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...
- Autores:
-
González Cangrejo, Johans
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2021
- Institución:
- Universidad Antonio Nariño
- Repositorio:
- Repositorio UAN
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uan.edu.co:123456789/5839
- Acceso en línea:
- http://repositorio.uan.edu.co/handle/123456789/5839
- Palabra clave:
- Machine Learning
validación
NASA
exoplanetas
observatorios
aprendizaje supervisado,
algoritmo
Machine Learning
validation
exoplanets
NASA
observatories
supervised learning
algorithm
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
| Summary: | 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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