Utilizing Machine Learning or Predictive Modelling of Stress Levels during Sleep

This paper focuses on examining and predicting stress levels using a dataset containing various physiological parameters. The dataset undergoes thorough preparation and scrutiny to understand its composition and quality, laying the groundwork for subsequent analysis. Through data visualization, insights into the relationships between stress levels and physiological attributes are gained, providing a basis for further investigation. The main emphasis of this paper lies in developing and evaluating machine learning models for predicting stress levels. Various models, such as Logistic Regression, Random Forest, Decision Tree, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Gaussian Naive Bayes, are rigorously trained and tested. These models exhibit impressive performance, with some achieving exceptional accuracy on test data. The successful development of these models suggests practical applications in healthcare, well-being monitoring, and stress management. Nevertheless, the study acknowledges limitations, particularly regarding data quality and representation. The models' effectiveness depends on the quality and diversity of the dataset. Future research and data collection efforts have the possibility of enhancing the precision and dependability of stress level predictions. Overall, this research lays a promising groundwork for utilizing machine learning in stress assessment and management, with potential benefits for individual health and well-being.