The paper focuses on applying association rules to market basket analysis, utilizing the Apriori Algorithm. The findings are presented through tables and plots. This work was completed as part of the Unsupervised Learning course at the Faculty of Economic Sciences, University of Warsaw.

The paper addresses dimensionality reduction based on Pokémon statistics and characteristics. For this purpose, the PCA (Principal Component Analysis) algorithm was utilized. The appropriate number of dimensions was adjusted, and illustrative visualizations of the obtained results were presented. This work was completed as part of the Unsupervised Learning course at the Faculty of Economic Sciences, University of Warsaw.

The paper explores the dominant color palettes in spring and autumn, using a park in Suwałki, Poland as a case study. The study employed image clustering methodology, specifically clara, and utilized the silhouette index. This work was completed as part of the Unsupervised Learning course at the Faculty of Economic Sciences, University of Warsaw.