Application of machine learning for predictions of consecutive dependent data of type {[(a, b) → c] →− d} - Diego Alexander Quevedo Piratova; Jhon Uberney Londoño Villalba; Arnaldo Andres Gonzalez Gomez (Tecnura)
Objective: Machine learning techniques have emerged in response to the desire for automatic pattern detection within datasets in fields such as statistics, mathematics, and data analytics. They allow for the extraction of relevant information from datasets of significantly large volumes, providing the possibility of making predictions. This paper presents an application focused on decision trees, linear regression, and random forest regression algorithms to predict final data from consecutive dependent data of type {[(a, b) → c] → D}. Methodology: The study adopts a quantitative research design, which takes as input datasets based on interval data. It utilizes a correlational research model by implementing Python and its Scikit-Learn library, which includes various algorithms for prediction. Specifically, we compare the application of decision trees, linear regression, and random forest regression on the same set of datasets, but with a characteristic of dependency between them. Results: Upon application of the proposed model, it yields an estimated prediction score, which indicates the accuracy of the model concerning the data provided Conclusions: The application of a complex algorithm does not inherently guarantee a higher rate of accuracy. Conversely, configuring the model correctly, training multiple trees, or adjusting parameter values can significantly enhance the obtained results. Financing: Unified National Corporation for Higher Education (CUN).