Article for analyzing data related to Covid-19 associated with Machine Learning

Abstract

The main objective of this article is to carry out and present the results of a comparative study using Machine Learning algorithms applied to a database collected from students about covid-19. In this study, different types of behavior of the selected algorithms will be demonstrated and their respective successes and errors in each scenario will be demonstrated, using Weka as the base software, which has different algorithms for the application of comparatives. The Lazy Kstar classifier algorithm, Naive Bayes Multinomial Text, J48 Tree, Random Tree and Random Forest Tree will be used for the tests.