Prediction of Employee Retention using Cassandra and Ensemble Learning
Shubham Karande, Ajay Shelake, Sivagami M, Sharon Sophia
Journal of Information Organization, Vol. 9, No. 4, 2019.
DOI: https://doi.org/10.6025/jio/2019/9/4/134-140
Link: http://www.dline.info/jio/fulltext/v9n4/jiov9n4_3.pdf
Abstract:
Employee turnover is now becoming a major problem in IT organizations, telecommunications and many other
industries. Why employees leave the organization? is the question raising among many HR managers. Employees are the most
important assets of an organization. Hiring new will always take more efforts and cost rather than retaining the old ones. This
paper focuses on finding the key features of voluntary employee turnover and how they can be overcome well before time. The
problem is to classify whether an employee will leave or stay. Data is taken from Kaggle. The proposed work will uses
ensemble learning to solve the problem, rather than focusing on a single classifier algorithm it will combine weak learning
algorithms to get a better ensemble model. We have used Cassandra to store the data in the form of table and retrieving data
to perform machine algorithm on them.