Makine Öğrenme Yöntemleri Kullanarak Mobilya Fiyatlarını Tahmin Etme

Abstract

Bu çalışma, makine öğrenmesi tabanlı regresyon modelleri kullanarak mobilya fiyatlarını tahmin etmeyi ve bu modellerin performanslarını bütünleşik bir deneysel çerçeve içerisinde karşılaştırmayı amaçlamaktadır. Doğru fiyat tahmini, mobilya sektöründe fiyatlandırma stratejileri, stok yönetimi ve pazar analizi gibi karar alma süreçlerinde kritik bir rol oynamaktadır. Çalışmada, mobilya ürünlerine ait hem sayısal hem de kategorik özellikleri içeren bir veri seti analiz edilmiştir. Karar Ağacı, Rastgele Orman, Gradient Boosting, XGBoost, K-En Yakın Komşular (KNN), Lasso ve Ridge gibi çeşitli makine öğrenmesi algoritmaları uygulanmıştır. Model performansı; Ortalama Mutlak Hata (MAE), Kök Ortalama Kare Hata (RMSE), Ortalama Mutlak Yüzde Hata (MAPE) ve Belirleme Katsayısı (R²) gibi metrikler kullanılarak değerlendirilmiştir. Elde edilen sonuçlar, topluluk tabanlı boosting yöntemlerinin diğer modellere kıyasla belirgin şekilde daha üstün performans sergilediğini göstermektedir. Tüm modeller arasında XGBoost en iyi performansı sağlamış olup, en düşük hata değerleri (MAE = 2.46, RMSE = 3.32, MAPE = %1.08) ve en yüksek R² değeri (0.9993) elde edilmiştir. Gradient Boosting benzer doğruluk seviyelerine ulaşmasına rağmen daha uzun hesaplama süresi gerektirmiştir. Buna karşılık, Random Forest, doğrusal modeller (Lasso ve Ridge) ve KNN daha düşük performans göstermiştir. Sonuç olarak, makine öğrenmesi yöntemlerinin mobilya fiyat tahmininde oldukça etkili olduğu görülmüş ve XGBoost modelinin doğruluk ile hesaplama verimliliği açısından en uygun çözüm olduğu belirlenmiştir. Önerilen yaklaşım, mobilya sektöründe karar destek süreçlerini destekleyebilecek güvenilir ve veri odaklı bir çözüm sunmaktadır.

Keywords

• Makine öğrenmesi
• mobilya endüstrisi
• fiyat tahmini
• ağaçişleri endüstrisi
• regresyon analizi

Supporting Institution

Bu çalışma, kamu, ticari veya kar amacı gütmeyen sektörlerdeki herhangi bir fon kuruluşundan özel bir hibe almamıştır.

Ethical Statement

Bu çalışma, insan katılımcı veya hayvan içermediği için etik kurul onayına gerek duymaz.

Thanks

Yazar, bu çalışmada kullanılan veri setini ve hesaplama ortamını sağladığı için Kaggle platformuna teşekkür eder.

Predicting Furniture Prices Using Machine Learning Methods

Abstract

This study aims to predict furniture prices using machine learning-based regression models and to compare their performance within a unified experimental framework. Accurate price prediction plays a critical role in decision-making processes such as pricing strategies, inventory management, and market analysis in the furniture sector. A dataset containing both numerical and categorical features related to furniture products was analyzed. Various machine learning algorithms, including Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors (KNN), Lasso, and Ridge, were implemented. Model performance was evaluated using multiple metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The results demonstrate that ensemble-based boosting methods significantly outperform other models. Among all models, XGBoost achieved the best overall performance, with the lowest error values (MAE = 2.46, RMSE = 3.32, MAPE = 1.08%) and the highest R² value (0.9993). Gradient Boosting also produced comparable results but required a longer computation time. In contrast, Random Forest, linear models (Lasso and Ridge), and KNN exhibited relatively lower performance. Overall, the findings highlight the effectiveness of machine learning approaches for furniture price prediction and indicate that XGBoost provides the most suitable balance between accuracy and computational efficiency. The proposed framework offers a reliable and data-driven solution that can support decision-making processes in the furniture industry.

Keywords

• Machine learning
• furniture industry
• price prediction
• wood industry
• regression analysis

Supporting Institution

This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Ethical Statement

This study does not require ethics committee approval, as it does not involve human participants or animals.

Thanks

The author would like to thank the Kaggle platform for providing the dataset and computational environment used in this study.

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