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This study uses Principal Component Analysis and clustering methods to identify countries performing poorly towards the United Nations' 17 sustainable development goals. It looks at 193 countries and their progression towards these goals. The data set has 32 variables that relate to sustainable development targets.

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chloe-s-wang/2021-03-27-ML-Python-Segmentation-Clustering-of-UN-Sustainability-Development-Goals

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2021-03-27-ML-Python-Segmentation-Clustering-of-UN-Sustainability-Development-Goals

This study uses Principal Component Analysis and clustering methods to identify countries performing poorly towards the United Nations' 17 sustainable development goals. It looks at 193 countries and their progression towards these goals using various indicators (examples: https://unstats.un.org/sdgs/indicators/database). The data set has 32 variables that relate to sustainable development targets, which can be downloaded at https://www.sdgindex.org/.

Copyright © 2021, Chloe Wang

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This study uses Principal Component Analysis and clustering methods to identify countries performing poorly towards the United Nations' 17 sustainable development goals. It looks at 193 countries and their progression towards these goals. The data set has 32 variables that relate to sustainable development targets.

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