A Real-World Text Classification Application for an E-commerce Platform

This study aims to reflect the real-world utilities of machine learning applications by implementing a set of different text classification algorithms in terms of accuracy and performance. We developed a system capable of executing various text classification algorithms and generated models trained on real product catalog data collected from morhipo.com, an online fashion commerce platform. The highest mean accuracy rate was obtained as 96.08% (ranging between 85.44% and 99.99%) with a standard deviation value of 5.65% by Linear Support Vector Classifier (LinearSVC) algorithm.