Market-basket Analysis
Association rules using apriori algorithm
Utilizing the Apriori algorithm in a Market Basket Analysis project to mine association rules from transactional data, revealing patterns of co-occurring items and informing strategic decision-making for product placement and marketing strategies.
Client
RV College of Engineering
Services
Data Mining
Industries
Shops/Supermarkets
Date
June 2022
I initially delved into data mining literature and explored various techniques for discovering patterns in transactional datasets. Understanding the principles of association rule mining, I identified the Apriori Algorithm as a suitable choice for its efficiency in extracting frequent itemsets. I then evaluated available libraries, ultimately selecting mlxtend and pandas for implementation due to their robust functionality and compatibility.
The outcome of applying the Apriori Algorithm to the transactional dataset revealed significant associations between items frequently co-purchased by customers. Implementing these insights led to a strategic rearrangement of inventory items, optimizing product placements for improved customer convenience and contributing to maximized sales.