Open AccessApproach to Kirana Store Product Arrangement Using Machine Learning
Author(s) -
Ajita K. Patel,
Krishna Kumar Tiwari
Publication year - 2021
Publication title -
asian journal of research in computer science
Language(s) - English
Resource type - Journals
ISSN - 2581-8260
DOI - 10.9734/ajrcos/2021/v11i430271
Subject(s) - product (mathematics) , computer science , domain name , association rule learning , object (grammar) , association (psychology) , grocery store , world wide web , business , advertising , artificial intelligence , the internet , mathematics , philosophy , geometry , epistemology
Market Basket Analysis (MBA) is a method for determining the association between entities, and it has often been used to study the association between products in a shopping basket. Trained Computer vision models are able to recognize objects in photos so accurately that it can even outperform humans in some instances. This study shows that combining objective detection techniques with market basket analysis can assist Stores/Kirana in organizing the products effectively. With the use of MBA and Object detection, we formulated recommendations for store arrangements along with putting a recommendation engine on top to help shoppers. After deploying this to local Kirana stores, the Kirana store was able to see an increase of 7% in the sale. The recommendation engine performed better than just the domain knowledge of the kirana store.