A Study on the Transaction Price of Second-hand Sailboats Based on the Random Forest Regression Model
DOI: 10.23977/acss.2023.071001 | Downloads: 19 | Views: 338
Author(s)
Xinyue Zhang 1, Xuanyi Xiang 1
Affiliation(s)
1 Sichuan University-Pittsburgh Institute, Chengdu, 610044, China
Corresponding Author
Xinyue ZhangABSTRACT
The second-hand sailboat market is booming but the prices are uncertain, which poses a significant challenge for sellers to determine the optimal selling price. To address this issue, this study employed three regression models, namely Random Forest Regression Model, Decision Tree Regression Model, and Supporting Vector Machine Regression Model, to explore the main factors affecting the pricing of second-hand sailboats, and predict the prices of second-hand sailboats. The result shows that the length of the second-hand sailboats impacts the most and the Random Forest Regression Model has the highest accuracy in predicting the transaction prices of second-hand sailboats. This prediction method can help sellers better price their boats and promote the development of the second-hand sailboat market.
KEYWORDS
Second-hand Sailboat Market, Price Prediction, Random Forest Regression Model, Decision Tree Regression Model, Supporting Vector Machine Regression ModelCITE THIS PAPER
Xinyue Zhang, Xuanyi Xiang, A Study on the Transaction Price of Second-hand Sailboats Based on the Random Forest Regression Model. Advances in Computer, Signals and Systems (2023) Vol. 7: 1-9. DOI: http://dx.doi.org/10.23977/acss.2023.071001.
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