Bi-LSTM Deep Learning Approach for Employee Churn Prediction

Abstract

Employee churn prediction is also known as ‘attrition’ or ‘turnover’ is referred to as the identification of employees planning to quit the organization in the future. Organizations invest time, effort, and money in employees’ training. Therefore, an experienced employee is an asset to the organization. If organizations could predict employee churn using machine learning techniques and can take timely measures, then they can prevent long-term loss. A number of machine learning models have been used for churn prediction of employees, such as Logistic Regression, Support Vector Machine, and MLP (Multi-Layer Perceptron). The aim of this study is to find the optimal algorithm of classification for the prediction of the churn employee rate. A deep learning approach based on B-LSTM (Bi-Directional Long Short-Term Memory) is being proposed and tested. The accuracy of B-LSTM is 97.5% during the consistency test. A comparative analysis with other machine-learning techniques is also performed and it is concluded that B-LSTM has proved more effective than other machine learning techniques investigated in this study.