Predicting Employee Attrition Using Machine Learning

Predicting Employee Attrition Using Machine Learning

Mr. M. Sriramulu , M. Tech­­1

V. Kusuma2, M. Rashmitha3, N. Anusha4, P. Mahesh Babu5, M. Bhanuja6

1Assistant professor, Dept. of CSE, Dhanekula Institute of Engineering and Technology, Ganguru, Vijayawada

2Student, Dept. of CSE, Dhanekula Institute of Engineering and Technology, Ganguru, Vijayawada

3Student, Dept. of CSE, Dhanekula Institute of Engineering and Technology, Ganguru, Vijayawada

4Student, Dept. of CSE, Dhanekula Institute of Engineering and Technology, Ganguru, Vijayawada

5Student, Dept. of CSE, Dhanekula Institute of Engineering and Technology, Ganguru, Vijayawada

6Student, Dept. of CSE, Dhanekula Institute of Engineering and Technology, Ganguru, Vijayawada

Abstract— Companies are constantly looking for ways to keep their professional workforce engaged to reduce the additional costs of recruitment and training. Predicting whether or not a particular employee will leave will help the company make preventative decisions. Unlike physical systems, human resource problems cannot be explained from a scientific-analytical perspective. Therefore, machine learning is the best tool to do this. Many reasons can cause employee anxiety. In this we can use several machine learning models are developed to automatically and accurately predict employee turnover. This work uses the IBM attrition dataset to train and test machine learning models; namely Logistical Regression, Random Forest and Gradient Boosting, examples. The ultimate goal is to accurately identify attrition among a company’s conservative workforce to help improve retention strategies and increase the satisfaction of those employees.

Keywords – Employee Attrition, IBM Dataset, Logistical Regression, Random Forest, Gradient Boosting, Prediction