New ML framework creates Global Ease of Living Index

Researchers have developed a machine learning framework to create a Global Ease of Living Index, combining socio-economic and infrastructural factors into a single score. This index aims to quantify quality of life by addressing missing data for economic indicators and using dimensionality reduction techniques like Principal Component Analysis and Factor Analysis. The framework, detailed in a recent arXiv paper, provides a practical tool for policymakers to identify areas needing improvement in major economies since 1970, with open data and code for reproducibility. AI

IMPACT Provides a reproducible tool for policymakers to assess and improve quality of life indicators in major economies.

RANK_REASON The cluster contains an academic paper detailing a new machine learning framework for creating a composite index. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• COVID-19
• factor analysis
• Global Ease of Living Index
• machine learning
• principal component analysis
• Rohitash Chandra

• paper
• product

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