Analysing the Refurbished Smart Phone Market with Machine Learning

Berrin Beyza Özen; Muhammed Fatih Alaeddinoğlu; Tolga Aydın

doi:10.16951/trendbusecon.1607949

TR EN

Yenilenmiş Akıllı Telefon Pazarının Makine Öğrenmesi ile Analizi

Öz

Yenilenmiş akıllı telefon pazarı, ekonomik ve çevresel faydaları nedeniyle son zamanlarda dikkat çekmektedir. Özellikle, artan çevre bilinci ve uygun maliyetli alternatiflerin aranması, yenilenmiş ürünlere olan talebi artırmıştır. Ancak, bu pazarın dinamikleri ve fiyatlandırma uygulamaları, yeni cihaz pazarından farklıdır. Fiyat oluşumu, cihazın durumu ve modeli gibi ürüne özgü çeşitli faktörlere bağlıdır. Ancak, bu çok faktörlü yapıyı analiz etmek ve doğru fiyat tahminleri yapmak tüketiciler, satıcılar ve yeniden üreticiler için hala zorlu bir görevdir. Bu bağlamda, makine öğrenimi yüksek doğrulukta fiyat tahmini yapılmasına destek olabilir. Yenilenmiş akıllı telefonlar için özellik tabanlı fiyat tahmin modelleri geliştirmek, fiyat dalgalanmalarını açıklamaya ve kullanım ve yenileme sonrası durumu dikkate alarak cihazın değerini tahmin etmeye yardımcı olur. Bu çalışmada, tahmin doğruluğunu artırmak için hem geleneksel makine öğrenimi hem de derin öğrenme yöntemleri kullanılmıştır. Model performansı MSE, MAE, RMSE ve R² puanı kullanılarak değerlendirilmiştir. XGB Regressor, geleneksel makine öğrenimi algoritmaları arasında 0,9902 R² ile en iyi sonucu elde etmiştir. Derin öğrenme modelleri arasında LSTM de 0,9870 R² ile güçlü bir performans göstermiştir.

Anahtar Kelimeler

Yenilenmiş Pazar , Akıllı Telefon Fiyatı , Regresyon , Fiyat Tahmini , Sürdürebilirlik

Analysing the Refurbished Smart Phone Market with Machine Learning

Abstract

The refurbished smartphone market has recently attracted attention because of its economic and environmental benefits. In particular, rising environmental awareness and the search for cost-effective alternatives have increased demand for refurbished products. However, the dynamics of this market and its pricing practices differ from those of the new-device market. Price formation depends on several product-specific factors, including device condition and model. Yet, analysing this multi-factor structure and producing accurate price estimates remains challenging for consumers, sellers, and remanufacturers. In this context, machine learning can support high-accuracy price prediction. Developing feature-based price prediction models for refurbished smartphones helps to explain price fluctuations and to estimate a device’s value by considering usage and post-refurbishment condition. In this study, both traditional machine learning and deep learning methods are used to improve prediction accuracy. Model performance is evaluated using MSE, MAE, RMSE, and the R² score. The XGB Regressor achieved the best result among the traditional machine learning algorithms, with an R² of 0.9902. Among the deep learning models, LSTM also performed strongly, reaching an R² of 0.9870.

Keywords

Refurbished Market , Smartphone Price , Regression , Price Prediction , Sustainability

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Büyük Veri , Modelleme ve Simülasyon , Planlama ve Karar Verme , Yapay Zeka (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Berrin Beyza Özen ^*
0009-0005-3646-5875
Türkiye

Muhammed Fatih Alaeddinoğlu
Türkiye

Tolga Aydın
0000-0002-8971-3255
Türkiye

Yayımlanma Tarihi

1 Ocak 2026

Gönderilme Tarihi

30 Aralık 2024

Kabul Tarihi

18 Temmuz 2025

Yayımlandığı Sayı

Yıl 2026 Cilt: 40 Sayı: 1

DOI

https://doi.org/10.16951/trendbusecon.1607949

IZ

https://izlik.org/JA43EY28ZB

APA

Özen, B. B., Alaeddinoğlu, M. F., & Aydın, T. (2026). Analysing the Refurbished Smart Phone Market with Machine Learning. Trends in Business and Economics, 40(1), 42-59. https://doi.org/10.16951/trendbusecon.1607949

Content of this journal is licensed under a Creative Commons Attribution 4.0 International License

29928

Yenilenmiş Akıllı Telefon Pazarının Makine Öğrenmesi ile Analizi

Öz

Anahtar Kelimeler

Analysing the Refurbished Smart Phone Market with Machine Learning

Abstract

Keywords

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster