Abstract
Data Mining is the process of analyzing data using automated methodologies to find hidden patterns [1]. Data mining processes aim at the use of the dataset generated by a process or business in order to obtain information that supports decision making at executive levels [2] [3] through the automation of the process of finding predictable information in large databases and answer to questions that traditionally required intense manual analysis [4]. Due to its definition, data mining is applicable to educational processes, and an example of that is the emergence of a research branch named Educational Data Mining, in which patterns and prediction search techniques are used to find information that contributes to improving educational quality [5]. This paper presents a performance study of data mining algorithms: Decision Tree and Logistic Regression, applied to data generated by the academic function at a higher education institution.
Highlights
In order to apply a data mining project, in any type of scenario, it is necessary to carry out a study of the available algorithms to determine the one that best suits the needs of the project to be carried out [6]
This paper presents a performance study of data mining algorithms: Decision Tree and Logistic Regression, applied to data generated by the academic function at a higher education institution
The analysis of the performance of the Decision Tree and Logistic Regression algorithms, under the indicators of response time, CPU usage, RAM usage and accuracy, over academic indicator data, reveals that the accuracy of these algorithms is different. In this way it is established that the Decision Tree algorithm has better accuracy, due to the fact that its sample mean value is higher
Summary
In order to apply a data mining project, in any type of scenario, it is necessary to carry out a study of the available algorithms to determine the one that best suits the needs of the project to be carried out [6]. This paper presents a performance study of data mining algorithms: Decision Tree and Logistic Regression, applied to data generated by the academic function at a higher education institution. A study has been developed on the performance of Microsoft's Decision Tree and Logistic Regression algorithms, applied to the academic data of a higher education institution.
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