Predicting Employee Attrition using XGBoost Machine Learning Approach

Considering the global competitive scenario, there is ocean of opportunities for skilled and talented persons in the world, and given a good chance, employees part from one organization to another. Employee turnover is regarded as the key issue for all organizations these days, because of its adverse effects on workplace productivity, and accomplishing organizational objectives on time. To overcome this problem, organizations are now taking support via machine learning techniques to predict the employee turnover. With high precision in prediction, organizations can take necessary actions at due course of time for retention or succession of employees. Most of the data comes from basic HR based database systems, which are not highly efficient in prediction and modeling and these models are not very accurate in data models and cannot assist the organizations to take successful decisions. The primary objective of this research paper is to predict employee attrition i.e. whether the employee is planning to leave or continue to work within the organization. In this paper, we propose a novel model for predicting Employee Attrition using Machine Learning based approach i.e. XGBoost which is highly robust. In order to validate the accuracy of the system proposed for Employee Attrition, the data set is acquired via online database and fetched to the system and highly stunning and precision results are shown by the system with regard to Employee turnover behavior.