| dc.coverage.spatial | Colombia | spa |
| dc.creator | Sabogal, Hermes | |
| dc.creator | García-Bedoya, Olmer | |
| dc.creator | Granados, Oscar M. | |
| dc.date.accessioned | 2021-11-09T17:30:32Z | |
| dc.date.available | 2021-11-09T17:30:32Z | |
| dc.date.created | 2021 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12010/22282 | |
| dc.description.abstract | El articulo analiza la pobreza en Colombia utilizando herramientas de aprendizaje automático supervisado a partir de los datos de Hogares, Personas y Vivienda del DANE para el periodo 2016 a 2019. Se examina la percepción de factores que influyen en la pobreza teniendo en cuenta las especificidades estructurales que conforman la medición de la pobreza, como la salud, el trabajo y la educación. El aporte de esta investigación es comparar el Índice de pobreza multidimensional con los factores relevantes de la situación de pobreza mediante el uso de herramientas aprendizaje automático. Los hallazgos revelan que el algoritmo XGBoost identifica los indicadores que causan la pobreza y permite proponer un marco de trabajo para lucha contra la pobreza. Palabras Clave: Aprendizaje automático, Medición y análisis de la pobreza, construcción de modelos y estimación, cambios tecnológicos. | spa |
| dc.format.extent | 32 páginas | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.language.iso | spa | spa |
| dc.publisher | Universidad de Bogotá Jorge Tadeo Lozano | spa |
| dc.source | instname:Universidad de Bogotá Jorge Tadeo Lozano | spa |
| dc.source | reponame:Expeditio Repositorio Institucional UJTL | spa |
| dc.subject | Brecha digital | spa |
| dc.subject | Desigualdad social | spa |
| dc.title | Un análisis de la pobreza en Colombia basado en aprendizaje automático | spa |
| dc.type.local | Trabajo de grado de maestría | spa |
| dc.subject.lemb | Pobreza -- Investigaciones -- Tesis y disertaciones académicas | spa |
| dc.subject.lemb | Necesidades básicas -- Tesis y disertaciones académicas | spa |
| dc.subject.lemb | Derecho al acceso a Internet -- Tesis y disertaciones académicas | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.type.hasversion | info:eu-repo/semantics/acceptedVersion | spa |
| dc.rights.local | Abierto (Texto Completo) | spa |
| dc.identifier.repourl | http://expeditio.utadeo.edu.co | spa |
| dc.creator.degree | Magíster en Ingeniería y Analítica de Datos | spa |
| dc.publisher.program | Maestría en Ingeniería y Analítica de Datos | spa |
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| dc.description.hashtag | #BrechaDigital | spa |
| dc.description.abstractenglish | The article analyzes poverty in Colombia using supervised machine learning tools from DANE’s date of Households, People, and Housing for 2016 to 2019. The article examines the perception of factors that influence poverty, considering the structural specificities that make up the poverty measurement, such as health, work, and education. The contribution of this research is to compare the Multidimensional Poverty Index with the factors that are relevant to the poverty situation using machine learning tools. The findings reveal that the XGBoost algorithm identifies indicators that cause poverty and allows proposing a framework to fight poverty. Keywords: Machine Learning, Measurement and Analysis of Poverty, Computational Techniques; Simulation Modeling, Model Construction and Estimation, Technological | spa |
| dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
