SavedAdd your journal

Journals / Journal of Life Economics / 2021 / Cilt: 8 - Sayı: 1

Comparison of machine learning classification algorithms for purchasing forecast

Rabia ÖZDEMİR
Münevver TURANLI
Pages
59–68
Publication Date
2021-01-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With the development of computer technologies and invention of internet, many concepts have entered our lives.
📊
Key Findings & Data: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.
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:E-commerce · Logistic Regression · Naïve Bayes

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.

Özet

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.

🔖

Cite This Article

ÖZDEMİR, R., TURANLI, M. (2021). Comparison of machine learning classification algorithms for purchasing forecast. Journal of Life Economics, Cilt: 8 - Sayı: 1, 59–68. https://ajindex.com/articles/115233

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. ALAN, A. & KARABATAK, M. (2020). Veri Seti - Sınıflandırma İlişkisinde Performansa Etki Eden Faktörlerin Değerlendirilmesi. Fırat Üniversitesi Müh. Bil. Dergisi. 32(2), 531-540.
  2. ALPAYDIN, E. (2010).
  3. Introduction to Machine Learning, United States of America. Massachusetts Institute of Technology. Second Edition, ISBN-13: 978-0-262-01243-0.
  4. ALPAYDIN, E. (2016). Machine learning: The New AI, United States of America. Massachusetts Institute of Technology. Fist Edition, ISBN-13: 978-0262529518.
  5. AYDIN, C. (2018). Makine Öğrenmesi Algoritmaları Kullanılarak İtfaiye İstasyonu İhtiyacının Sınıflandırılması. European Journal of Science and Technology. 14, 169-175.
  6. BAGUI, S., FANG, X., KALAIMANNAN, E., BAGUI, S.C. & SHEEHAN, J., (2017). Comparison of Machine-Learning Algorithms for Classification of VPN Network Traffic Flow Using Time-Related Features. Journal of Cyber Security Technology, https://doi.org/10.1080/23742917.2017.1321891, 1(2), 108-126.
  7. BARTLETT, J. (2015). The Dark Net: Inside the Digital Underworld, United States of America. Melville House, ISBN: 978-1-61219-489-9.
  8. BOZKIR, A.S, SEZER, E. & GÖK, B. (2009). Öğrenci Seçme Sınavında (ÖSS) Öğrenci Başarımını Etkileyen Faktörlerin Veri Madenciliği Yöntemleriyle Tespiti. 5th International Advanced Technologies Symposium, 13-15
  9. May, Karabük, 1-7.
  10. COLEMAN, M.J. & GANONG, L.H. (2014). The Social History of the American Family: An Encyclopedia. United States of America: Sage Publications. COLLEY, W., (2019), Comparison of Machine Learning Algorithms For Financial Evaluations. Thesis (MS), Kocaeli: Gebze Technical University. CORNELIUS, P., KIRKMAN, G., SACHS, J. & SCWAB, K. (2002). Country Profiles. The Global Information Technology Report 2001-2002:
  11. Readiness for the Networked World, New York, Oxfod: Oxford University Press.
  12. ÇELIK, B. (2015). An Exploratory Analysis of Online Shopping Behavior in Turkey. Thesis (MS), Bahçeşehir University, Istanbul. ÇELİK, B. & ERTEMEL, A.V. (2016). An Exploratory Analysis of Online Shopping Behavior in Turkey. International Journal of Commerce and Finance, 2(1), 67-80.
  13. GÜNAY, M. (2018). Makine Öğrenmesiyle Müşteri Kayıplarının Tahmini. Thesis (MS), Istanbul University, İstanbul.
  14. HAN, J., KAMBER, M. & PEI, J. (2012). Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers.
  15. KANTARCI, Ö., ÖZALP, M., SEZGİNSOY, C., ÖZAŞKINLI, O. & CAVLA, C. (2017). Dijitalleşen Dünyada Ekonominin İtici Gücü: E-Ticaret, TÜSİAD Publication, ISBN: 978-605-165-022-7, April, 04-587. KAPLAN