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  4. Vol 7, Issue 10, 2023

A Study on the Transaction Price of Second-hand Sailboats Based on the Random Forest Regression Model

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DOI: 10.23977/acss.2023.071001 | Downloads: 19 | Views: 337

Author(s)

Xinyue Zhang 1, Xuanyi Xiang 1

Affiliation(s)

1 Sichuan University-Pittsburgh Institute, Chengdu, 610044, China

Corresponding Author

Xinyue Zhang

ABSTRACT

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 Model

CITE 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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