Educational Data Classification using Different Classifiers for Real Time Student Applications

The application of data mining techniques to educational datasets is gaining increasing attention due to the growing availability of student-related information. However, organizing and interpreting this data effectively poses a significant challenge because of its high dimensional and complexity. This study explores the use of the Linear Support Vector Classifier (Linear - SVC), SVM, and naive bias known for its computational efficiency and robustness, in categorizing educational data. The model's output can reveal actionable insights into student performance, offering valuable support for real-time academic assessments. Additionally, it holds potential for informing future strategies related to student admissions and selection processes in higher education institutions.