Advances and Applications in Statistics

The Advances and Applications in Statistics is an internationally recognized journal indexed in the Emerging Sources Citation Index (ESCI). It provides a platform for original research papers and survey articles in all areas of statistics, both computational and experimental in nature.

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DETERMINING THE BEST ESTIMATION MODEL WITH TREE-BASED MACHINE LEARNING METHODS: IMPLEMENTATION ON CUSTOMER SPENDINGS FOR E-COMMERCE WEBSITES

Authors

  • Mehmet Yalçin
  • Seda Bağdatli Kalkan

Keywords:

tree-based models, e-commerce, machine learning, ensemble learning.

DOI:

https://doi.org/10.17654/0972361722029

Abstract

Individuals who can easily access Internet have turned to online shopping instead of shopping in physical stores as a result of the development of technology. Individuals’ tendency for online shopping has improved the e-commerce sector. Factors such as ability to realize global sales, reduction in physical store expenses, ability to realize 24/7 sales, online and low-cost stock tracking make e-commerce important. Since they use Internet channels in purchasing, the mobility of the customers is also monitored and the behavior of the customers is reflected as data. Thus, the number of studies that predicts the purchasing behavior of customers increases along with prediction models.

Received: January 31, 2022
Accepted: March 9, 2022

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Published

24-09-2025

Issue

Section

Articles

How to Cite

DETERMINING THE BEST ESTIMATION MODEL WITH TREE-BASED MACHINE LEARNING METHODS: IMPLEMENTATION ON CUSTOMER SPENDINGS FOR E-COMMERCE WEBSITES. (2025). Advances and Applications in Statistics , 75, 91-109. https://doi.org/10.17654/0972361722029

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