@article{MAKHILLJEAS2020152219488,
    title = {A Random Forest Classifier for Digital Newspaper Readers},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {15},
    number = {22},
    pages = {3668-3673},
    year = {2020},
    issn = {1816-949x},
    doi = {jeasci.2020.3668.3673},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2020.3668.3673},
    author = {Adel,Enrique De and},
    keywords = {Random forest,classification,newspapers,supervised learning,recommender systems},
    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.}
    }