Predictive Modelling of Stress Levels During Sleep using Machine learning

Abstract: This study thoroughly explores the analysis and prediction of stress levels using a dataset that encompasses a diverse range of physiological parameters. The dataset undergoes meticulous preparation and scrutiny to comprehend its composition and quality, laying the groundwork for subsequent analysis. Through data visualization, valuable glimpses on the connections amongst stress levels and physiological attributes are gained, serving as a foundational step for further examination. The primary focus of this work is on the development and evaluation of machine learning models for stress level prediction. Various models, including Logistic Regression, Random Forest, Decision Tree, Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Gaussian Naive Bayes, are rigorously trained and tested. These models exhibit remarkable performance, with selected ones achieving impeccable accuracy on the test data. The successful development of these models opens up practical applications in healthcare, well-being monitoring, and stress management. However, the study acknowledges certain limitations, particularly about the portrayal and the accuracy of the data. The level of accuracy nor broadness of the dataset affect the effectiveness of the models. Future studies and data gathering initiatives might improve the precision and reliability of stress level predictions. In conclusion, this work establishes a promising foundation for utilizing machine learning in stress assessment and management, with the potential to positively impact individuals' health and well-being.