Algoritmos de Aprendizaje Supervisado en la Clasificación de Exoplanetas en Python

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 o...

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Detalles Bibliográficos
Autor principal:	González Cangrejo, Johans
Otros Autores:	Orjuela Vargas, Sergio Alejandro
Formato:	Trabajo de grado (Pregrado y/o Especialización)
Lenguaje:	spa
Publicado:	Universidad Antonio Nariño 2022
Materias:	Machine Learning validación NASA exoplanetas observatorios aprendizaje supervisado, algoritmo validation exoplanets observatories supervised learning algorithm
Acceso en línea:	http://repositorio.uan.edu.co/handle/123456789/5839
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

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Sumario:	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.