Comprehensive Analysis of Stress Levels Using Ensemble Learning and Neural Networks for Insightful Understanding

It is vital to comprehend and predict stress levels as it has a significant impact on both the health of the individual and the community. A wide range of machine learning models, ensemble techniques, and sequential neural networks are used in this study to provide a thorough investigation of stress level prediction. Our analysis explores the complex variables affecting stress by utilizing a carefully selected dataset from Kaggle that includes 2838 instances with 7 features each. Each model was examined in detail, displaying a range of performance measures. These models included Naive Bayes, Support Vector Machines, Decision Trees, K-Nearest Neighbors, and XGBoost. The Voting Classifier served as an example of how well the ensemble approach combined many models, resulting in an improved total accuracy of 73%. By accurately identifying intricate non-linear correlations in the stress dataset with a 76% accuracy rate, a sequential neural network enhanced the study. The study of the results provides a basis for further research by revealing the advantages and disadvantages of each model. This work advances our knowledge of stress and its management by contributing to the creation of increasingly precise and complex prediction models as part of the continuing conversation on stress analysis.