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Exploratory analysis and selection of relevant attributes on the saber 11 test dataset for the city of Cartagena
This work aims to develop a study based on both exploratory data analysis and the selection of the best attributes that affect academic performance, using the data set of the 2019 Saber 11 tests from the city of Cartagena. To develop the study, an adaptation of the SEMMA data mining methodology was used, defining four methodological phases, namely: F1. Data sampling; F2. Exploration and modification of data; F3. Application of attribute selection method; and F4. Analysis of the obtained results. As relevant results of the study, it was obtained that the areas with the highest averages were critical reading and mathematics. Likewise, it was evidenced that parent training at the postgraduate level has a representative influence on student performance. Finally, a set of dataset attributes that affect the performance of the five areas of the test were identified. This study aims to serve as a reference at a research level for the characterization of academic performance in different regions, to contribute to the development of strategies focused on strengthening educational quality.