Student Performance Assessment Analysis

This research paper explores student performance assessment by integrating data analysis, visualization, and machine learning techniques.Leveraging a dataset encompassing various student attributes, including demographics, parental education levels, and study habits, we conducted comprehensive analyses to unveil the intricate relationships between these factors and academic outcomes.Subsequently, we applied 20 machine learning algorithms, ranging from linear regression to decision tree regressor, to predict students' future performance.Our comparative analysis not only underscores the efficacy of machine learning in predictive modeling but also elucidates the strengths and limitations of each algorithm in educational contexts.Furthermore, our findings reveal nuanced correlations between different attributes and academic achievements, providing valuable insights for educational practitioners and policymakers.By contributing to the discourse on educational analytics, this study highlights the transformative potential of machine learning in optimizing pedagogical strategies and fostering student success.