Gayrimenkul Fiyat Tahmini ve Alttaki Özelliklerin Analizi İçin C4.5 – CART Karar Ağacı Modeli

Fiyat tahmini için makina öğrenmesi uygulamaları farklı alanlarda kullanılmaktadır. Gayrimenkul alanında fiyat tahmini son yıllarda ön plana çıkmaktadır. Ancak, çalışmaların büyük bölümü tahmin performansına odaklanmış olup fiyata etki eden faktörlerin incelenmesi göz ardı edilmiştir. Bu çalışmada gayrimenkul fiyat tahmin için bir C4.5 – CART ağacı modeli geliştirilmiştir. Bu model hem nümerik hem de kategorik fiyat tahmini yapabilmektedir. Ek olarak fiyata etki eden faktörler detaylıca analiz edilerek ortaya çıkarılmıştır. İlgili modelin performansı bu alanda bir altın standart olan Direkt Kapitalazyon modeli ile karşılaştırılmıştır. Her iki model web kazıyıcı tarafından elde edilen güncel gerçek zamanlı veri kümeleri üzerinde test edilmiştir. Nümerik tahmin için geliştirilen modelin kök ortalama kare hatası 13.169 iken Direkt Kapitalizasyon için 359,69 bulunmuştur. Kategorik tahmin için kesinlik ve KAPPA metrikleri kullanılmıştır. Modelin KAPPA sayısı %81 ve kesinlik değeri %88’dir.

Anahtar Kelimeler:

Makine öğrenmesi, Karar ağacı, C4.5, CART, Direkt kapitalizasyon

A C4.5 – CART DECISION TREE MODEL FOR REAL ESTATE PRICE PREDICTION AND THE ANALYSIS OF THE UNDERLYING FEATURES

The machine learning approaches are used in different domains for price prediction. Real estate price prediction comes to fore in recent years. However, most of the studies focus on the prediction performance and the factors affecting the price are often ignored. In this study, a C4.5 – CART model to predict the residential real estate prices is developed. This model is capable of predicting both numeric and categorical price for real estate properties. In addition, the factors affecting the price are reveled and analyzed in detail. The performance of the developed model is compared to Direct Capitalization model, which is used as a gold standard in the domain. Both models are tested on a dataset that includes updated real time data that is gathered by a web scraper. For numeric prediction, RMSE of the developed model is 13.169 and 358.69 for the Direct Capitalization model. KAPPA and accuracy is used for the categorical prediction. The model has 81% KAPPA and 88% accuracy.

Keywords:

Machine learning, Decision tree, C4.5, CART, Direct Capitalization,

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