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- W4387163604 abstract "The value of sailboats varies with age and market conditions. In this paper, a price prediction model is developed to help sailboat brokers in Hong Kong (SAR) to open up the second-hand sailboat market. We expect to provide the most accurate assessment of the transaction price of used sailboats under various objective conditions for sailboat brokers and clients. We first collected information on used sailboats from 2005-2019, and classified, qualitatively analyzed and quantitatively analyzed the data to obtain several influential indicators. Then, we performed ridge regression analysis and prediction, and found that the fit coefficient of ridge regression was 0.76 using python, which proved the effectiveness of using ridge regression model to predict prices. Next, the CatBoost algorithm was used for 10,000 machine learning training sessions and the fit coefficient was increased to 0.9. Finally, historical data of sailboats were used for the fit and the error was found to be within 2%, laying the foundation for its prediction of prices. Then, we selected a subset of 16 data-rich sailboats and combined with Hong Kong economic data to perform ridge regression for monohull and catamaran to predict their prices in Hong Kong and compare them with the actual values; combining with Hong Kong economic data finally concluded that catamaran sailboats are more sensitive to regional effects, which is very different from the predicted results." @default.
- W4387163604 created "2023-09-30" @default.
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- W4387163604 date "2023-08-18" @default.
- W4387163604 modified "2023-09-30" @default.
- W4387163604 title "CatBoost Ridge Regression Based Sailboat Price Forecasting" @default.
- W4387163604 doi "https://doi.org/10.1109/icsece58870.2023.10263515" @default.
- W4387163604 hasPublicationYear "2023" @default.
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