Random forest analysis of job satisfaction

Abstract

Job satisfaction plays an important role in the productivity of an organization. Satisfaction of an employee cannot only be determined through variables like salary and location. There are lots of factors that affect satisfaction which further affects the performance of an organization. The main goal of this project is to obtain better knowledge of the parameters responsible for job satisfaction and based on it how various organizations differ from each other with respect to their working conditions. It presents the result of an empirical study of how factors like age, gender, department, education, marital status, hours per week, overtime, hike, native country etc. affects job satisfaction of the labor force of a particular country using machine learning. The study is based upon the results obtained through supervised algorithm, Random Forest. Through this research we attempt to discover how the different aspects of job satisfaction are related to job prevailing parameters. The model will help organizations increase productivity of its employees by ensuring a better working condition.