Comparative analysis of various machine learning and deep learning approaches for car resale price prediction in the turkish market

Fatih Uysal

Makale detayları

Comparative analysis of various machine learning and deep learning approaches for car resale price prediction in the turkish market

Yazarlar:

Fatih Uysal

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Elektrik Mühendisliği (Diğer)

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.13 Issue 1, pp.342-349

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309730

pISSN:

2564-6605

Anahtar kelimeler:

Özet:

 With escalating environmental concerns worldwide, the shift towards second-hand car markets has emerged as an eco-friendly alternative to reduce the carbon footprint associated with manufacturing new vehicles. However, the lack of accurate and efficient price prediction mechanisms may impede the growth and efficiency of these markets. This study, focusing on the Turkish second-hand car market, contributes towards addressing this gap by introducing a unique, comprehensive dataset gathered from various online markets across Turkey, thereby offering a broad spectrum of data pertaining to different vehicle types, specifications, and resale conditions. The study employs both classical machine learning methods, specifically decision trees, and deep learning models to predict used car prices. This comparative analysis aims to assess the potential of these methods in improving the predictability and transparency of resale price determination. Despite the superior performance of decision tree models, the study found that deep learning techniques achieved comparable results, indicating their potential for further optimization and enhancement. The accurate prediction of resale prices could streamline the operations of second-hand car markets, increasing their appeal to potential buyers and sellers. This could also contribute to environmental sustainability by significantly reducing the demand for new cars.

 With escalating environmental concerns worldwide, the shift towards second-hand car markets has emerged as an eco-friendly alternative to reduce the carbon footprint associated with manufacturing new vehicles. However, the lack of accurate and efficient price prediction mechanisms may impede the growth and efficiency of these markets. This study, focusing on the Turkish second-hand car market, contributes towards addressing this gap by introducing a unique, comprehensive dataset gathered from various online markets across Turkey, thereby offering a broad spectrum of data pertaining to different vehicle types, specifications, and resale conditions. The study employs both classical machine learning methods, specifically decision trees, and deep learning models to predict used car prices. This comparative analysis aims to assess the potential of these methods in improving the predictability and transparency of resale price determination. Despite the superior performance of decision tree models, the study found that deep learning techniques achieved comparable results, indicating their potential for further optimization and enhancement. The accurate prediction of resale prices could streamline the operations of second-hand car markets, increasing their appeal to potential buyers and sellers. This could also contribute to environmental sustainability by significantly reducing the demand for new cars.

 With escalating environmental concerns worldwide, the shift towards second-hand car markets has emerged as an eco-friendly alternative to reduce the carbon footprint associated with manufacturing new vehicles. However, the lack of accurate and efficient price prediction mechanisms may impede the growth and efficiency of these markets. This study, focusing on the Turkish second-hand car market, contributes towards addressing this gap by introducing a unique, comprehensive dataset gathered from various online markets across Turkey, thereby offering a broad spectrum of data pertaining to different vehicle types, specifications, and resale conditions. The study employs both classical machine learning methods, specifically decision trees, and deep learning models to predict used car prices. This comparative analysis aims to assess the potential of these methods in improving the predictability and transparency of resale price determination. Despite the superior performance of decision tree models, the study found that deep learning techniques achieved comparable results, indicating their potential for further optimization and enhancement. The accurate prediction of resale prices could streamline the operations of second-hand car markets, increasing their appeal to potential buyers and sellers. This could also contribute to environmental sustainability by significantly reducing the demand for new cars.

 Çevresel kaygıların yoğunlaşmasıyla birlikte, ikinci el araç piyasaları, yeni araçların üretimindeki karbon ayak izini azaltma konusunda çevre dostu bir alternatif olarak ön plana çıkmıştır. Ancak, etkili ve doğru fiyat tahmin mekanizmalarının yetersizliği, bu piyasaların büyüme ve verimliliği üzerinde engelleyici bir etkiye sahip olabilir. Bu çalışma, bu sorunu çözme hedefine yönelik olarak, özellikle Türk ikinci el araç piyasası üzerinde durmuştur ve Türkiye genelindeki farklı online pazarlardan derlenen geniş bir veri seti sunmuştur. Bu veri seti, çeşitli araç türleri, özellikleri ve yeniden satış koşulları hakkında geniş kapsamlı bilgiler sağlamaktadır. Çalışmada, ikinci el araç fiyatlarının tahmininde hem klasik makine öğrenmesi yöntemleri -özellikle karar ağaçları- hem de derin öğrenme modelleri kullanılmıştır. Bu karşılaştırmalı analizin amacı, bu metotların yeniden satış fiyatının belirlenmesinde tahmin gücünü ve şeffaflığı nasıl iyileştirebileceğini değerlendirmektir. Karar ağaçlarının daha yüksek performans göstermiş olmasına rağmen, derin öğrenme tekniklerinin de benzer sonuçlar elde ettiği ve bu nedenle daha fazla optimizasyon ve geliştirme potansiyeli taşıdığı tespit edilmiştir. Yeniden satış fiyatlarının doğru bir şekilde tahmin edilmesi, ikinci el araç piyasalarının işleyişini daha verimli hale getirebilir ve potansiyel alıcılar ve satıcılar için daha çekici kılabilir. Ayrıca bu durum, yeni araç talebini önemli ölçüde azaltarak çevresel sürdürülebilirliğe katkıda bulunabilir.

Makale detayları

Comparative analysis of various machine learning and deep learning approaches for car resale price prediction in the turkish market

Yazarlar:

Fatih Uysal

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Elektrik Mühendisliği (Diğer)

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.13 Issue 1, pp.342-349

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309730

pISSN:

2564-6605

Resale price prediction

tabular data

deep learning

decision treesYeniden satış fiyatı tahminleme

tablo verisi

derin öğrenme

karar ağaçları

Sayı

Makale detayları

Yazarlar: Fatih Uysal

Yayıncı: Nigde Omer Halisdemir University

Ana Başlıklar: Elektrik Mühendisliği (Diğer)

Dergi: Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.13 Issue 1, pp.342-349

Belge Tipi: Makale

Yayın Tarihi: 1/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001309730

pISSN: 2564-6605

Sayı

Yazarlar:

Fatih Uysal

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Elektrik Mühendisliği (Diğer)

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.13 Issue 1, pp.342-349

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309730

pISSN:

2564-6605