Accuracy comparison of machine learning algorithms for predictive analytics in higher education

Brohi, Sarfraz Nawaz; Pillai, Thulasyammal Ramiah; Kaur, Sukhminder; Kaur, Harsimren; Sukumaran, Sanath; Asirvatham, David

Abstract

In this research, we compared the accuracy of machine learning algorithms that could be used for predictive analytics in higher education. The proposed experiment is based on a combination of classic machine learning algorithms such as Naive Bayes and Random Forest with various ensemble methods such as Stochastic, Linear Discriminant Analysis (LDA), Tree model (C5.0), Bagged CART (treebag) and K Nearest Neighbors (KNN). We applied traditional classification methods to classify the students’ performance and to determine the independent variables that offer the highest accuracy. Our results depict that the data with the 11 features using random forest generated the best accuracy value of 0.7333. However, we revised the experiment with ensemble algorithms to reduce the variance (bagging), bias (boosting) and to improve the prediction accuracy (stacking). Consequently, the bagging random forest outperformed other methods with the accuracy value of 0.7959.

• Presentation Conference Type: Conference Paper (published)
• Acceptance Date: Jul 1, 2019
• Online Publication Date: Jul 14, 2019
• Publication Date: Jan 1, 2019
• Deposit Date: Sep 9, 2022
• Journal: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
• Print ISSN: 1867-8211
• Electronic ISSN: 1867-822X
• Publisher: Springer Verlag (Germany)
• Volume: 285
• Pages: 254-261
• Series ISSN: 1867-822X
• DOI: https://doi.org/10.1007/978-3-030-23943-5_19
• Keywords: Predictive analytics, Machine learning, Higher education
• Public URL: https://uwe-repository.worktribe.com/output/9941066
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-030-23943-5_19
• Related Public URLs: https://www.springer.com/series/8197