This project applies association rule mining to a subset of the InBillo dataset in order to identify interpretable combinations of business characteristics associated with low customer trust. The analysis focused on non-score attributes such as firm age, size, legal form, financial transparency (debts) and online presence, using the Apriori algorithm to identify and extract repetitive patterns.

This project applies unsupervised learning to identify distinct and interpretable player importance profiles in the Spanish ACB league in the 2024-25 season, using engineered performance features that go beyond traditional box-score evaluations. By combining clustering and principal component analysis, the study examines whether dimensionality reduction clarifies and stabilizes the structure of player importance without altering the underlying groupings.