Used Sailboat Price Prediction Model Based on SSA Optimized XG-Boost Algorithm

As the economy grows and the supply and demand for new boats gradually balance, the trading volume of used sailboats has increased. The transaction price of used sailboats is affected by factors such as age, equipment, and market, making it difficult to assess and predict their value. Therefore, this paper aims to study the used sailboat market in Hong Kong and develop a mathematical model that can explain and predict the trading price of used sailboats to investigate the effect of regional effects on the trading price. In this paper, this work first pre-processed the data of sailboats by using VMD series decomposition to decompose the data containing time series and also used the XG-Boost model for data prediction, and the model can achieve a fit of over 95% in the training set, and the indicators of the model such as MAE and MAPE are also very accurate. In order to improve the accuracy of the model, this paper uses the SSA optimization method to optimize the catamaran price prediction model further and obtain a more accurate prediction model. Meanwhile, to discuss whether the regional effect is consistent on different sailboats, this paper verifies the regional effect with the help of the ANOVA method of the importance of indicator characteristics output from the XGB model. The results show that the area effect is essential and has a more significant impact on single-handed sailboats. Meanwhile, this paper calculates that the fit of the XGB model in the Hong Kong trading market still achieves the original accuracy, and the effect of GDP on the trading price of single-hull sailing boats is significantly higher than that of catamarans.