Research Article
BibTex RIS Cite

Price estimation of secondhand cars sold on the internet with artificial neural network method

Year 2020, , 49 - 61, 01.06.2020
https://doi.org/10.34231/iuyd.698095

Abstract

Data mining is an important field of study for e-commerce firms. With the overwhelming amount of available data, that increases exponentially it is inevitable to use data mining methods. Because cost of a good or a service, potential of selling the product, potential price of the product etc. can be estimated by these methods with enough variables. No matter which method we use it must be cost and time efficient. For this reason, we must use data mining methods to make accurate and immediate estimations without any delays. In this work we tried to estimate prices of the vehicles in the secondhand car market with artificial neural network method. For this we used six step process to solve this problem. Problem definition was made by using data mining stages, in the first stage data cleaning was done in data preparation, in second stage we arranged data for the exploration, in third stage modelling was done, in fourth stage created model was evaluated, and in the fifth step data was adapted by model deployment to the working principles of the algorithms that would be used. Then, in the final stage it was evaluated by the methods of multilayer perceptron aka artificial neural network method. Results from artificial neural networks method compared with the actual data and the results analyzed.

References

  • Ahsaan, Ul-S., Mourya, K., A., Majid, A. (2019), Predictive Analytics And Modeling Of Big Data Through Mutual Contraction Of Map-Reduce And R-Programming Libraries, Acta Technica Corviniensis – Bulletin Of Engineering Tome Xii [2019] | Fascicule 4 [October – December
  • Akter, S., & Wamba, S. F. (2016). Big Data Analytics In E-Commerce: A Systematic Review And Agenda For Future Research. Electronic Markets, 26(2), 173-194.
  • Asilkan, Ö. ve Irmak, S., (2009). İkinci El Otomobillerin Gelecekteki Fiyatlarının Yapay Sinir Ağları İle Tahmin Edilmesi, Süleyman Demirel Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi Y.2009, C.14, S.2 S.375-391. Y.2009, Vol.14, No.2, .375-391.
  • Çelik, Ö., & Osmanoğlu, U. Ö. (2019). Prediction of The Prices of Secondhand Cars. Avrupa Bilim ve Teknoloji Dergisi, (16), 77-83.
  • Du, J., Xie, L., & Schroeder, S. (2009). Practice Prize Paper—PIN Optimal Distribution of Auction Vehicles System: Applying Price Forecasting, Elasticity Estimation, and Genetic Algorithms to Used-Vehicle Distribution, Marketing Science, 28(4), 637-644.
  • Gültekin, S. U. (2017). Veri Madenciliği: Yapay Sinir Ağı Ve Doğrusal Regresyon Yöntemleri İle Fiyat Tahmini, Yayımlanmamış Yüksek Lisans Tezi, PAU. Sos. Bil. Enstitüsü.
  • Hand D., (2001). Principles of Data Mining (Adaptive Computation and Machine Learning Series, MIT
  • Ho, A., Romano, R., & Wu, X. A. (2012). Don’t Get Kicked-Machine Learning Predictions for Car Buying, Stanford University, CS229 Machine Learning.
  • Kiran, F., Zubair, A., Shahzadi, I. and Abbas, A. (2018), Internet-Based Digital Marketing Strategies Fordata-Rich Environments: A Social Network Perspective To Study Gossips, The Bottom Line, Vol. 31 No. 2, Pp. 98-113. https://doi.org/10.1108/BL-03-2018-0012
  • Koyuncugil, A. S., & Özgülbaş, N. (2009). Veri madenciliği: Tıp ve sağlık hizmetlerinde kullanımı ve uygulamaları. İnternational Journal Of Informatics Technologies, 2(2).
  • Kuiper, S. (2008). Introduction to Multiple Regression: How Much Is Your Car Worth?, Journal of Statistics Education, 16(3).
  • Lin, Y. (2015). Auto Car Sales Prediction: A Statistical Study Using Functional Data Analysis and Time Series, Doctoral Dissertation, University of Michigan.
  • Listiani M. (2009). Support Vector Regression Analysis for Price Prediction in a Car Leasing Application, Master Thesis. Hamburg University of Technology.
  • Luk K. C., Ball J. E., Sharma A., (2000). A Study of Optimal Model Lag and Spatial Inputs to Artificial Neural Network for Rainfall Forecasting, Journal of Hydrology, Volume: 227, 56 – 65.
  • Oprea, C. (2011). Making The Decision On Buying Secondhand Car Market Using Data Mining Techniques, The USV Annals of Economics And Public Administration, 10(3), 17-26.
  • Peerun S., Chummun N. H., Pudaruth S., (2015). Predicting the Price of Secondhand Cars using Artificial Neural Networks, Proceedings of the Second International Conference on Data Mining, Internet Computing, and Big Data, Reduit, Mauritius, 17-21
  • Pudaruth, S. (2014). Predicting The Price Of Used Cars Using Machine Learning Techniques, Int. J. Inf. Comput. Technol, 4(7), 753-764.
  • Purohit, D. (1992). Exploring The Relationship Between The Markets For New And Used Durable Goods: The Case Of Automobiles, Marketing Science, 11(2), 154-167.
  • Singh, Y., & Chauhan, A. S. (2009). Neural Networks In Data Mining, Journal of Theoretical & Applied Information Technology, 5(1), 37-42.
  • Sun, N., Bai, H., Geng, Y., & Shi, H. (2017, June). Price evaluation model in secondhand car system based on BP neural network theory. In 2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 431-436). IEEE.
  • TU, Jack V., (1996), Advantages and Disadvantages of Using Artificial Neural Networks versus Logistic Regression for Predicting Medical Outcomes, J Clin Epidemiol, XLIX,11: 1225-1231. doi:10.1016/S0895-4356(96)00002-9
  • Voß, S., & Lessmann, S. (2013). Resale Price Prediction In The Used Car Market, In Tristan Symposium VIII.
  • WEKA (2020) Download Date 20.05.2020 URL https://tr.wikipedia.org/wiki/Weka

