Employee Attrition Prediction using Artificial Neural Networks

Abstract

Employee attrition is a significant concern for businesses as it can negatively impact productivity, profitability, and overall success. Employee turnover can be costly and time-consuming, and it can also result in the loss of valuable talent and knowledge. It is important for businesses to predict and understand employee attrition so that they can take proactive measures to retain employees. During Covid, the organisations faced issues due to employees leaving uncertainly, making it difficult for the HR Department to hire new people quickly and the companies had to spend a huge fortune to fill the void. It is important for businesses to monitor and evaluate the effectiveness of retention strategies over time to ensure that they are achieving the desired outcomes. Artificial Intelligence has proved to be of great use in predicting how likely an employee is to leave the organization. By using predictive analytics to identify employees who are at risk of leaving and developing targeted retention strategies, businesses can reduce the negative impact of employee attrition and create a more stable and productive workforce. Our system assists with foreseeing the rate at which employees are stopping position in light of getting logical information available and utilize different Artificial Neural Networks to diminish prediction error. The main objective of this model is to study employee attrition in an organization, to find out the issues of the representatives in the enterprise, and to distinguish how maintenance procedure lessens worker turnover.