Employee attrition prediction with convolutional neural network and synthetic minority over-sampling technique

Employee attrition negatively affects organizational efficiency, customer service quality, and brand reputation. In this study, we explore the important issue of predicting employee attrition, which aids managers in taking effective retention strategies and mitigating the significant costs associated with recruiting and training new personnel. Different from existing research, we introduce a novel deep learning model featuring an intermediary layer that automatically generates a hidden image representation from tabular data. This intermediary step facilitates the efficient utilization of Convolutional Neural Networks, specifically designed for image data, thereby enhancing predictive accuracy. Furthermore, we employ the widely used Synthetic Minority Over-sampling Technique for handling imbalanced data to further improve our model’s performance. Our new CNN-based deep learning model demonstrated the best performance for predicting employee attrition, which can assist in reducing costs related to turnover and facilitate the implementation of a succession plan to ensure seamless transitions.