Author : Ashish Pandey 1
Date of Publication :11th September 2023
Abstract: Human stress is a prevalent main problem in today's society and it can lead to negative physical and mental health outcomes. Traditional methods of measuring stress, such as self-report questionnaires, can be subjective and time-consuming. Machine learning techniques have the potential to provide a more objective and efficient approach to stress detection. In this project, aim to develop a machine learning model for detecting stress using a dataset on Kaggle that contains 116 columns of various physiological , and demographic features. The dataset was collected from participants who completed a stress-inducing task, such as a speech or a math test, and also completed self-reported stress levels. In this project use machine learning techniques support vector machines (SVM) to design a stress detection model. Finally, the trained model to predict stress levels for new participants based on their physiological and demographic features. In conclusion, study shows that machine learning can effectively detect stress in individuals and has the potential to be a valuable tool in early stress detection and prevention. Future research should focus on further improving the accuracy and scalability of the proposed approach to enable real-world applications.
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