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Published on 6 May 2025
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Tang,Y. (2025). Hybrid House Price Prediction Model by Integration of Simple Linear Regression and Cubic Spline Interpolation. Theoretical and Natural Science,105,61-70.
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Hybrid House Price Prediction Model by Integration of Simple Linear Regression and Cubic Spline Interpolation

Yiwen Tang *,1,
  • 1 Department of Mathematics, The University of Hong Kong, Hong Kong, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/2025.22572

Abstract

In this day and age, house-purchase has become a crucial consideration for almost everyone, whether seeking a residence or making an investment. Therefore, analyzing the relationship between these factors and house prices is vitally important for both buyers and sellers to make informed decisions. Some researchers have used a linear regression model that can predict the house price for a company or individual. This paper focuses on a target sample data set of “Houses in London” from Kaggle. The author firstly provides an analysis of two methods to model the relationship between the dependent variable, House Price, and an independent variable, Square Meters. These methods are the Simple Linear Regression Model (SLR) and Cubic Spline Interpolation polynomial (CSI), respectively. Then, a Hybrid House Price Prediction Model is established to predict the house price with specific Square Meters by integrating SLR and CSI. Finally, the author uses Multiple Linear Regression to model the effects of various independent variables from the target sample to the House Price. The research significance of this paper mainly includes increasing the accuracy and comprehensiveness of the House Prediction Model by constructing a Hybrid Model and using the MLR model, respectively.

Keywords

House Price Prediction, Simple Linear Regression, Cubic Spline Interpolation, Multiple Linear Regression

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Cite this article

Tang,Y. (2025). Hybrid House Price Prediction Model by Integration of Simple Linear Regression and Cubic Spline Interpolation. Theoretical and Natural Science,105,61-70.

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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About volume

Volume title: Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation

Conference website: https://2025.confmpcs.org/
ISBN:978-1-80590-077-1(Print) / 978-1-80590-078-8(Online)
Conference date: 27 June 2025
Editor:Anil Fernando
Series: Theoretical and Natural Science
Volume number: Vol.105
ISSN:2753-8818(Print) / 2753-8826(Online)

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