Special Education Demand Prediction Database in Peru Using Machine Learning

Prediction and decision-making in various sectors have been greatly enhanced through time series analysis using machine learning algorithms. However, their application in the field of special education remains incipient. This field presents challenges in terms of inclusion, planning, and resources, especially in countries like Peru, where existing administrative records are underutilized for analytical and predictive purposes. This study explores the application of an explainable machine learning algorithm for time series modeling and forecasting in the Peruvian education system. The Random Forest algorithm was implemented on a multivariate database composed of official enrollment records in Special Basic Education corresponding to the 2019–2024 period, with the aim of projecting the evolution of demand until 2025. The demand-predictive approach allows for the identification of nonlinear and dynamic growth patterns at different educational levels (0–2 years, Preschool and Primary) across the country's regions. The models achieved high accuracy rates (R² > 0.97), with performance metrics including a root mean square error (RMSE) below 190, a mean absolute error (MAE) below 70, and a mean absolute percentage error (MAPE) below 10%, demonstrating their usefulness as a strategic support tool for decision-making, optimizing the planning of economic and financial resources for special education. Unlike traditional approaches, this proposal combines predictive modeling, algorithmic interpretability, and geospatial analysis, contributing to educational planning that is more sensitive to diversity and structural inequalities.