Hybrid House Price Prediction Model by Integration of Simple Linear Regression and Cubic Spline Interpolation

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.