A novel classification model for employees turnover using neural network to enhance job satisfaction in organizations - Ahmed, Tarig Mohamed (Array, 2021)
The most important challenge facing modern organizations is to keep their employees as valuable assists. Employee turnover is one of these challenges. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. The proposed model is based on machine learning algorithms. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84% and AUC (ROC) of 74%. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner and setting proactive plans to keep them. Besides the model, three important features should be dealt with carefully as Over Time, Job Level, Monthly Income.