Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)

House Price Prediction Based on Machine Learning: A Case of King County

Authors
Yijia Wang1, , Qiaotong Zhao2, *,
1Queen’s University. 39-2250 Rockingham Drive, L6H 6J3, Oakville, ON, Canada. Email:18yw148@queensu.ca
2Civil Aviation University of China. No.2898, Jinbei Highway, Dongli District, Tianjin, China 300300. Email:zhaoqiaotong@163.com

These authors contributed equally.

*Corresponding author. Email: zhaoqiaotong@163.com
Corresponding Author
Qiaotong Zhao
Available Online 26 March 2022.
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.

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Volume Title
Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
Series
Advances in Economics, Business and Management Research
Publication Date
26 March 2022
ISBN
10.2991/aebmr.k.220307.253
ISSN
2352-5428
DOI
10.2991/aebmr.k.220307.253How to use a DOI?
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  -