Stress Detection using RNN Algorithm

The objective of this study is to detect human stress using the Machine Learning approach. The proposed approach employs NLP and RNN algorithms to identify and classify stress from text data. The main goal is to build a model that can accurately detect stress in people from their text data, which can be used in practical applications such as mental health diagnosis, stress management, and personal training. The key challenge in this study is to train the model on a labeled dataset of text data that contains examples of accented and non-accented text and to identify patterns and features that can distinguish between the two. To address this challenge, various Python libraries such as NumPy, Pandas, matplotlib, and Sklearn are used to preprocess data, extract features, train the model, and evaluate its performance. This approach provides a powerful tool for detecting stress and has the potential to improve people's general well-being. By accurately identifying and classifying stress from text data, the proposed approach can help individuals and organizations better understand and manage stress in various contexts. The potential applications of this approach extend beyond stress detection, as it can be adapted to classify other types of emotions or sentiments in text data. Overall, this study presents a promising approach to detecting stress using machine learning, with significant potential for practical applications and an impact on people's well-being.