Workforce Data Analysis

Today, businesses and organizations around the world are keeping up with the ever-changing and developing technology breakthroughs. In the last few years, data science has been used to make educated, smart decisions and improve business performance. An organization's workforce is one of its key elements and understanding the workforce goes a long way in advancing the organization's success. This study investigates the effects of employee characteristics, supervisor characteristics, and other job parameters on employee presence in the factory. The analysis was conducted using hypothesis testing methods such as the chi-squared test and analysis of variance and also using machine learning algorithms like multiple linear regression and classification algorithms like K-nearest neighbor, random forest and support vector machine. Gaining insight into the personal characteristics of employees, as well as how these traits, including commitment to work, may be influenced by the traits of their supervisors, provides a valuable means of understanding the complexities of human behavior within workplace dynamics. This enables us to understand how their traits fit their workstyle group.