Abstract

This paper aims to develop a machine learning model to predict student stress based on relevant academic, behavioral, and personal attributes. The prediction model is built using numerous combinations of features and various well-known classification algorithms. We achieve a greater performance level with our proposed student stress prediction model with an accuracy of 86%.

Keywords

Machine Learning, Stress Prediction, Classification Techniques, Predictive Modeling, Mental Health, Academic Performance

References

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How to Cite

Deepak Singh, Mr. Ankur Kumar Varshney, “StressAlert: Early Detection of Student Stress,” International Journal of Computer Techniques, Volume 12, Issue 3, June 2025. ISSN: 2394-2231