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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

An End-to-end Machine Learning System for Mitigating Checkout Abandonment in E-Commerce

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DOI: http://dx.doi.org/10.15439/2022F167

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 129132 ()

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Abstract. Electronic Commerce (E-Commerce) has become one of the most significant consumer-facing tech industries in recent years. This industry has considerably enhanced people's lives by allowing them to shop online from the comfort of their own homes. Despite the fact that many people are accustomed to online shopping, e-commerce merchants are facing a significant problem, a high percentage of checkout abandonment. In this study, we have proposed an end-to-end Machine Learning (ML) system that will assist the merchant to minimize the rate of checkout abandonment with proper decision making and strategy. As a part of the system, we developed a robust machine learning model that predicts if someone will checkout the products added to the cart based on the customer's activity. Our system also provides the merchants with the opportunity to explore the underlying reasons for each single prediction output. This will indisputably help the online merchants in business growth and effective stock management.


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