Welcome to our Teaching Aide !
Overview
Association rule mining is a key data mining technique used to discover relationships between items in large transactional datasets. This project focuses on the Apriori algorithm, applying it to a grocery transaction dataset from Kaggle to extract meaningful insights into customer purchasing behavior. By identifying frequently co-purchased items, businesses can optimize retail strategies, marketing efforts, and inventory management. Additionally, we explore the challenges and privacy implications associated with the Apriori algorithm. Furthermore, we analyze a case study on cancer medications, demonstrating how the Apriori algorithm was implemented to uncover critical data patterns in patient treatment and decision-making. Association rule mining is a fundamental technique in data mining that helps uncover associations between items in large transactional datasets.
Walk through
Link To Our Document :
Link to Our Implementation of Apriori Algorithm
https://github.com/kaushik9038/arm_project
Meet our Team :
Kaushik Mazumder
Krupali Kanubhai Patel
Rupesh Kowtharapu
Ready to apply your knowledge from the Teaching Aide ?
- This study examines the ethical and privacy dangers that emerge when healthcare data analysis utilizes the Apriori algorithm as demonstrated by the “Head and Neck Cancer Medications” case study. How can these risks be mitigated?
- The Apriori algorithm employs a “bottom-up” method to discover frequent itemsets. Describe how this procedure operates and discuss the reasons for its computational expense when processing large datasets.
- What preprocessing steps are required in the Jupyter Notebook before applying the Apriori algorithm to the grocery transaction dataset?
Find the Answers here :
