Published on: April 2026
PURCHASE PATTERN ANALYTICS USING MARKET BASKET ANALYSIS FOR RETAIL INSIGHTS
Dnyaneshwari V. Nanekar
Prof. Kanif Satav
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Abstract
Understanding how customers purchase products has become very important in today’s retail environment, where businesses aim to improve sales and customer experience through data-driven strategies. With large volumes of transaction data being generated every day, it is possible to identify patterns in customer buying behaviour that were not easily visible earlier. These patterns can help retailers make better decisions related to product placement, cross-selling, and inventory management.
In this project, we analyze customer purchase behaviour using transactional retail data. Each transaction represents a basket of items purchased together, which allows us to study how different products are related to each other. The dataset was first cleaned and transformed to remove inconsistencies, handle missing values, and structure it into a basket format suitable for analysis.
To identify product associations, the Apriori algorithm was applied. This method helps in finding frequently occurring itemsets and generating association rules based on support, confidence, and lift. These metrics help in understanding how strongly products are connected and how likely they are to be purchased together.
How to Cite this Paper
Nanekar, D. V. (2026). Purchase Pattern Analytics using Market Basket Analysis for Retail Insights. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.705
Nanekar, Dnyaneshwari. "Purchase Pattern Analytics using Market Basket Analysis for Retail Insights." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.705.
Nanekar, Dnyaneshwari. "Purchase Pattern Analytics using Market Basket Analysis for Retail Insights." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.705.
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- •Published on: Apr 25 2026
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