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

Kaliraj S., Raghavendra S., Femilda Josephin J., Sivakumar V., Karthick K.

International Journal of System of Systems Engineering, cilt.15, sa.3, ss.215-231, 2025 (Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 3
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1504/ijsse.2025.147009
  • Dergi Adı: International Journal of System of Systems Engineering
  • Derginin Tarandığı İndeksler: Scopus, PASCAL, INSPEC, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.215-231
  • Anahtar Kelimeler: classification algorithms, consumer behaviour, deep learning, e-commerce biological product, supervised machine learning
  • İstanbul Üniversitesi Adresli: Evet

Özet

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.