Machine learning building price prediction with green building determinant

Thuraiya Mohd, Syafiqah Jamil, Suraya Masrom


In the era of Industrial 4.0, many urgent issues in the industries can be effectively solved with artificial intelligence techniques, including machine learning. Designing an effective machine learning model for prediction and classification problems is an ongoing endeavor. Besides that, time and expertise are important factors that are needed to tailor the model to a specific issue, such as the green building housing issue. Green building is known as a potential approach to increase the efficiency of the building. To the best of our knowledge, there is still no implementation of machine learning model on GB valuation factors for building price prediction compared to conventional building development. This paper provides a report of an empirical study that model building price prediction based on green building and other common determinants. The experiments used five common machine learning algorithms namely Linear Regression, Decision Tree, Random Forest, Ridge and Lasso tested on a set of real building datasets that covered Kuala Lumpur District, Malaysia. The result showed that the Random Forest algorithm outperforms the other four algorithms on the tested dataset and the green building determinant has contributed some promising effects to the model.


Algorithm, Green building, Machine learning, Phyton, Prediction, Skirt-learn

Full Text:




  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

View IJAI Stats