AI Salary Prediction Using Machine Learning

Abstract

The determination of employee salaries has traditionally relied on manual surveys and historical data analysis, which often fail to account for the dynamic factors influencing compensation. With the rise of Artificial Intelligence (AI) and Machine Learning (ML), data-driven models can now predict salaries more accurately by learning from real-world patterns.

This paper presents an AI-based salary prediction system that applies machine learning regression algorithms to forecast employee compensation based on multiple factors, including education, experience, location, and job role. Models such as Linear Regression, Decision Tree Regressor, Random Forest Regressor, and Support Vector Regressor (SVR) are trained and evaluated using real-world datasets.

Experimental results reveal that the Random Forest model outperforms others in prediction accuracy. The proposed system provides organizations with an unbiased, scalable, and automated tool for salary estimation, enabling more transparent and fair compensation strategies.