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Purpose – This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions. Design/methodology/approach – This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used. Findings – This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust. Originality/value – This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before modeling.