Random Forest Analysis of Job Satisfaction
Jackulin Mahariba A.1, Tanvi Mahajan2, Sparsh Heda3
1Jackulin A. Mahariba, Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Tanvi Mahajan, B.Tech, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Sparsh Heda, B.Tech, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1608-1611 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6724048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Job Satisfaction, Job Performance, Parameters, Classification, Hierarchical Clustering.
Scope of the Article: Classification