Unveiling the Salary Landscape: Predicting Compensation for Software Engineers

The tech industry is fast-paced and competitive, making it difficult to accurately determine the pay of software engineers. Often, the traditional methods such as relying on experience alone without considering other factors like educational background and geographic location lead to inaccurate salary estimates. This paper proposes a novel approach that uses machine learning for better estimation of software engineer wages. By analyzing many professional and demographic variables such as education level, coding years, country among others, the paper has come up with a model that overcomes some limitations in conventional techniques. The data trained three regression models which include Linear Regression, Decision Tree Regression and Random Forest Regression on data from Stack Overflow Developer Survey which was pre-processed with caution. In this process, Grid Search Cross-Validation was used for hyperparameter tuning so as to improve the predictability of the Decision Tree Regression model. This research seeks to give programmers an insight into their worth in the job market and help employers develop fair compensation packages based on facts about current trends in programming labor markets. Experimental results indicate that despite having room for improvement, Decision Tree Regression (DTR) performs better compared to other models used in this study.