Yenilenmiş Akıllı Telefon Pazarının Makine Öğrenmesi ile Analizi

Yıl 2026, Cilt: 40 Sayı: 1, 42 - 59, 01.01.2026

Berrin Beyza Özen , Muhammed Fatih Alaeddinoğlu , Tolga Aydın

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

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

Teşekkür

SENATECH BİLGİ TEKNOLOJİLERİ SANAYİ TİCARET A.Ş.'ye gerçek piyasa verilerine ulaşma, sektörel deneyimlerle çalışmanın bulgularını doğrulama ve alan uzmanlarıyla saha araştırmaları gerçekleştirme konusundaki değerli katkıları için içten teşekkürlerimizi sunarız.

Analysing the Refurbished Smart Phone Market with Machine Learning

Öz

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.

Anahtar Kelimeler

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

Teşekkür

We thank SENATECH Information Technology Industry Trade Inc. for giving us real market data, using its industry knowledge to check the study's results, and doing field research with professionals in this field.

Kaynakça

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