AI-Based Student Performance Prediction and Academic Success Recommendation System Using Random Forest and Explainable Machine Learning

Random Forest is an ensemble supervised machine learning algorithm widely used for classification and prediction problems. It constructs multiple decision trees during the training phase and combines their outputs through majority voting to generate the final prediction. Unlike a single decision tree, Random Forest significantly reduces overfitting by introducing randomness during both data sampling and feature selection. The algorithm was selected for this research because of the following advantages: • High prediction accuracy • Robust performance on structured educational datasets • Ability to process multiple academic variables simultaneously • Reduced overfitting compared to individual decision trees • Built-in feature importance estimation for Explainable AI   In the proposed system, Random Forest successfully classified students into High Performer, Average Performer, and At-Risk categories while achieving an overall prediction accuracy of 89%. The feature importance generated by the algorithm also enabled interpretation of academic factors influencing prediction outcomes, making the system more transparent and reliable for educational applications.

[1] C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. [2] L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [3] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd ed. New York, NY, USA: Springer, 2009. [4] I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4th ed. Burlington, MA, USA: Morgan Kaufmann, 2017. [5] F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011. [6] W. McKinney, "Data Structures for Statistical Computing in Python," in Proceedings of the 9th Python in Science Conference (SciPy 2010), Austin, TX, USA, 2010, pp. 56–61. [7] J. D. Hunter, "Matplotlib: A 2D Graphics Environment," Computing in Science & Engineering, vol. 9, no. 3, pp. 90–95, 2007. [8] F. Chollet, Deep Learning with Python, 2nd ed. Shelter Island, NY, USA: Manning Publications, 2021. [9] R. S. Baker and K. Yacef, "The State of Educational Data Mining in 2009: A Review and Future Visions," Journal of Educational Data Mining, vol. 1, no. 1, pp. 3–17, 2009. [10] C. Romero and S. Ventura, "Educational Data Mining: A Review of the State of the Art," IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 40, no. 6, pp. 601–618, Nov. 2010.