This paper employs two data-driven methods, Random Forest (RF) and Support Vector Machines (SVM), to develop mineral prospectivity models for an epithermal Au deposit. Four distinct are presented comparison: one employing RF three using SVM with different kernel functions—namely linear, Radial Basis Function (RBF), polynomial. The analysis leverages a compact training dataset, encompassing just...