Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)

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A Comparative Study of Random Forest Regression for Predicting House Prices Using

Authors
Mohan Mao1, *
1Zhengzhou Foreign Language School New Fengyang Campus, Henan Province, Zhengzhou, 450000, China
*Corresponding author.
Corresponding Author
Mohan Mao
Available Online 14 February 2024.
DOI
10.2991/978-94-6463-370-2_63How to use a DOI?
Keywords
random forest; gradient boosting predict model
Abstract

Based on the rapid development of the real estate market, real estate prices in various regions of the world fluctuate greatly and are unstable, and we need to make some predictions for real estate prices. However, in reality, we pay too much attention to the relationship between past property prices and current property prices and often ignore the prediction of future house prices. Research on predictive models is lacking. Therefore, studying real estate forecasting models is one of the best solutions to solve the problems faced by the real estate market based on the thinking of the current situation. In response to this problem, I propose to use a random forest model, gradient boosting, and optional to build a reasonable predictive model. The final results prove that this predictive model can be used to some extent to predict changing real estate prices in the future market. It is hoped that the method in this paper can provide a reference for subsequent research on predictive models.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
Series
Advances in Intelligent Systems Research
Publication Date
14 February 2024
ISBN
10.2991/978-94-6463-370-2_63
ISSN
1951-6851
DOI
10.2991/978-94-6463-370-2_63How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Mohan Mao
PY  - 2024
DA  - 2024/02/14
TI  - A Comparative Study of Random Forest Regression for Predicting House Prices Using
BT  - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
PB  - Atlantis Press
SP  - 619
EP  - 626
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-370-2_63
DO  - 10.2991/978-94-6463-370-2_63
ID  - Mao2024
ER  -