Predictive model of water potability through a decision tree in Artificial Intelligence
Descripción del Articulo
The objective of this work was to use the decision tree technique to define a model capable of predicting water potability. To evaluate the performance of the decision tree classification, a dataset extracted from Kaggle was used, which has 3276 water samples divided by the potability variable. Appl...
| Autores: | , , , |
|---|---|
| Formato: | artículo |
| Fecha de Publicación: | 2022 |
| Institución: | Universidad La Salle |
| Repositorio: | Revistas - Universidad La Salle |
| Lenguaje: | español |
| OAI Identifier: | oai:ojs.revistas.ulasalle.edu.pe:article/72 |
| Enlace del recurso: | https://revistas.ulasalle.edu.pe/innosoft/article/view/72 https://doi.org/10.48168/innosoft.s9.a72 https://purl.org/42411/s9/a72 https://n2t.net/ark:/42411/s9/a72 |
| Nivel de acceso: | acceso abierto |
| Materia: | Drinking water artificial intelligence decision tree Agua potable inteligencia artificial árbol de decisión |
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Predictive model of water potability through a decision tree in Artificial IntelligenceModelo predictivo de la potabilidad del agua mediante un árbol de decisión en Inteligencia ArtificialZevallos Apaza, Angel AlexisOnque Gárate, Sofía SairCanaza Cuadros, Arian Eduardo JavierChoqueneira Ccasa, Paulina MiriamDrinking waterartificial intelligencedecision treeAgua potableinteligencia artificialárbol de decisiónThe objective of this work was to use the decision tree technique to define a model capable of predicting water potability. To evaluate the performance of the decision tree classification, a dataset extracted from Kaggle was used, which has 3276 water samples divided by the potability variable. Applying the Pandas and Scikit Learn libraries, a model based on a decision tree evaluated with the metrics of precision, accuracy, completeness, and F1 score was defined, achieving 0.77, 0.80, 0.85, and 0.81, respectively.En este trabajo se planteó como objetivo utilizar la técnica de árbol de decisión para definir un modelo capaz de predecir la potabilidad del agua. Para evaluar el rendimiento de la clasificación del árbol de decisión se utilizó un dataset extraído de Kaggle que cuenta con 3276 muestras de agua divididas por la variable de potabilidad. Aplicando las librerías Pandas y Scikit Learn se logró definir un modelo basado en un árbol de decisión evaluado con las métricas de precisión, exactitud, exhaustividad y puntuación F1 logrando 0.77, 0.80, 0.85 y 0.81 respectivamente.Universidad La Salle2022-09-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionJournal papertextArtículos originalesapplication/pdftext/htmlhttps://revistas.ulasalle.edu.pe/innosoft/article/view/72https://doi.org/10.48168/innosoft.s9.a72https://purl.org/42411/s9/a72https://n2t.net/ark:/42411/s9/a72Innovation and Software; Vol 3 No 2 (2022): September - February; 121-131Innovación y Software; Vol. 3 Núm. 2 (2022): Septiembre - Febrero; 121-1312708-09352708-0927https://doi.org/10.48168/innosoft.s9https://purl.org/42411/s9https://n2t.net/ark:/42411/s9reponame:Revistas - Universidad La Salleinstname:Universidad La Salleinstacron:USALLEspahttps://revistas.ulasalle.edu.pe/innosoft/article/view/72/83https://revistas.ulasalle.edu.pe/innosoft/article/view/72/84https://purl.org/42411/s9/a72/g83https://purl.org/42411/s9/a72/g84https://n2t.net/ark:/42411/s9/a72/g83https://n2t.net/ark:/42411/s9/a72/g8420222022Derechos de autor 2022 Innovación y Softwarehttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessoai:ojs.revistas.ulasalle.edu.pe:article/722025-07-03T08:02:01Z |
| dc.title.none.fl_str_mv |
Predictive model of water potability through a decision tree in Artificial Intelligence Modelo predictivo de la potabilidad del agua mediante un árbol de decisión en Inteligencia Artificial |
| title |
Predictive model of water potability through a decision tree in Artificial Intelligence |
| spellingShingle |
