Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
In this research, the potential of machine learning methods based on Decision Trees (DT) and Random Forest (RF) models is developed in the context of classifying readers of a digital newspaper. For this purpose, the number of visits of users to each section of the newspaper in a 6-month interval has been taken into account. The models of DT and RF developed in this study, classify the profiles of readers who access the journal with an accuracy of 98.07% and AUC value of 99.27%, thus, demonstrating that it serves as a valid tool for making strategic and operational decisions when creating, manage and present content in the user-website interaction.
How to cite this article:
Enrique De La Hoz-Domĺnguez, Adel Mendoza-Mendoza and Daniel Mendoza-Casseres. A Random Forest Classifier for Digital Newspaper Readers.
DOI: https://doi.org/10.36478/jeasci.2020.3668.3673
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2020.3668.3673