Comparison of machine learning classification algorithms for purchasing forecast

Rabia Özdemir and Münevver Turanlı ()
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Rabia Özdemir: İstanbul Ticaret University,
Münevver Turanlı: İstanbul Ticaret University,

JOURNAL OF LIFE ECONOMICS, 2021, vol. 8, issue 1, .59-68

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.

Keywords: E-commerce; Logistic Regression; Naïve Bayes; Support Vector Machines; Classification (search for similar items in EconPapers)
JEL-codes: C11 C38 C39 C53 L81 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:jle:journl:v:8:y:2021:i:1:p:59-68

DOI: 10.15637/jlecon.8.1.06

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