Implementation of Machine Learning Algorithms for analysing database of grocery store of Ludhiana district

Authors

  • Navjot Kaur Student, Department of Computer Applications, PCTE Institute of Engineering and Technology, Ludhiana, Punjab, India
  • Sourav Joshi Student, Department of Computer Applications, PCTE Institute of Engineering and Technology, Ludhiana, Punjab, India

Keywords:

Market Basket Analysis, Apriori, Grocery, matplotlib

Abstract

This study focuses on analysing grocery transaction data using Market Basket Analysis (MBA) to uncover purchasing patterns and improve retail strategies. The process begins with the collection of grocery transaction data, followed by the importation of necessary Python libraries such as `pandas`, seaborn`, and `matplotlib` for data manipulation, analysis, and visualisation. Using these libraries, association rules are generated through popular algorithms like Apriori to identify frequently co-purchased items. The results of this analysis are then visualised through graphs, providing clear insights into consumer buying behaviour. The findings are aimed at optimising sales trends over time, sales by item category, payment method usage and supporting targeted marketing strategies in the grocery retail sector. The findings depict the effectiveness of MBA in optimising retail operations and enhancing customer satisfaction, ultimately contributing to more efficient and targeted marketing efforts in the grocery retail sector.

References

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Published

2026-04-27

Issue

Vol. 7 No. 2 (2025): Journal of Advanced Research in Modeling and Simulation

Section

Research Article

Copyright (c) 2026 Journal of Advanced Research in Modeling and Simulation

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