Yapay sinir ağı yöntemi ile internet üzerinden satılan ikinciel araçların fiyat tahmini

Year 2020, , 49 - 61, 01.06.2020
https://doi.org/10.34231/iuyd.698095

Abstract

Veri madenciliği, e-ticaret firmaları için önemli bir çalışma alanıdır. Çok büyük miktardaki mevcut verinin katlanarak artmasıyla veri madenciliği yöntemlerinin kullanılması kaçınılmaz hale gelmiştir. Çünkü bir mal veya hizmetin maliyeti, ürünü satma potansiyeli, ürünün potansiyel fiyatı vb. yeterli değişkenle birlikte bu yöntemlerle tahmin edilebilir. Hangi yöntemi kullanırsak kullanalım maliyet ve zaman açısından verimli olmalıdır. Bu nedenle, herhangi bir gecikme olmaksızın doğru ve hızlı tahminler yapmak için veri madenciliği yöntemlerini kullanmalıyız. Bu çalışmada, ikinci el otomobil pazarındaki araçların fiyatlarını yapay sinir ağı yöntemi ile tahmin etmeye çalıştık. Bu sorunu çözmek için altı aşamalı bir süreç kullandık. Veri madenciliği aşamaları kullanılarak problem tanımı yapılmış, ilk aşamada veri hazırlamada veri temizliği yapılmış, ikinci aşamada keşif için veri düzenlenmiş, üçüncü aşamada modelleme yapılmış, dördüncü aşamada oluşturulan model değerlendirilmiş ve beşinci aşamada veriler, model ile kullanılacak algoritmaların çalışma prensiplerine göre uyarlanmıştır. Daha sonra son aşamada yapay sinir ağı yöntemi olan çok katmanlı algılayıcılarla değerlendirilme yapılmıştır. Yapay sinir ağları yönteminden elde edilen sonuçlar ile var olan gerçek veriler karşılaştırıldı.

