COMPARISON OF MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR PURCHASING FORECAST

Rabia ÖZDEMİR; Münevver  TURANLI

doi:10.15637/jlecon.8.1.06

Authors

Rabia ÖZDEMİR İstanbul Ticaret Üniversitesi/TÜRKİYE 0000-0002-2774-6799
Münevver TURANLI İstanbul Ticaret Üniversitesi/TÜRKİYE 0000-0002-9535-4527

10.15637/jlecon.8.1.06

Abstract

With the development of computer technologies and invention of internet, many concepts have entered our lives. With the starting of wide usage of globalized internet network, concept of machine learning has emerged in time for smarter management of data flow in big dimensions. In line with technological developments, all activities began to be carried to digital environment and as a result of this, concept of e-commerce has entered our lives. E-commerce is one of the areas where machine learning is used most widely. By examining product purchasing situations in accordance with data available at the enterprises, various researches have been made for selection of most appropriate model in order to predict future data. In the study it was mentioned about concepts of e-commerce and machine learning and by applying Logistic Regression, Naïve Bayes and Support Vector Machines being machine learning classification algorithms, it has been aimed to determine the model having best accuracy ratio.

JEL codes: L81, C11, C38, C39, C53

Keywords:

e-commerce Lojistik Regression Naive Bayes Support Vector Machines Classification

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COMPARISON OF MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR PURCHASING FORECAST

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