National Power Index

In this study, data were collected on 7 main components (demographic, geographic, economic, military, political, psycho-social, and technological power) and 84 sub-components to evaluate national power indicators. The data were obtained from official databases of international organizations such as the World Bank, the United Nations, the International Monetary Fund (IMF), and similar sources. The collected data were first standardized through processes such as handling missing values and normalization. Subsequently, Principal Component Analysis (PCA) was performed using the Python programming language. PCA was used to reduce the dimensionality of the large-scale dataset, identify principal components, and measure the contribution of each component to the national power index. As a result of the analysis, variance ratios and component loadings were calculated, determining the factors that best represent national power. The dataset enables researchers to conduct comparative analyses of national power components and carry out similar studies. The processes of data collection, processing, and analysis are explained in detail in the article. This dataset can serve as a foundational resource for calculating national power indices and conducting cross-country comparisons.