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Comparison of machine learning classification algorithms for purchasing forecast

Author

Listed:
  • Rabia Özdemir

    (Ä°stanbul Ticaret University,)

  • Münevver Turanlı

    (Ä°stanbul Ticaret University,)

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.

Suggested Citation

  • Rabia Özdemir & Münevver Turanlı, 2021. "Comparison of machine learning classification algorithms for purchasing forecast," JOURNAL OF LIFE ECONOMICS, Holistence Publications, vol. 8(1), pages .59-68, January.
    • Handle: RePEc:jle:journl:v:8:y:2021:i:1:p:59-68
    DOI: 10.15637/jlecon.8.1.06
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    More about this item

    Keywords

    E-commerce; Logistic Regression; Naïve Bayes; Support Vector Machines; Classification;
    All these keywords.

    JEL classification:

    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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