Salary Prediction of Government Teaching Professionals Using Machine Learning

Salary Prediction of Government Teaching Professionals Using Machine Learning Dr. P.N. Shiammala Dr. P.N. Shiammala Department of Computer Application, VELS Institute of Science Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India. N. Uma Mageshwari N. Uma Mageshwari Department of Computer Application, VELS Institute of Science Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India.

Predicting the salary of government teaching professionals is essential for ensuring equitable compensation and effective workforce planning in the education sector. This study presents a machine learning-based approach to predict the monthly salary of government college teaching professionals across Indian states using features such as years of experience, educational qualification, number of publications, designation, specialization, and state of employment. Two regression algorithms are compared: Linear Regression and Random Forest Regressor. The dataset comprises 200 records generated based on realistic salary structures of government teaching professionals in India. The models are evaluated using standard metrics including R² Score, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and prediction accuracy. Experimental results demonstrate that Random Forest Regressor achieves a superior accuracy of 96.81% (R² = 0.9681) compared to Linear Regression at 92.47% (R² = 0.9247), owing to its ability to capture non-linear relationships in the data. The proposed system provides a reliable, data-driven framework for salary estimation that can assist policy makers and educational administrators in fair compensation planning. Keywords: Machine Learning, Linear Regression, Random Forest, Salary Prediction, Government Teaching Professionals.
05 03 2026 1 8 10.55041/ijcope.v2i5.010 https://ijcope.org/article/salary-prediction-of-government-teaching-professionals-using-machine-learning/