Ensemble method based architecture using random forest importance to predict employee's turn over

Abstract

The departure of a skilled employee can create a problem for a company and this incident is increasing globally. Employee turnover has become an important issue these days due to the heavy workload, low pay, low job satisfaction, poor working environment. Companies face problems as their budget will increase, losing skilled manpower and employees' trust. It's taking time to adjust for a new employee and bring risk and increase the cost for the company. It is necessary to bring appropriate solutions to the problem. The main purpose of this paper is to predict the turnover of employees with the help of state of the art machine learning classifier. We have determined employee turnover selection factors using some prediction models. We first pre-processed the dataset by removing correlative attributes. Then, we have scaled the attributes. Secondly, a Sequential selection algorithm (SBS) has been using to reduce features from a high number to a relatively small signal-canton. Then use Chi-square and Random Forest important algorithms to determine the most significant shared key features. Then we get average-montly-hours, satisfaction-level, time-spend-company are responsible for the employee's departure. Then, we have applied different state of the art machine learning algorithm to measure the accuracy. We have achieved the highest accuracy of 99.4% using the reduced feature with 10-Fold Cross-validation by applied the Random Forest classifier and which is higher than the mentioned reference work.