Abstract
Stress has become an integral and unavoidable part of our lives. It has created an alarming situation for the mental health of teenagers and youth globally. At the critical juncture of teenage to adulthood transition, many challenges are faced by teenagers that too with exposure of social networking devices. Hence, it is imperative to learn about various factors that cause stress and identify those features that are more significant contributors so that appropriate measures can be taken to cope up with it effectively. This paper is a step toward analyzing stress among students of a few educational institutions in India. The data have been collected from 650 respondents using Likert scale of 5. With the application of different data visualization techniques and random forest regressor algorithm, 15 important contributing factors from a list of 25 features have been identified and the prediction of stress level has been done with a R-squared value of 0.8042.
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Pabreja, K., Singh, A., Singh, R., Agnihotri, R., Kaushik, S., Malhotra, T. (2021). Stress Prediction Model Using Machine Learning. In: Bansal, P., Tushir, M., Balas, V., Srivastava, R. (eds) Proceedings of International Conference on Artificial Intelligence and Applications. Advances in Intelligent Systems and Computing, vol 1164. Springer, Singapore. https://doi.org/10.1007/978-981-15-4992-2_6
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DOI: https://doi.org/10.1007/978-981-15-4992-2_6
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