Lakhasly

The well-being of students has increasingly come under scrutiny, particularly in the context of their academic and social environments, which are often sources of substantial stress.This approach not only helps in identifying the predominant stressors but also in understanding how different factors interact with each other, providing a foundation for holistic intervention strategies.This study employs a comprehensive dataset encompassing various psychological, physiological, environmental, academic, and social dimensions to explore students' stress dynamics.By integrating machine learning techniques, specifically correlation analysis and Random Forest modeling for feature importance evaluation, the study aims to provide a detailed understanding of how these factors interrelate and impact student stress.Machine learning offers robust analytical capabilities that can handle complex, multi-dimensional data, making it possible to derive more nuanced insights into the stress factors affecting students.

The well-being of students has increasingly come under scrutiny, particularly in the context of their academic and social environments, which are often sources of substantial stress. This stress affects their mental health, academic performance, social interactions, and overall quality of life. Recognizing and addressing the factors contributing to student stress is imperative for fostering healthier educational environments. Past research has identified multiple dimensions of student life that contribute to stress, including academic pressure, social relationships, and personal health issues. Studies have traditionally focused on isolated aspects of student stress, often neglecting the interplay between various factors. With the advent of machine learning and big data analytics, there is a growing potential to undertake a more holistic analysis. Machine learning offers robust analytical capabilities that can handle complex, multi-dimensional data, making it possible to derive more nuanced insights into the stress factors affecting students. This approach allows for the examination of direct correlations and the relative importance of each factor in contributing to overall stress levels. This study employs a comprehensive dataset encompassing various psychological, physiological, environmental, academic, and social dimensions to explore students' stress dynamics. By integrating machine learning techniques, specifically correlation analysis and Random Forest modeling for feature importance evaluation, the study aims to provide a detailed understanding of how these factors interrelate and impact student stress. The goal is to identify the most significant stressors, thereby guiding targeted interventions to reduce stress and enhance student well-being in educational settings. This approach not only helps in identifying the predominant stressors but also in understanding how different factors interact with each other, providing a foundation for holistic intervention strategies.