Akademik Veri Yönetim Sistemi
Konya mühendislik bilimleri dergisi (Online), cilt.10, sa.1, ss.147-161, 2022 (Hakemli Dergi)
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.