SmartCart: A Personalized Product Recommendation System Based on Purchase History.

Author(s)	Ms. Ananya Saumya, Dr, Pavithra G, Dr Swapnil SN, Kavitha U R
Country	India
Abstract	In the rapidly changing e-commerce environment of today, personalized product suggestions are key to customer satisfaction and business success. This project presents SmartCart, a dynamic product recommendation system that can analyze users' purchase history and provide personalized suggestions based on their purchasing behavior. Created as part of an internship at Hewlett Packard Enterprise (HPE), SmartCart uses pattern recognition algorithms to determine best-selling items, favorite brands, and product categories. The system was implemented with Python, data science principles, Linux system utilities, and statistical analysis. The paper documents the architecture of the system, data processing process, and recommendation logic, in addition to mentioning its potential for use in other e-commerce domains and future implementation using AI and machine learning to enhance accuracy and responsiveness.
Keywords	E-commerce, Product Recommendation, Purchase Pattern Analysis, SmartCart, Personalization, Data Science, Machine Learning, Python, Customer Behavior, Recommendation System
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-30
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.43166
Short DOI	https://doi.org/g9g759

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International Journal For Multidisciplinary Research