References

  • Ahsaan, Ul-S., Mourya, K., A., Majid, A. (2019), Predictive Analytics And Modeling Of Big Data Through Mutual Contraction Of Map-Reduce And R-Programming Libraries, Acta Technica Corviniensis – Bulletin Of Engineering Tome Xii [2019] | Fascicule 4 [October – December
  • Akter, S., & Wamba, S. F. (2016). Big Data Analytics In E-Commerce: A Systematic Review And Agenda For Future Research. Electronic Markets, 26(2), 173-194.
  • Asilkan, Ö. ve Irmak, S., (2009). İkinci El Otomobillerin Gelecekteki Fiyatlarının Yapay Sinir Ağları İle Tahmin Edilmesi, Süleyman Demirel Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi Y.2009, C.14, S.2 S.375-391. Y.2009, Vol.14, No.2, .375-391.
  • Çelik, Ö., & Osmanoğlu, U. Ö. (2019). Prediction of The Prices of Secondhand Cars. Avrupa Bilim ve Teknoloji Dergisi, (16), 77-83.
  • Du, J., Xie, L., & Schroeder, S. (2009). Practice Prize Paper—PIN Optimal Distribution of Auction Vehicles System: Applying Price Forecasting, Elasticity Estimation, and Genetic Algorithms to Used-Vehicle Distribution, Marketing Science, 28(4), 637-644.
  • Gültekin, S. U. (2017). Veri Madenciliği: Yapay Sinir Ağı Ve Doğrusal Regresyon Yöntemleri İle Fiyat Tahmini, Yayımlanmamış Yüksek Lisans Tezi, PAU. Sos. Bil. Enstitüsü.
  • Hand D., (2001). Principles of Data Mining (Adaptive Computation and Machine Learning Series, MIT
  • Ho, A., Romano, R., & Wu, X. A. (2012). Don’t Get Kicked-Machine Learning Predictions for Car Buying, Stanford University, CS229 Machine Learning.
  • Kiran, F., Zubair, A., Shahzadi, I. and Abbas, A. (2018), Internet-Based Digital Marketing Strategies Fordata-Rich Environments: A Social Network Perspective To Study Gossips, The Bottom Line, Vol. 31 No. 2, Pp. 98-113. https://doi.org/10.1108/BL-03-2018-0012
  • Koyuncugil, A. S., & Özgülbaş, N. (2009). Veri madenciliği: Tıp ve sağlık hizmetlerinde kullanımı ve uygulamaları. İnternational Journal Of Informatics Technologies, 2(2).
  • Kuiper, S. (2008). Introduction to Multiple Regression: How Much Is Your Car Worth?, Journal of Statistics Education, 16(3).
  • Lin, Y. (2015). Auto Car Sales Prediction: A Statistical Study Using Functional Data Analysis and Time Series, Doctoral Dissertation, University of Michigan.
  • Listiani M. (2009). Support Vector Regression Analysis for Price Prediction in a Car Leasing Application, Master Thesis. Hamburg University of Technology.
  • Luk K. C., Ball J. E., Sharma A., (2000). A Study of Optimal Model Lag and Spatial Inputs to Artificial Neural Network for Rainfall Forecasting, Journal of Hydrology, Volume: 227, 56 – 65.
  • Oprea, C. (2011). Making The Decision On Buying Secondhand Car Market Using Data Mining Techniques, The USV Annals of Economics And Public Administration, 10(3), 17-26.
  • Peerun S., Chummun N. H., Pudaruth S., (2015). Predicting the Price of Secondhand Cars using Artificial Neural Networks, Proceedings of the Second International Conference on Data Mining, Internet Computing, and Big Data, Reduit, Mauritius, 17-21
  • Pudaruth, S. (2014). Predicting The Price Of Used Cars Using Machine Learning Techniques, Int. J. Inf. Comput. Technol, 4(7), 753-764.
  • Purohit, D. (1992). Exploring The Relationship Between The Markets For New And Used Durable Goods: The Case Of Automobiles, Marketing Science, 11(2), 154-167.
  • Singh, Y., & Chauhan, A. S. (2009). Neural Networks In Data Mining, Journal of Theoretical & Applied Information Technology, 5(1), 37-42.
  • Sun, N., Bai, H., Geng, Y., & Shi, H. (2017, June). Price evaluation model in secondhand car system based on BP neural network theory. In 2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 431-436). IEEE.
  • TU, Jack V., (1996), Advantages and Disadvantages of Using Artificial Neural Networks versus Logistic Regression for Predicting Medical Outcomes, J Clin Epidemiol, XLIX,11: 1225-1231. doi:10.1016/S0895-4356(96)00002-9
  • Voß, S., & Lessmann, S. (2013). Resale Price Prediction In The Used Car Market, In Tristan Symposium VIII.
  • WEKA (2020) Download Date 20.05.2020 URL https://tr.wikipedia.org/wiki/Weka
There are 23 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Research Article
Authors

Sait Gültekin This is me

Arzu Organ

Publication Date June 1, 2020
Published in Issue Year 2020

Cite

APA Gültekin, S., & Organ, A. (2020). Price estimation of secondhand cars sold on the internet with artificial neural network method. İnternet Uygulamaları Ve Yönetimi Dergisi, 11(1), 49-61. https://doi.org/10.34231/iuyd.698095