This paper was prepared by a first-year student pursuing Data Science and Business Analytics at the University of Warsaw's Faculty of Economic Sciences. The research was conducted as part of an unsupervised learning class led by Professor Dr. hab. Katarzyna Kopczewska. This project applies association rule mining to market basket analysis to uncover latent customer purchasing patterns and product co-occurrence structures. By integrating these insights with a strategic business lens, the analysis informs high-impact decisions in cross-selling, shelf placement optimisation, promotional bundling, and targeted merchandising to enhance revenue performance and customer basket value.

This paper was prepared by a first-year student pursuing Data Science and Business Analytics at the University of Warsaw's Faculty of Economic Sciences. The research was conducted as part of an unsupervised learning class led by Professor Dr. hab. Katarzyna Kopczewska. This paper examines PCA-based image compression and chromatic analysis alongside more advanced unsupervised techniques of computational color transfer, including palette extraction and luminance-aware pixel mapping across artworks. Using RGB channel decomposition, k-means clustering, and statistical distribution matching in perceptual color spaces, the project compressed visual data while identifying and reassigning dominant chromatic structures between paintings. Collectively, the analysis demonstrates how dimensionality reduction and palette-based transfer can model, manipulate, and reinterpret pictorial color information, while also revealing the constraining role of luminance in achieving faithful cross-image color transitions.

This paper was prepared by a first-year student pursuing Data Science and Business Analytics at the University of Warsaw's Faculty of Economic Sciences. The research was conducted as part of an unsupervised learning class led by Professor Dr. hab. Katarzyna Kopczewska.