Machine Learning Models for Salary Prediction Dataset using Python

Abstract

In today’s world, salary is the primary source of motivation for many regular employees, which makes salary prediction very important for both employers and employees. It helps employers and employees to make estimations of the expected salary. Fortunately, technological advancements like Data Science and Machine Learning (ML) have made salary prediction more realistic. In this paper, we exploit the benefits of data science to collect a 20,000+ dataset of salaries in the USA. We then apply three supervised ML techniques to the obtained datasets to produce salary prediction. The learning models are linear regression, random forest, and neural networks. The output of the three models is analyzed and compared to show the following; neural network outperforms the other ML models for better accuracy with accuracy level 83.2%, and linear regression has the fastest time of 0.363s for training the model.