Algoritmos de Aprendizaje Supervisado en la Clasificación de Exoplanetas en Python
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Date
2021-11-17
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Universidad Antonio Nariño
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http://purl.org/coar/resource_type/c_7a1f
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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.
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