Prediction of The Price of Second-hand Sailboat Based on XGboost Regression Model

DOI

10.2991/978-94-6463-262-0_25How to use a DOI?

Keywords

Second-hand sailboat pricing; XGboost regression; Random forest

Abstract

With the development of economy and society, the flow of second-hand sailboats is increasing day by day. The sales market of second-hand sailboats has great potential. Studying the factors that affect the pricing of second-hand sailboats will help merchants to formulate accurate marketing strategies and obtain rich commercial profits. A comprehensive comparison of used sailboat price models developed by the random forest regression model and the XGBoost regression model using MSE, RMSE, MAE, and MAPE based on pre-processing and correlation analysis of advertising data and relevant supplemental data for approximately 3,500 sailboats of 36 to 56 feet in length sold in Europe, the Caribbean, and the United States in December 2020, concluded that The XGBoost regression model performs best in this problem. Based on the XGBoost regression model, the analysis focuses on the influence of region on used sailboat prices and makes a specific analysis of used sailboat prices in Hong Kong (SAR), with a view to providing a reference for pricing issues in the used sailboat market in Hong Kong.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiuran Nie
PY  - 2023
DA  - 2023/10/09
TI  - Prediction of The Price of Second-hand Sailboat Based on XGboost Regression Model
BT  - Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023)
PB  - Atlantis Press
SP  - 212
EP  - 222
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-262-0_25
DO  - 10.2991/978-94-6463-262-0_25
ID  - Nie2023
ER  -