STRESS DETECTION IN IT PROFESSIONAL BY MACHINE LEARNING

Stress levels amongst the Indian employees have increased due to a variety of factors and are a matter of great concern for the organizations. This study is based on Indian working professionals and real data has been collected by using non-probability convenience sampling. A questionnaire was drafted based on eighteen factors affecting the mental health of professionals. This study addresses two dimensions, first is to identify the important influential features that trigger stress in the lives of working professionals, and the second is to predict the stress levels. Various supervised machine learning algorithms have been experimented with and of all these algorithms, the Support Vector Machine Regressed model showed the best performance. The main contribution of the paper lies in the identification and ranking of ten important stress triggering features that can guide organizations to develop policies to take care of their employees. The other deliverable is the development of a GUI?based stress prediction software based on Machine learning techniques. As the globalization, is expanding individuals twist towards the cutting-edge life Stress issue turns into a significant issue among in experts and understudies life. The term pressure is causing different mental issue face to face. Understudies of various courses and distinctive expert college are expanding ambushed with this pressure. The points of this examination are to research natural, social, mental and scholarly postgraduate and doctoral understudies. The quantities of tests of this investigation are 220 undergrad and postgraduate understudies. The information of my examination was gathered independent from anyone else – planned poll and by PSS Scale and the overview has organized inquiries which were gathered through google dox. There are many pressure expectation calculations has been proposed like SVM, KNN, RANDOM FOREST, NAVIE BAYES, LOGISTIC REGRESSION, DECISION TREE. Different machine learning methods are utilized that related in this field. Too, it talks about the application territories and difficulties for stress forecast with knowledge into the past research work.