Bibliographic citations
Atalaya, K., Flores, N., Flores, Á. (2019). Propuesta de analytics a los patrones de comportamiento en el proceso de clasificación socioeconómica en el MIDIS [Trabajo de investigación, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/625611
Atalaya, K., Flores, N., Flores, Á. Propuesta de analytics a los patrones de comportamiento en el proceso de clasificación socioeconómica en el MIDIS [Trabajo de investigación]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2019. http://hdl.handle.net/10757/625611
@mastersthesis{sunedu/4380679,
title = "Propuesta de analytics a los patrones de comportamiento en el proceso de clasificación socioeconómica en el MIDIS",
author = "Flores Alvarado, Ángela",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2019"
}
The socioeconomic classification process made by Ministry of development and social inclusion (MIDIS), allows socioeconomic level assignment to households and their members, mainly through household assessment mechanisms base on their socioeconomic, demographic and social characteristics. This evaluation allows it possible to identify households and people in situations of poverty, vulnerability or exclusion, as potential beneficiaries of State programs and subsidies. The application of analytics and data mining, through techniques such as clustering, for the search of patterns by identifying clusters with similar characteristics, and classification, to catalog new records, allowed determining the socioeconomic classification of households; as well as establishing the socioeconomic classification of new household registers. For the application of these data mining techniques, the CRISP-DM methodology was used. Used Information was collected from the information collection formats of the characteristics of the households and the most relevant variables were considered for the model construction. The purpose of this paper is to find a pattern of behavior for the socioeconomic classification process, through the application of Analytics, seeking to contribute to the reduction of the percentage of households that receive support from social programs without corresponding them, in order to have an effective social support policy.
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