During the WEKA classes with the students, each student had the opportunity to work hands-on with their own dataset. The main objective was for each student to learn and experiment with the analysis and modelling tools offered by WEKA, adapting them to specific data according to their interests or needs.
Throughout the session, students were testing different algorithms, such as classification, regression and clustering, in order to find the best fit for their data, and once an initial algorithm was selected, they focused on adjusting the parameters to improve its performance, as well as spending time interpreting the results obtained. This included analysing metrics such as accuracy, confusion matrix and other key indicators depending on the type of model used.
It allowed students not only to learn about WEKA's capabilities, but also to acquire fundamental skills in data analysis, modelling and results-based decision making. It was a dynamic class where they experienced the entire process of building, evaluating and improving machine learning models.