Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 26, 20 December 2023
Open
Access | Article
Machine learning on USA house price prediction
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Abstract
Nowadays, an increasing number of students are opting to study abroad in order to acquire more advanced knowledge and pursue a superior educational environment. In many foreign countries, the option to apply for school dormitories is only available during the first year of university or graduate school. At other times, international students have to search for rented apartments or apply to stay with local host families. However, when studying abroad for an extended period, purchasing a property can potentially result in significant savings compared to renting. Therefore, this study focuses on comparing three types of machine learning techniques: multiple linear regression, Random Forest, and XGboost in predicting house prices in the United States. This research could provide reference for families studying abroad or property investors. Based on the preliminary findings of this study so far, it can be concluded that the XG-boost model demonstrates the highest accuracy and stability among these three methods.
Keywords
Linear regression model, random forest model, XGboost model, USA house price prediction.
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-235-0
- ISBN (Online)
- 978-1-83558-236-7
- Published Date
- 20 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/26/20241019
- Copyright
- 20 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited