Artificial intelligence approach for modeling house price prediction
Yükleniyor...
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Indexed keywords SciVal Topics Abstract Real estate has a vast market volume across the globe. This domain has been growing significantly in the past few decades. An accurate prediction can help buyers, and other decision-makers make better decisions. However, developing a model that can effectively predict house prices in complex environments is still a challenging task. This paper proposes machine learning models for the accurate prediction of real estate house prices. Furthermore, we investigated the feature importance and various data analysis methods to improve the prediction accuracy. Linear Regression, Decision Tree, XGBoost, Extra Trees, and Random Forest were used in this study. For all models, hyperparameters were first calculated using k-fold cross-validation, and then they were trained to apply to test data. The models were tested on the Boston housing dataset. The proposed method was evaluated using Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics.
Açıklama
Anahtar Kelimeler
Convolutional Neural (CNN), Convolutional Neural Network Real Estate Price Prediction, House Price Prediction, Machine Learning
Kaynak
2022 2nd International Conference on Computing and Machine Intelligence, ICMI 2022 - Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Cekic, M., Korkmaz, K. N., Mukus, H., Hameed, A. A., Jamil, A., & Soleimani, F. (2022). Artificial intelligence approach for modeling house price prediction. Paper presented at the 2022 2nd International Conference on Computing and Machine Intelligence, ICMI 2022 - Proceedings, doi:10.1109/ICMI55296.2022.9873784
