With the ongoing COVID-19 pandemic, business and organizations have adapted to unconventional and different working styles and patterns, like working from home, working with limited employees in the office premise, etc. With the new normal here to stay for the recent future, employees have also adapted to different working environment and routines, which has also resulted in fatigue and stress for many, as they adapt to the new normal and adjust their personal and professional lives. Employees may feel stressed when they are unable to cope with the prolonged uncertainty and pressure. Other factors leading to stress may include feeling isolated while working remotely, lower wages or salaries, lack of opportunity for advancement or growth, unmanageable workload, extended working hours, unsatisfactory work environment, lack of connect with the team, lack of ability and skill to cope with the work apart from the fear of catching the virus. Spotting early signs of stress among employees will help employers identify and address the scenario, and hence help in reducing its impact on the employee and on the organization.
The algorithms used in this project includes:
- Logistic Regression
- Decision Tree
- Support Vector Machines
- k- nearest neighbors
- Random forest
Out of these: Logistic Regression, Support Vector Machines and Random Forest performs the best and therefore have been optimized further using 'Grid Search' to fine tune hyperparameters.
After the optimization of parameters, 'Feature Selection (Recursive)' is used to select the most suitable features from the data and finalize the predictions of the project.