The Exploration of Modelling for The Student Achievement Predictor

Abstract

The project is rooted in the analysis of historical student data. It encompasses a comprehensive approach involving data observation, meticulous analysis, thorough comparison, systematic processing, and efficient coding techniques. The ultimate goal is to harness the power of machine learning to predict students' academic achievements and discern the key features that exert the most significant influence on their learning outcomes. In terms of the machine learning model, this study explored and assessed several machine learning algorithms, including but not limited to the K-Neighbors Classifier, Logistic Regression, and Decision Tree Classifier. These models are scrutinized and fine-tuned to ensure their suitability for the task at hand. Furthermore, a pivotal aspect of this project is identifying the features that wield the greatest impact on students' learning achievements. By employing feature selection techniques, this study aims to uncover the critical factors that can make a difference in educational outcomes. This information can guide educational institutions in designing targeted interventions to support student success. The experimental results obtained from this study demonstrated the effectiveness of the employed machine learning methods.