWCONF61366.2024.10692137&amp;partnerID=40&amp;md5=54203106c9d4812ff9b40f45656d9fab</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835039532-7 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sumalatha K.N., PG Department of Business Administration, Maris Stella College (Autonomous), Vijayawada, India; Anupama N., PG Department of Business Administration, Maris Stella College (Autonomous), Vijayawada, India; Sasikumar P., Department of Sciences and Humanities Christ (Deemed to be University), Bengaluru, India; Sucharitha M.M., Department of Professional Studies, Christ (Deemed to be University), Bengaluru, India; Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to be University), Bengaluru, India