This project presents an exploratory analysis of association rules using the Groceries transaction dataset available in the arules package in R. The goal of the analysis is to identify patterns of products that are frequently purchased together in retail transactions. Association rules were generated using the Apriori algorithm, with rule quality evaluated using the standard measures of support, confidence, and lift. The analysis reveals several strong relationships between product categories, particularly among fresh food items such as fruits and vegetables, as well as among dairy products. The results illustrate how market basket analysis can uncover meaningful purchasing patterns and provide insights that may support retail decision-making, such as product placement, promotional strategies, and recommendation systems.

Exploratory analysis of wine characteristics using clustering methods and dimensionality reduction techniques.