Leveraging Data Science for Enhanced Academic Outcomes: Insights from the Student Performance Dataset

The present work applies techniques of data science to the "Student Performance Dataset" in finding factors that could predict academic achievement of secondary school students in Portugal. Based on demographics, study habits, parental education, and schools, this research searches for the performance trend and patterns of students. Specific techniques applied for this study were EDA and correlation analysis, predictive modeling of results, therefore, giving actionable insight into how individualized learning strategies could best be improved upon. It is noted that the study time, absenteeism, parents' education, and final grades are all highly correlated. Further, different predictive models on student performance were developed using Linear Regression, Support Vector Regression, and Decision Tree Regression, while the best results in light of R-squared and Mean Squared Error metrics came out for Linear Regression. The hidden message from this study is that interventions should be tailored in education, with data-driven approaches being the need of the hour to increase academic performance. Dealing with outliers, optimization of feature selection, and using a robust preprocessing technique are in tune with the broader objective of equitable and effective educational systems. These findings have practical implications for policymakers, educators, and institutions seeking to implement evidence-based strategies for academic excellence.