Salary Prediction with Machine Learning in Teachers Hired from the Region of Cusco Perú

This article presents an analysis of machine learning (ML) models to predict the salaries of 11,392 contracted teachers appointed at Ugel in the Cusco region of Peru, using recent data from the unique payroll system. The core element of the study is the hired teachers, deliberately excluding the salaries of the appointed teachers from the analysis. A significant result of this research is the identification of a new ML model capable of predicting teacher salaries with considerable accuracy, based on independent variables closely related to salary. This finding is noteworthy because it fills a gap in existing ML applications for salary prediction, indicating a promising direction for future research in this area. Even though the methodology used to analyze salary data is exhaustive, it does not take into account gender differences, which may affect salary variation during the three-year period considered. This oversight suggests that future research should include a broader range of variables, including gender, to improve the accuracy and applicability of salary predictions for both appointed and contracted faculty. Such an approach could provide more nuanced information about the factors that influence teacher salaries and help develop more equitable and effective salary models. One of the key contributions of the article is the detailed examination of the factors that influence the salaries of appointed teachers, including age, position, educational level, modular code, weekly hours, period, and other Dummy variables. The use of Decision Tree Regressor (DTR), Gradient Boosting Regressor (GBR), Random Forest Regressor (RFR), Neural Network Regressor (NNR), and Support Vector Regressor (SVR) models yielded accurate metrics to choose the best model for salary prediction. This research not only advances our understanding of the determinants of teacher salaries in the Cusco-Peru region, but also offers a valuable framework for similar studies in other contexts. Finally, in comparison with other research, this highlights the robustness of the chosen ML models, underscoring the potential of ML in educational administration and policy formulation. abstract environment.