Introduction: This paper is part of a research project that aims to construct a predictive model for students’ academic performance, as result of an iterative process of experimentation and evaluation of the pertinence of some data mining techniques. Methodology: This paper was written in 2016 in the Universidad El Bosque, Bogotá, Colombia, and presents a comparative analysis of the performance and relevance of the J48 and Random Forest algorithms, in order to identify the most influential demographic and icfes score variables, as well as the classification rules, to predict the first year academic performance of the Engineering Faculty students, in Universidad El Bosque, Bogotá, Colombia. Results: The analysis process was carried out on 7,644 students’ records, and it was developed in two phases. Firstly, the data needed to feed the mining process was extracted and prepared. Secondly, the data mining process itself was implemented through preprocessing data and executing the classification algorithms available in Weka. Some significant variables and rules to predict academic performance are found, according to the studied population characteristics. Conclusions: The academic risk seen as the cause of the desertion phenomenon must be studied as a phenomenon itself. Establishing its causes facilitates the creation of preventive strategies for the accompaniment of students through their process, aimed to mitigate the risk of both phenomena.