Analysis of School Dropout Rate in Paraguay Using a Machine Learning Approach

Abstract

This study investigates the school dropout rates in Paraguay, focusing on the transition from ninth grade to the first year of secondary school in the Concepción department. Using an extract, transform, and load (ETL) process, data from the Paraguayan Ministry of Education and Science and the National Institute of Statistics were analyzed. The research employs clustering techniques, particularly K-means, to identify patterns and risk profiles among students. The findings highlight the significant impact of socio-economic factors, such as poverty and child labor, on school dropout rates. These insights aim to inform targeted interventions to improve educational outcomes and reduce dropout rates in Paraguay.