Predictive model of water potability through a decision tree in Artificial Intelligence Zevallos Apaza, Angel Alexis Drinking water artificial intelligence decision tree Agua potable inteligencia artificial árbol de decisión |
| title_short |
Predictive model of water potability through a decision tree in Artificial Intelligence |
| title_full |
Predictive model of water potability through a decision tree in Artificial Intelligence |
| title_fullStr |
Predictive model of water potability through a decision tree in Artificial Intelligence |
| title_full_unstemmed |
Predictive model of water potability through a decision tree in Artificial Intelligence |
| title_sort |
Predictive model of water potability through a decision tree in Artificial Intelligence |
| dc.creator.none.fl_str_mv |
Zevallos Apaza, Angel Alexis Onque Gárate, Sofía Sair Canaza Cuadros, Arian Eduardo Javier Choqueneira Ccasa, Paulina Miriam |
| author |
Zevallos Apaza, Angel Alexis |
| author_facet |
Zevallos Apaza, Angel Alexis Onque Gárate, Sofía Sair Canaza Cuadros, Arian Eduardo Javier Choqueneira Ccasa, Paulina Miriam |
| author_role |
author |
| author2 |
Onque Gárate, Sofía Sair Canaza Cuadros, Arian Eduardo Javier Choqueneira Ccasa, Paulina Miriam |
| author2_role |
author author author |
| dc.subject.none.fl_str_mv |
Drinking water artificial intelligence decision tree Agua potable inteligencia artificial árbol de decisión |
| topic |
Drinking water artificial intelligence decision tree Agua potable inteligencia artificial árbol de decisión |
| description |
The objective of this work was to use the decision tree technique to define a model capable of predicting water potability. To evaluate the performance of the decision tree classification, a dataset extracted from Kaggle was used, which has 3276 water samples divided by the potability variable. Applying the Pandas and Scikit Learn libraries, a model based on a decision tree evaluated with the metrics of precision, accuracy, completeness, and F1 score was defined, achieving 0.77, 0.80, 0.85, and 0.81, respectively. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022-09-30 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Journal paper text Artículos originales |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://revistas.ulasalle.edu.pe/innosoft/article/view/72 https://doi.org/10.48168/innosoft.s9.a72 https://purl.org/42411/s9/a72 https://n2t.net/ark:/42411/s9/a72 |
| url |
https://revistas.ulasalle.edu.pe/innosoft/article/view/72 https://doi.org/10.48168/innosoft.s9.a72 https://purl.org/42411/s9/a72 https://n2t.net/ark:/42411/s9/a72 |
| dc.language.none.fl_str_mv |
spa |
| language |
spa |
| dc.relation.none.fl_str_mv |
https://revistas.ulasalle.edu.pe/innosoft/article/view/72/83 https://revistas.ulasalle.edu.pe/innosoft/article/view/72/84 https://purl.org/42411/s9/a72/g83 https://purl.org/42411/s9/a72/g84 https://n2t.net/ark:/42411/s9/a72/g83 https://n2t.net/ark:/42411/s9/a72/g84 |
| dc.rights.none.fl_str_mv |
Derechos de autor 2022 Innovación y Software https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Derechos de autor 2022 Innovación y Software https://creativecommons.org/licenses/by/4.0 |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf text/html |
| dc.coverage.none.fl_str_mv |
2022 2022 |
| dc.publisher.none.fl_str_mv |
Universidad La Salle |
| publisher.none.fl_str_mv |
Universidad La Salle |
| dc.source.none.fl_str_mv |
Innovation and Software; Vol 3 No 2 (2022): September - February; 121-131 Innovación y Software; Vol. 3 Núm. 2 (2022): Septiembre - Febrero; 121-131 2708-0935 2708-0927 https://doi.org/10.48168/innosoft.s9 https://purl.org/42411/s9 https://n2t.net/ark:/42411/s9 reponame:Revistas - Universidad La Salle instname:Universidad La Salle instacron:USALLE |
| instname_str |
Universidad La Salle |
| instacron_str |
USALLE |
| institution |
USALLE |
| reponame_str |
Revistas - Universidad La Salle |
| collection |
Revistas - Universidad La Salle |
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1844626634388275200 |
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13.403676 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
