Data mining techniques and multivariate analysis to discover Patterns in university final researches
Técnicas de minería de datos y análisis multivariado para descubrir patrones en investigaciones finales universitarias.
Date
2019
2019
Author
Amelec, Viloria
Rodríguez López, Jorge
García Leyva, Diana Margarita
Vargas Mercado, Carlos
Hernández-Palma, Hugo
Orellano Llinas, Nataly
Arrozola David, Mónica
Velasquez Rodriguez, Javier
Metadata
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Abstract
The aim of this study is to extract knowledge from the final researches of the Mumbai University Science Faculty. Five classification models were applied: Vector Support Machines, Neural Networks, Decision Tree, Random Forest and Powering; considering the Experiment Design and Multivariate Analysis Lines. Results showed that for the Experiment Design line, the most accurate model was Random Forest with 71.48% predictions that are correct respecting to the total. Regarding the Multivariate Analysis line, there was no significant difference in overall accuracy, fluctuating by 97%.
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