This study aims to perform dimensionality reduction on a dataset containing physical properties of superconducting materials. Since the dataset includes 81 features, many of which are likely correlated, dimensionality reduction provides an effective way to simplify the dataset and serves as a strong starting point for further analysis. To achieve this, Principal Component Analysis will be employed. PCA is a linear technique that transforms the original variables into a smaller set of uncorrelated variables, known as principal components, while retaining as much variance as possible from the original dataset.

The primary goal of this study is to identify similarities among European countries in terms of their level of development. Two key variables are used for this purpose. The first, GDP per capita, reflects a country’s economic and financial prosperity. The second, the Human Development Index (HDI), captures the quality of human capital, including factors such as life expectancy and years of education. Together, these variables provide a comprehensive view of a country’s development level. To explore patterns in the data, multiple clustering algorithms will be applied: k-means, PAM (Partitioning Around Medoids) and hierarchical clustering (both agglomerative and divisive approaches). Using different methods will help verify the robustness of the results and uncover meaningful groupings among European countries.