Employee Salary Prediction in HRMS Using Regression Models

Abstract

The study uses regression models to examine AI model engagement in employee salary prediction. Salary prediction is significantly critical for both employees and employers. Employees try to get maximum benefit for the services they provide, and employers emphasize achieving organizational goals through optimized employee salaries. Inconsistency in employee salaries may cause organizations financial losses and organizational objectivity failure. Historically, salary was determined by historical data, market surveys, and personal judgment, which often resulted in inconsistencies and biases. With the availability of large datasets from Human Resource Management Systems and advanced machine learning algorithms, there is an opportunity to enhance the fairness of Salary Prediction. To overcome this problem in organizations, we have proposed a regression model for salary prediction with a promising accuracy rate of 99% with regression models. The methodology includes data processing steps including EDA, data standardization, feature correlation, and feature engineering to enhance the accuracy of the models. This study used Random Forest Regressor, Gradient Boosting Regressor, and Light Gradient Boosting Machine Regressor models for employee salary prediction. This research paper provides a valuable understanding of HR analytics for HR professionals and organizations for salary prediction of employees in an organization. Also, this research investigates the use of Machine learning algorithms to predict employee salaries while comparing employee performance and eliminating biases. The aim is to develop robust, data-enriched frameworks in HRMS for organizations for accurate and transparent salary prediction.