Usluoğlu, SedatKılınç, DenizBozyiğit, Fatma2025-03-212025-03-2120212757-9778https://hdl.handle.net/20.500.14034/2768E-commerce platforms need to have a well-managed online product catalog to make products easily accessible. However, the organization of catalog and categorization of products can be time-consuming due to the large volume of product data in e-commerce. In this direction, our study aims to develop an accurate categorization of product data with the adoption of big data analytics. Accordingly, various machine learning algorithms (Support Vector Machine, Naive Bayes, and Stochastic Gradient Descent) were utilized to organize online catalogs from Spark MLLib. Performed classifiers were trained and tested on product catalog data collected from a fashion retailer in Turkey, Boyner Group, which combines cutting-edge digital services with a vast network of exciting stores.eninfo:eu-repo/semantics/openAccesse-commerce platformsproduct categorizationmachine learningbig data analytic toolsArticle128-Jan