A Sailboat Pricing Model Based on PCA, Clustering and Regression

Abstract

Like many luxury goods, the value of sailboats also varies with age and market conditions. This article focuses on developing a mathematical model to explain a given price list for sailboats, which studies data on approximately 35 sailboats 36 to 56 feet long sold in Europe, the Caribbean, and the United States in December 2020, providing decision-making references for traders in the real sailing market. In order to predict and evaluate the value of sailboats, this paper first processed outliers and multiple indicators without differences. Then, statistical methods are used to process the data using multiple linear regression and machine learning. After the data processing is completed, the grey correlation model and factor analysis model can be used to obtain the proportion of each indicator in price. In summary, this article can effectively apply the idealized model to real life while establishing it, which has a good effect on improving the feasibility of the model.