Using machine learning for early identification of At-risk students

The use of machine learning (ML) in education offers promising opportunities to enhance teaching strategies and improve student outcomes. Predictive models can identify at-risk students early, enabling timely and targeted interventions. This study applies ML techniques to predict students’ academic performance using a real-world dataset from a higher education institution. The dataset consists of 1419 student records with 12 features describing the academic attributes. The preprocessing steps includes handling missing values, outlier removal, data standardization, and feature engineering to improve data quality and model performance. Six regression models including linear regression, ridge regression, decision tree regressor, random forest regressor, gradient boosting regressor, and support vector regressor were evaluated after comprehensive preprocessing and model optimization. Among these, the Random Forest Regressor achieved the highest predictive accuracy with R² equals 0.93. These results highlight the potential of regression-based approaches for deployment in early-warning systems, supporting data-driven decision-making and improved educational outcomes.