In The Fast-paced And Demanding World Of Information Technology (IT), The Well-being Of Employees Is Of Paramount Importance. Recognizing And Addressing Stress In IT Employees Is Crucial For Both Individual Health And Overall Organizational Performance. This Project Endeavours To Provide A Reliable And Efficient Solution For The Early Detection Of Stress In IT Employees Through The Analysis Of Text Data. The Primary Objective Of This Study Is To Develop A Stress Detection System Using The Random Forest Algorithm, Which Has Demonstrated Exceptional Performance In The Field Of Machine Learning. These Features Encompass A Wide Range Of Text-based Attributes, Including Sentiment Analysis, Topic Modelling, And Linguistic Markers Associated With Stress And Well-being. Through A Rigorous Process Of Data Pre-processing, Feature Engineering, And Model Training, We Achieve An Impressive 99% Accuracy In Stress Detection.
