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 Christina, Sowmya; Sowjanya, S.; Lakshmhyma, Ch.; Prathiba, L.; Basha, Md Shaik Amzad Title A name given to the resource Data-Driven Insights into Student Performance: Benchmarking Machine Learning Models for Grade Prediction using Regression and Classification Approaches 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 International Conference on Intelligent Systems and Computational Networks, ICISCN 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICISCN64258.2025.10934398" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICISCN64258.2025.10934398</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002698034?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002698034?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 Christina S., Christ (Deemed to be University), Department of Professional Studies, Bengaluru, India; Sowjanya S., Rajeev Gandhi Memorial College of Engineering, Department of Management Studies, Nandyal, India; Lakshmhyma Ch., Maris Stella College (Autonomous), Department of Business Administration, Vijayawada, India; Prathiba L., Ashoka Women's Engineering College (Autonomous), Kurnool, India; Basha M.S.A., Gitam (Deemed to be University), Gitam School of Business, Hyderabad, India Description An account of the resource This research explores the effectiveness of 17 machine learning models in predicting student performance across Mathematics and Portuguese datasets. The primary goal of this study was to evaluate and compare regression and classification models to identify the most accurate predictors of student grades. A range of algorithms was tested, including linear models (Linear Regression, Elastic Net, Ridge, Lasso), tree-based models (Random Forest, Gradient Boosting, CatBoost, LightGBM), and advanced techniques (Neural Networks, SVM, XGBoost, Naive Bayes, SVR). The methodology involved data preprocessing, feature engineering, and splitting data into training and test sets. Base models were implemented, followed by hyperparameter tuning to optimize performance. Metrics like RMSE, MAE, MSE, R2 (for regression), and accuracy, precision, recall, F1 score (for classification) were used to assess performance. The study found that Gradient Boosting and Elastic Net consistently outperformed other models in regression tasks, achieving the highest R2 scores. For classification, Logistic Regression proved to be the most accurate, followed by Naive Bayes. These findings provide valuable insights for model selection in educational performance prediction, establishing Gradient Boosting and Logistic Regression as benchmark models. 2025 IEEE. Subject The topic of the resource classification; machine learning; regressions models; student performance Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152924-6; 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