Feature Selection in House Price Prediction
DOI:
https://doi.org/10.54097/hbem.v21i.14755Keywords:
Feature selection, house price prediction, SVM.Abstract
This study aims to construct a model to choose effective features and use them to predict the market price of an exact house, which can help people with house pricing and property evaluating. This paper preliminarily constructs several machine learning models like Linear Regression, SVM, KNN and compares their accuracy on this problem to choose the best-fit one for improving. After using parameter tuning to optimize this model, this study tries to use and recursive feature elimination and genetic algorithm to select features to improve simple SVM model. After feature selection, this study re-evaluated the accuracy of the model and compared which features had a greater impact on the predictions. After comparison, this study found that in this particular case, features have closer connections with living condition, traffic condition and sale condition will have a huge impact on the house price.
Downloads
References
Zhu Haiyu, Wang Hijie, Ye. Cancan Prediction of housing prices in urban hotspot areas based on XGBoost algorithm - taking Nanjing Jiangbei New District as an example. Construction Economics, 2022, (S2): 433 - 437.
Yang Linchuan, Chen Yang, Xu Nenglai, Zhao Rui, Chau K.W., Hong Shijian. Place-varying impacts of urban rail transit on property prices in Shenzhen, China: Insights for value capture, Sustainable Cities and Society, 2020, 58: 102140.
Li, N., Li, R. Y. M., & Nuttapong, J. Factors affect the housing prices in China: a systematic review of papers indexed in Chinese Science Citation Database. Property Management, 2022, 40 (5): 780 – 796.
Zhou, Z. H. Machine learning. Springer, 2021.
Anders Hjort, Johan Pensar, Ida Scheel & Dag Einar Sommervoll. House price prediction with gradient boosted trees under different loss functions, Journal of Property Research, 2022, 39 (4): 338 – 364.
Wang Xibin, Wen Junhao, Zhang Yihao, Wang Yubiao Real estate price forecasting based on SVM optimized by PSO. Optik, 2014, 125 (3): 1439 - 1443.
Wang Tao, Yang Jian. Nonlinearity and intraday efficiency tests on energy futures markets, Energy Economics, 2010, 32 (2): 496 - 503.
Bergstra, J., Bengio, Y. Random search for hyper-parameter optimization. Journal of machine learning research, 2012, 13 (2).
Gao, Y. M., Zhang, R. J. Analysis of House Price Prediction Based on Genetic Algorithm and BP Neural Network. Computer Engineering, 2014, 40 (4): 187 – 191.
Lamba, R., Gulati, T., & Jain, A. A Hybrid Feature Selection Approach for Parkinson’s Detection Based on Mutual Information Gain and Recursive Feature Elimination. Arabian Journal for Science and Engineering, 2022, 47 (8): 10263 – 10276.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
