Human Stress Detection Based on Sleeping Habits Using Machine Learning Algorithm

• Python
• Machine Learning

Stress, an increasingly prevalent aspect of modern life, can significantly impact an individual?s physical and mental well-being. Hence, understanding and monitoring stress levels play a crucial role in promoting overall health and quality of life. The project ?Human Stress Detection Based on Sleeping Habits Using Machine Learning with Random Forest Classifier? presents a novel and effective approach to detect human stress levels by analyzing their sleeping habits. Leveraging the powerful capabilities of Python programming language, the study employs the Random Forest Classifier algorithm, known for its versatility and accuracy in classification tasks. The primary objective of this research is to develop a reliable stress detection system that can provide valuable insights into individuals? stress levels, enabling timely interventions and promoting better mental health. The dataset used in this project is carefully accurate and comprises various essential parameters related to both sleep patterns and stress levels. These parameters include the user?s snoring range, respiration rate, body temperature, limb movement rate, blood oxygen levels, eye movement, the number of hours of sleep, heart rate, and stress levels categorized into five classes: 0 (low/normal), 1 (medium low), 2 (medium), 3 (medium high), and 4 (high). The inclusion of these diverse parameters ensures a comprehensive analysis of sleep patterns and their correlation with stress levels.?