PREDICTING ACADEMIC SUCCESS: A MACHINE LEARNING APPROACH USING DECISION TABLES AND RANDOM FORESTS ALGORITHMS

Abstract

Machine Learning (ML) in educational data prediction refers to the use of AI-driven algorithms to analyses academic data (e.g., grades, attendance, engagement) and forecast student performance, identify at-risk learners, and recommend interventions. By processing historical and real-time data, machine learning (ML) models uncover hidden patterns that enable educators to optimize their teaching strategies and enhance learning outcomes. This research cones with data collected from ‘UCI Machine Learning Repository’ and the database has total of 33 attributes along 395 rows. The two classification classifiers used in this paper were Decision Table (DT") and Random Forest (RF). The best first search algorithm has been used as a preprocessing step with both classifier models. The distribution of these models is based on the analysis of the Mean Square Root Error between the predicted and actual values. The proposed decision table yields a better result as compared to the random forest algorithm with the blast 1.92 root mean squared error.