Data-Driven Student Performance Analysis: A Machine Learning Approach

The utilization of machine learning (ML) methods has the potential to address the challenges posed by the rapid growth of student-related data, enabling better predictions of student performance and supporting informed managerial decisions. These techniques analyze data through advanced models and algorithms to forecast academic outcomes. This research focuses on identifying key factors that influence student performance using ML approaches. By leveraging statistical and classification algorithms, machine learning enhances the accuracy of predictions. The research explores relevant factors and applies in state-of-art models to achieve precise performance predictions. Various studies have employed ML techniques to predict student success, highlighting its broad applicability. This research proposes a framework for assessing students' academic achievements. The dataset includes information such as demographic details, prior academic records, and family background. Data was sourced from students across multiple universities using online surveys, comprising 24 attributes adapted from prior research. The objective is to identify the critical attributes that significantly affect student performance. It also evaluates distinguish classification techniques to enhance prediction accuracy. Experimental findings reveal that the Support Vector Machine (SVM) outperforms other methods, achieving a maximum accuracy of 62.50%. This research proposed the effective prediction tools can be developed to improve educational outcomes effectively.