House Price Prediction Based on Machine Learning: A Case of King County
These authors contributed equally.
- DOI
- 10.2991/aebmr.k.220307.253How to use a DOI?
- Keywords
- Catboost; House Price; King County; Prediction
- Abstract
This paper focuses on formulating a feasible method for house price prediction. A dataset containing features and house price of King County in the US is used. During the data preprocessing, extreme values are winsorized and highly correlated features are removed. Eight models including Catboost, lightGBM and XGBoost serve as candidate models. They are evaluated by several evaluation indicators, including rooted mean square error, R-squared score, adjusted R-squared score and K-fold cross validation score. The model that has low RMSE, achieves a high R-squared score and adjusted R-squared score, especially in the test set, and acquires a high score in cross validation is considered a better model. This paper finds out that Catboost performs the best among all models and can be used for house price prediction. Location, living space and condition of the house are the most important features influencing house price. After comparison and contrast with other papers, it is attested that findings in this paper conform to real life. This paper formulates a model that fits better than preceding studies for house price prediction and makes necessary supplement to the exploration of features that influence house price from a microscope.
- Copyright
- © 2022 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Yijia Wang AU - Qiaotong Zhao PY - 2022 DA - 2022/03/26 TI - House Price Prediction Based on Machine Learning: A Case of King County BT - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) PB - Atlantis Press SP - 1547 EP - 1555 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.220307.253 DO - 10.2991/aebmr.k.220307.253 ID - Wang2022 ER -