Shahrood University of Technology Journal of Mining and Environment 2251-8592 16 3 2025 05 01 Categorization of Mineral Resources using Random Forest Model in a Copper Deposit in Peru 947 962 3419 10.22044/jme.2025.15568.2984 EN Marco Antonio Cotrina-Teatino Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru Jairo Jhonatan Marquina-Araujo Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru Jose Nestor Mamani-Quispe Faculty of Chemical Engineering, National University of the Altiplano of Puno, Puno, Peru Solio Marino Arango-Retamozo Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru 0000-0003-3594-0329 Johnny Henrry Ccatamayo-Barrios Department of Mining Engineering, Universidad Nacional San Cristobal de Huamanga, Ayacucho, Peru Joe Alexis Gonzalez-Vasquez Department of Industrial Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru Teofilo Donaires-Flores Faculty of Chemical Engineering, National University of the Altiplano of Puno, Puno, Peru Maxgabriel Alexis Calla-Huayapa Faculty of Industrial Process Engineering, National University of Juliaca, Juliaca, Peru Journal Article 2025 01 10 This work aimed to categorize mineral resources in a copper deposit in Peru, using a machine learning model, integrating the K-prototypes clustering algorithm for initial classification and Random Forest (RF) as a spatial smoother. A total of 318,443 blocks were classified using geostatistical and geometric variables derived from Ordinary Kriging (OK) such as kriging variance, sample distance, number of drillholes, and geological confidence. The model was trained and validated using precision, recall, and F1-score metrics. The results indicated an overall accuracy of 97%, with the measured category achieving 98% precision and an F1-score of 0.98. The total estimated tonnage was 5,859.36 Mt, distributed as follows: 1,446.13 Mt (measured), 2,249.22 Mt (Indicated), and 2,164.01 Mt (Inferred), with average copper grades of 0.43%, 0.33%, and 0.31% Cu, respectively. Compared to the traditional geostatistical methods, this hybrid approach improves classification objectivity, spatial continuity, and reproducibility, minimizing abrupt transitions between categories. The RF model proved to be a robust tool, reducing classification inconsistencies and better capturing geological uncertainty. Future studies should explore hybrid models (K-means with RF, ANN with K-Prototypes, gradient boosting, and deep learning) and incorporate economic variables to optimize decision-making in resource estimation. https://jme.shahroodut.ac.ir/article_3419_6647bcbcd57e640bbd31f1567ef0f5a7.pdf