A Novel Analysis of Employee Attrition Rate by Maneuvering Machine Learning

Abstract

Employee attrition rate in Tech industry has become dreadful day by day in all over the world. Meanwhile It has been noticed that churn (attrition) rate in IT industries is growing rapidly than expected especially during pandemic times. This is taken as a foremost issue by each tech industry, to analyze and adapt to the change. The main snag is that, the expenditure of recruiting on a new employee is foremost ineffective than retaining a company trained professional employee. Also retaining an employee will assure certain credibility and work culture of the company than the new employee. Also, the latter will be given access to training modules and code of conduct of the company with lots of Information Overload on a short span of time. It is essential to mention, not every organization has comprehensive training programs for their employees, especially the start-up tech firms, which focuses heavily on skilled workers with experience beforehand. This anonymity causes HR departments to scrutinize and tweak their actions according to current trend in the market. The major goal of this study is to make predictions whether the skillful employee will quit or continue further and predict the reason for quit using supervised classification and machine learning algorithms. Acquainting the human resource team to help them with the required analytics to make decisions based on machine learning.