This project applies the Apriori algorithm on the Instacart dataset to uncover frequent item associations. Using the arules package in R, we analyze transaction data to identify product combinations often purchased together. The findings provide insights for personalized recommendations and targeted marketing. Visualizations of the rules highlight strong associations, helping businesses optimize product placements and promotions.

Probabilidad y estadística- Caso 03

Combined method of dimension reduction and clustering techniques for Unsupervised Learning Final Project, University of Warsaw

Probabilidad y estadística- Caso 02

Probabilidad y estadística - Caso 01

Early Detection of Mental Health Risks Using Association Rule Miningnt