Open AccessA Comparative Analysis of Countries' Performance According to SDG Indicators based on Machine Learning
Author(s) -
Guilherme Augusto Dornelles de Souza,
Joséte Florêncio dos Santos,
Gabriel SantClair,
Janaína Gomide,
Luan Santos
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2021.18248
Subject(s) - sustainable development , principal component analysis , dimension (graph theory) , work (physics) , computer science , environmental economics , political science , artificial intelligence , economics , mathematics , engineering , mechanical engineering , pure mathematics , law
The Sustainable Development Goals (SDGs) are part of a global effort to reduce the impacts of climate change, promoting social justice and economic growth. The United Nations provides a database with hundreds of indicators to track the SDGs since 2016 for a total of 302 regions. This work aims to assess which countries are in a similar situation regarding sustainable development. Principal Component Analysis was used to reduce the dimension of the dataset and k-means algorithm was used to cluster countries according to their SDGs indicators. For the years of 2016, 2017 and 2018 were obtained 11, 13 and 11 groups, respectively. This paper also analyses clusters changes throughout the years.