Algoritmos de Aprendizaje Supervisado en la Clasificación de Exoplanetas en Python

Abstract

Currently there is a large number of databases, given the multiple sources such as: social networks, banking movements, consultations in web browsers for private, business or academic use. A clear example is the study of exoplanets carried out by NASA, through multiple sources such as ground-based observatories and space telescopes (NASA, 2021). It is important to mention that, at the time of starting this work, the aforementioned database contains 4512 confirmed planets; without a doubt, a quite important figure with enough potential to study in search of patterns and new knowledge that leads to new observations.