The analysis starts with an OLS model to explain salary variation using demographic and organizational factors. Diagnostic tests revealed issues such as influential observations, heteroscedasticity, non-normal residuals, and multicollinearity. To address these limitations, robust regression and regularized models (Ridge, Lasso, and Elastic Net) were applied. The results showed that regularization improved predictive performance and model stability, with Elastic Net providing the best balance between accuracy, robustness, and interpretability.

This report applies **Market Basket Analysis (MBA)**, a data mining technique traditionally used in retail to study product co-occurrence in shopping carts, to uncover hidden patterns within successful team compositions in League of Legends Season 11. The core analytical concept treats each winning team composition as a single transaction: the five champions played by the winning team in a match form a "basket," and individual champions are the "items" inside it. The goal is to identify sets of champions that statistically appear together most frequently in matches resulting in a victory. Connected to: https://rpubs.com/Marta_B/Lol_Champion_ClustersiDimension