International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
HEURISTIC APPROACH IN RETAIL SECTOR BY USING MARKET BASKET ANALYSIS TECHNIQUE Abhang Mali*1, Amol Dhawale*2, Sumit Rokade*3, Shraddha Ashtekar*4, Prof. R. H. Borhade*5 1-4Students, Computer Engineering 5Head of Department of Computer Engineering Smt. Kashibai Navale College of Engineering
Pune, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - A company or organization analyzes their products to increase the product’s deployment substantially. They also dive deep into knowing customer behaviour. In the same way, Market Basket Analysis involves the analysis of products to extract their relationship with each other. This aids the retailers to know which items or itemsets would be more frequently purchased. The paper includes the implementation of Market Basket Analysis technique on various grocery datasets. It includes different methodologies/tools used, Market Basket Analysis technique system architecture, data flow diagrams, and test cases. It distinctly explains the working of the Apriori algorithm. The deployment model here, represents working of the dynamic web application. It signifies how the Apriori algorithm helps to predict frequent itemsets. The performance metrics’ outcomes exhibit the assurance of the model to be used in grocery shops to predict frequently purchased items. Key Words: Association Rule Mining (ARM), Data Mining, Frequently purchased items, FP Growth Tree algorithm, Market Basket Analysis, Shopkeepers
1. INTRODUCTION The retailers of small fields like general stores, medical stores, and grocery fields store their data either in a book(record) or in an Excel sheet. Here they invest their time and memory to analyse their present items to extract some useful insight. This insight is regarding the increase of particular products/items’ sales. Item 1
1 Book record or excel sheet
Item 2
Analysis of items
Frequently purchased items
Item n Fig1. Prediction of items without Market Basket Analysis For example – a customer goes to a grocery shop. If he/she buys bread, then it is more favorable that he/she will also buy milk or butter with it. The shopkeeper keeps these items (milk, butter, jam, etc) close to the milk item. This works on the probability of these secondary items that are to be purchased along with the primary item. Thus, finding the probability and displaying frequently purchased secondary items is what Market Basket Analysis works on.
2. OBJECTIVE To create a dynamic web application that predicts frequently purchased items with the help of the Market Basket Analysis technique.
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