Academic Journal of Business & Management, 2023, 5(2); doi: 10.25236/AJBM.2023.050207.
Haoyue Zou
Faculty of Science, Hunan University of Technology, Zhuzhou, China
With the rapid development of machine learning and related fields, it has been widely used in various industries. House price is a hot topic in the nation’s livelihood, and the influencing factors are very complex. Therefore, a dataset with a large number of features is chosen as the data for processing in this paper. Firstly, the least-squares and random forest regression algorithms in machine learning are introduced; afterwards, the training and test set data are processed and analysed to identify the seven main factors affecting housing prices; finally, the two models are evaluated, and the results show that both algorithms can accurately predict housing prices.
Housing price forecasting; Least-squares; Random Forest regression algorithms
Haoyue Zou. Machine Learning-Based Analysis of Housing Price Predictors. Academic Journal of Business & Management (2023) Vol. 5, Issue 2: 34-40. https://doi.org/10.25236/AJBM.2023.050207.
[1] Xu, G., Zhang, K., (2014). Property price evaluation based on random forest model. Statistics and decision making., 17:4
[2] Tang B S, Ho., S W, W., (2021). Predicting property prices with machine learning algorithms. Journal of Property Research, 38: 48-70.
[3] Truong, Q., Nguyen, M., Dang, H., (2020). Housing price prediction via improved machine learning techniques. Procedia Computer Science, 174: 433-442.
[4] Tian, R., (2019). Boston house price prediction based on multiple machine learning algorithms. China New Communications, 11: 228-230