Implementación de CRISP-DM como herramienta de apoyo para la toma de decisiones en análisis descriptivos de contaminantes del aire y variables meteorológicas

Business intelligence better known as Business Intelligence is the set of methodologies and techniques for exploring data. The CRISP-DM methodology provides the necessary steps to carry out a data mining project. Data mining attempts to discover information in data sets and air quality is the levels that indicate the pollutants present in the air. To analyze air quality monitoring data, different countries have been applying data analysis methodologies and techniques, this has allowed them to have useful information to create pertinent actions. These actions are becoming more and more necessary, especially in urban centers where more and more problems with air quality arise for reasons such as overcrowding. In order to have useful information to support these actions, the CRISP-DM methodology guides through a set of sequential and dynamic steps, the way in which the application of data mining techniques should be carried out, with which possible relationships are obtained between pollutants and also meteorological variables. For all of the above, it is necessary for the university to begin in the study and application of data analysis methodologies and techniques and this is what in this work will be done to the air quality data published on the SIATA website.

• Autores: Monsalve Cardona, Marcela Cristina
• Fecha de publicación: 2021
• Institución: Universidad de San Buenaventura
• Repositorio: Repositorio USB
• Idioma: spa
• OAI Identifier: oai:bibliotecadigital.usb.edu.co:10819/8648
• Acceso en línea: http://hdl.handle.net/10819/8648
• Palabra clave: Business Intelligence; CRISP-DM; Data mining; calidad del aire; air quality; Toma de decisiones; Gestión de negocios
• License: Atribución-NoComercial-SinDerivadas 2.5 Colombia