Classification of Countries' HDI Through Development Indicators

International Journal of Economics and Management Studies

© 2021 by SSRG - IJEMS Journal

Volume 8 Issue 10

Year of Publication : 2021

Authors : Zaheer Abbas, Abdul Hakim H M Mohamed, Abdur Rehman, Dr Faris Omar

10.14445/23939125/IJEMS-V8I10P101

How to Cite?

Zaheer Abbas, Abdul Hakim H M Mohamed, Abdur Rehman, Dr Faris Omar, "Classification of Countries' HDI Through Development Indicators," SSRG International Journal of Economics and Management Studies, vol. 8,  no. 10, pp. 1-7, 2021. Crossref, https://doi.org/10.14445/23939125/IJEMS-V8I10P101

Abstract:

United Nations Development Program (UNDP) annually publishes a report for the Human development index based on Health, Educational, Social and Economic development factors. This study proposed a method for classifying and predicting the human development index (HDI) using important development indicators. Kernel principal component analysis (KPCA) and k-nearest neighbour (KNN) classifiers are used for dimensionality reduction and classification. A sample of 757 Omani students was selected, of which 81.2% were female. Sixty per cent of the data used in this study was extracted from the United Nations Development Program and World Bank databases. Dimensionality reduction technique was applied to the data to overcome the over-fitting and extract the important information with a minimum loss. To address the violation of linearity assumption, Kernel principal component analysis was used for classification purposes. Correlation Matrix for development indicators, classification Report, Confusion Matrix and Classification Boundaries are constructed. The results of KNN showed 77 per cent classification accuracy in predicting any country HDI.

Keywords:

HDI, Development Indicators, Dimensions, UNDP, Supervised Learning, Unsupervised Learning, KPCA, KNN, Dimensionality Reduction.

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