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Estudio de predictores de felicidad a nivel mundial

Authors: Jimbo Granda, María Augusta;

Estudio de predictores de felicidad a nivel mundial

Abstract

El objetivo principal de desarrollar este proyecto es el de descubrir factores clave que hacen a la gente más feliz, para lo cual se ha diseñado y construido un modelo de minería de datos. Los datos que se consideran en el desarrollo de este proyecto han sido obtenidos de encuestas históricas correspondientes a los años 2015, 2016 y 2017 en más de 100 países a nivel mundial. Los seis principales factores que constan en cada uno de los datasets y que sirven para evaluar la felicidad en cada país son: producción económica, apoyo social, esperanza de vida, libertad, ausencia de corrupción y generosidad. Para el análisis de los datos del informe se utiliza técnicas de machine learning y minería de datos, la programación se ha desarrollado en el lenguaje R. Según los resultados obtenidos, se concluye o da respuesta a las siguientes preguntas clave: ¿Cuáles son los principales factores que contribuyen a la felicidad? ¿Existen diferencias importantes en dichos factores entre países? ¿Existen diferencias de felicidad en los tres años? ¿Existen relaciones entre las distintas regiones según el nivel de felicidad? - ¿En qué región se encuentran los países más felices y menos felices del mundo?.

L'objectiu principal de desenvolupar aquest projecte és el de descobrir factors clau que fan a la gent més feliç, per la qual cosa s'ha dissenyat i construït un model de mineria de dades. Les dades que es consideren en el desenvolupament d'aquest projecte han estat obtinguts d'enquestes històriques corresponents als anys 2015, 2016 i 2017 en més de 100 països a nivell mundial. Els sis principals factors que consten en cadascun dels datasets i que serveixen per avaluar la felicitat a cada país són: producció econòmica, suport social, esperança de vida, llibertat, absència de corrupció i generositat. Per a l'anàlisi de les dades de l'informe s'utilitza tècniques de machine learning i mineria de dades, la programació s'ha desenvolupat en el llenguatge R. Segons els resultats obtinguts, es conclou o dóna resposta a les següents preguntes clau: Quins són els principals factors que contribueixen a la felicitat?- Hi ha diferències importants en aquests factors entre països? - Hi ha diferències de felicitat en els tres anys? - Existeixen relacions entre les diferents regions segons el nivell de felicitat? - En quina regió es troben els països més feliços i menys feliços del món ?.

The main objective of developing this project is to discover key factors that make people happier, for which a data mining model has been designed and built. The data considered in the development of this project have been obtained from historical surveys corresponding to the years 2015, 2016 and 2017 in more than 100 countries worldwide. The six main factors that appear in each of the datasets and that serve to assess happiness in each country are: economic production, social support, life expectancy, freedom, absence of corruption and generosity. For the analysis of the data of the report, machine learning and data mining techniques are used, the programming has been developed in the R language. According to the results obtained, the following key questions are concluded or answered: What are the main factors that contribute to happiness?- Are there important differences in these factors between countries? - Are there differences in happiness in the three years? - Are there relations between the different regions according to the level of happiness? - In which region are the happiest and least happy countries in the world?.

Country
Spain
Related Organizations
Keywords

factors clau, factores clave, Mineria de dades -- TFM, key factors, mineria de dades, Minería de datos -- TFM, minería de datos, happiness, data mining, felicidad, felicitat, Data mining -- TFM

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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Green