Sakarya University Journal of Computer and Information Sciences

Yazarlar: Serap KAZAN, Hakan KARAKOCA

Konular:-

DOI:10.35377/saucis.02.01.523139

Anahtar Kelimeler:Machine learning,Product category

Özet: With the advancement of technology and the development of the internet, the power of knowledge has come to the fore. However, in the internet world, information pollution and chaos started to emerge. Machine learning algorithms can be used to extract and interpret meaningful data from this complex. In this study, it is aimed to reach the category information of the explanation entered in the form of text. Product information from an e-commerce site was obtained by labeling the data set. This data set is modeled by machine learning algorithms and it is aimed to make accurate estimation to divide into 9 different categories. During this training, Random Forest, Decision Tree, Multinominal Naive Bayes (Multinominal NB), Logistic Regression, Support Vector Machines (SVM) and Artificial Neural Networks (ANN) classifiers were used and the results were compared with the tables.

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