Poker Hand Valuator

• Autores: Vargas Quispe, Estith Bryan, Macedo Pillco, Eybert, Quispe Ttito, Juan Carlos, Cano Vilcapaza, Jose Miguel
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad La Salle
• Repositorio: Revistas - Universidad La Salle
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.ulasalle.edu.pe:article/157
• Enlace del recurso: https://revistas.ulasalle.edu.pe/innosoft/article/view/157; https://doi.org/10.48168/innosoft.s15.a157; https://purl.org/42411/s15/a157; https://n2t.net/ark:/42411/s15/a157
• Nivel de acceso: acceso abierto
• Materia: Dataset; poker hand; poker texas; prediction; neural network; dataset; predicción; red neuronal

Our objective is the prediction of poker hands, the probability that you can draw a good hand when you make 5 steals from a 52-card poker deck. We apply neural networks to make this prediction together with different libraries that help the process is more simplified and the results are more reliable, therefore in the use of this methodology we managed to obtain an average of 97% in most cases with a deviation of 2.5% which we consider acceptable due to the very unbalanced amount of the data from this dataset, therefore this artificial intelligence method helps us predict new hands and make better decisions as you find yourself in a game situation.