Predicting Employee Under Stress for Preemptive Remediation Using Machin Learning Algorithms

The modern world is filled with stress. A person's pressure is affected by a variety of factors. Representatives in IT are more likely to be under pressure due to work pressure, overburdening, higher worker mastery, and so on. When a person is stressed, it can lead to a variety of mental health issues such as depression, anxiety, somatization, lack of concentration, andsoon.Asaresult,itisnecessarytoidentifyhumanstressatan earlystageinordertoprovide appropriate solutions and alleviate stress. There has been a lot of research done on stress prediction. Many research papers use Machine Learning techniques to predict stress, and many papersuse IOT -based sensors to extract the features needed for stress prediction. There are so many existing systems in predicting employee stress. In this project we are going to predict the employee stress using XGBOOST algorithm since it gives more Accuracy. Based on this the prediction we will remediate the persons under stress in the early stage which is good for their health and as well as the work.