PREDICTING STUDENTS PERFORMANCE USING PERSONALIZED ANALYTICS

Abstract: Predicting academic performance is an important task for the students in university, college, and school, etc. The factors, which affect the student’s academic performance, are class quizzes, assignments, lab exams, mid, and final exams. The student’s academic performance should be informed to the class teacher in advance that will decrease the student’s dropout and increase the performance. In this paper, machine learning classification algorithms such as decision tree, Support Vector Machine (SVM), and Naive Bayes are implemented to predict the student’s academic performance. The performance of an algorithm has been evaluated based on confusion matrix, accuracy, precision, recall, and F1 score. The obtained result shows that the Naive Bayes classification algorithm performs better Record Terms – Prediction using SVM, Machine Learning.