Title: Predicting consumer's intention of biological products using e-commerce data

Authors: S. Kaliraj; S. Raghavendra; J.S. Femilda Josephin; V. Sivakumar; K. Karthick

Addresses: Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India ' Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India ' Faculty of Engineering and Natural Science, Computer Engineering, Istinye University, Istanbul, 34010, Turkey ' Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India ' Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Thottiam, 621215, Tamilnadu, India

Abstract: Digitalisation has evolved as a boon to the e-commerce market. Biological products and organic products also target e-commerce platforms to increase their business. E-commerce has the upper hand over traditional marketing practices due to its adequate accessibility and usability. The research revolves around consumers' opinions in the form of ratings and the idea that the products sold on e-commerce platforms correlate with the product's rating and features like brand, price, etc. This lets the practitioners predict the consumers' intention by predicting the possible rating. There are many approaches available to predict consumer intention based on e-commerce data. In this paper, we have evaluated the performance of all the machine learning classification algorithms. All of these are used in our proposed structure to predict consumer intention on a product. Here we trained machine learning algorithms using an extracted dataset for forecasting biological product ratings based on other product features. Performance of different machine learning algorithms on e-commerce data discussed using metrics.

Keywords: supervised machine learning; consumer behaviour; classification algorithms; e-commerce biological product; deep learning.

DOI: 10.1504/IJSSE.2025.147009

International Journal of System of Systems Engineering, 2025 Vol.15 No.3, pp.215 - 231

Received: 01 Apr 2023
Accepted: 23 Jun 2023
Published online: 10 Jul 2025 *

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