Study of Sailboat Price Forecasting Based on Random Forest Regression

Abstract

This paper aims to solve the problem of predicting the listing price of sailboats, using random forest, decision tree and integrated learning methods for analysis. First, data cleaning is performed to remove missing data. Then, the data are analyzed to find that the make, model, size, time and regional factors of sailboats may affect their listing prices. In terms of regional characteristics, annual income per capita was extracted as one of the influencing factors. Using all available characteristics, regression trees and random forest regression models were built and R2 was used as an evaluation criterion for model prediction accuracy. Finally, the R2 value of the monohull sailboat listing price prediction model was calculated to be 0.84, and the R2value of the catamaran listing price prediction model was 0.99.In addition, this paper also involves the exploration of other features. Ultimately, this paper provides an estimate of the listed price per sailboat and discusses the issue of estimation accuracy.