(untitled) (Journal of Advances and Scholarly Researches in Allied Education, 2024)
If we want to see lasting improvements in our economy, education is a must. In light of this, educational institutions on a global scale are working to enhance the educational system for the benefit of both students and instructors. Educational institutions must ensure that low-performing students are identified early and accurately so that they can provide high-quality education and get appropriate assistance to decrease dropout rates. The purpose of this project is to examine data mining techniques as a means to forecast students’ academic success. The main goal is to provide methods that may enhance the performance of students on the prediction test. Using the best subset of features determined in Phase I, an improved SVM classifier is used to forecast a student’s performance in Phase 2. There are two ways in which the SVM classifier gets improved. The first one eliminates superfluous training support vectors, which improves both the accuracy and the time complexity. To get around knowing the K values before clustering, the K Means clustering technique is tweaked utilising ensemble clustering technology.