Data Analysis and Prediction of Student Academic Performance

Introduction: Predicting student performance across datasets with varying distributions remains a complex challenge in educational analytics. This study presents a novel approach to address this issue by utilizing transfer learning techniques to improve prediction accuracy. Objective: The research leverages a comprehensive dataset from Kaggle, encompassing demographic details, social factors, and academic performance indicators, to uncover significant patterns and relationships that influence student outcomes. By analyzing these factors, the study provides valuable insights that enable students to assess their academic progress, refine their learning strategies, and enhance overall efficiency.Method: The proposed methodology not only improves predictive accuracy but also bridges existing gaps in understanding student performance across diverse educational contexts. Result: These findings can be applied to develop personalized support systems, empowering students with actionable recommendations tailored to their individual needs.Conclusion: By addressing these challenges, the study contributes to a deeper understanding of student performance dynamics and highlights the potential of advanced predictive techniques to drive meaningful educational interventions.