Well-classified values of 98.90% for operational data and 98.72% for pile/piling data were obtained. Two models, one of them with actual operational data and another one with data generated in a controlled environment (piling) are presented. This paper describes an experience using artificial intelligence techniques, particularly random forest, to develop predictive models for copper recovery by leaching, using data from an enterprise present in northern Chile for more than 20 years. New regulations from the Chilean government and changes in the copper mining industry (such as a trend to underground mining) are fostering the search for better results in typical processes such as leaching. These changes, produced by technological context and more controls (e.g., environmental controls), are also becoming visible in Chilean mining. Future research lines are proposed to validate these outcomes, mostly concerned with the acquisition of more data across several future semesters.Ĭopper mining activity is going through big changes due to increasing technological development in the area and the influence of industry 4.0. As a result, the final classifier presented an accuracy of 80%, and one of the most influential variables according to this model was Covered Roles 2, which represents the number of roles covered in each group. The main objective of this work is to provide evidence of the real impact of the Belbin test outcomes on the final performance of a team of students, using as a metric of variable importance the one provided by a Random Forest model, supplied by the scikit‐learn library.

It aims to analyze the importance of variables influencing the final average grade of the UCN students in projects related to different subjects, focusing on the dimensions resulting from the Belbin test. This paper describes the process of generating a predictive model of students’ academic performance in different engineering subjects at Universidad Católica del Norte (UCN).

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