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@article{197111,
        author = {Ashutosh Dev and Gaurav Kumar and Deepak Gupta},
        title = {Student Performance Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6932-6935},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197111},
        abstract = {This study investigates the behavioural and academic predictors of academic performance among undergraduate engineering students using supervised machine learning techniques. A dataset comprising demographic profiles, academic records, behavioural patterns, and psychological attributes was analysed. Five machine learning algorithms—Random Forest, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Naive Bayes—were applied and compared. SHAP (SHapley Additive exPlanations) analysis was employed to identify and rank feature contributions. Random Forest achieved the highest accuracy (91%). Assignment submission regularity, laboratory attendance, and self-study hours emerged as the most influential behavioural predictors. The study recommends targeted behavioural monitoring and early intervention frameworks for engineering programmes.},
        keywords = {Engineering Students, Machine Learning, Behavioural Predictors, Feature Importance, SHAP Analysis, Academic Performance, Educational Data Mining},
        month = {April},
        }