ItemBook Chapter

ONLINE CUSTOMERS PURCHASE INTENTIONS CLASSIFICATION USING HYBRID XGBOOST MACHINE LEARNING MODEL AND ITS SECURITY CONCERNS

Title
ONLINE CUSTOMERS PURCHASE INTENTIONS CLASSIFICATION USING HYBRID XGBOOST MACHINE LEARNING MODEL AND ITS SECURITY CONCERNS
Creator
Prathap, Boppuru Rudra; Kumar, Kukatlapalli Pradeep; Joseph, P. Mani
Description
With technology advancing rapidly and data growing at an unprecedented rate, e-commerce businesses are gathering extensive customer informa tion. Yet, many organizations struggle to fully leverage this data. Grasping customer behaviour is vital for businesses aiming to create effective marketing strategies, boost product visibility, and drive sales. Several factors affect consumer purchasing decisions, including search history, time spent viewing products, bounce rates, and demographic details like age, gender, and income. This study investigates the key elements influencing customer behaviour and investigates data mining techniques to enhance revenue predictions. A major challenge in e-commerce is converting website traffic into actual sales. Attracting visitors alone does not ensure revenue. To tackle this issue, classification techniques are used to assess the likelihood of a purchase based on user interactions. Furthermore, while artificial intel ligence and machine learning have revolutionized e-commerce analytics, security issues persist. Adversarial attacks on machine learning models can create risks, making security a critical consideration. This research offers a thorough analysis of classification techniques for evaluating business revenue generation. It also examines ways to improve model accuracy while addressing security weaknesses. The findings aim to enhance e-commerce decision-making, customer targeting, and overall business growth. 2026 by Apple Academic Press, Inc.
Source
Attacks on Artificial Intelligence: The New Facets of Cyber Ecospace;pp.137-154
Date
01-01-2025
Publisher
Apple Academic Press
Coverage
Prathap M.R., Department of Computer Science and Engineering, CHRIST (Deemed to be University, Bangalore, India; Kumar K.P., Department of Computer Science and Engineering, CHRIST (Deemed to be University, Bangalore, India; Joseph P.M., Department of Computer Science, Modern College of Business and Science, Muscat, Oman
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-104009235-4; 978-177491622-3;
Format
online
Language
English
Type
Book chapter

Citation

Prathap, Boppuru Rudra; Kumar, Kukatlapalli Pradeep; Joseph, P. Mani, “ONLINE CUSTOMERS PURCHASE INTENTIONS CLASSIFICATION USING HYBRID XGBOOST MACHINE LEARNING MODEL AND ITS SECURITY CONCERNS,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25288.

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