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Automated valuation of used sailboat prices based on random forest regression modeling
- 1 Xi'an Jiaotong-liverpool University
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Abstract
This study presents a machine learning method for regression prediction of used sailboat prices. The dataset contains attributes such as brand, length, year, and listing price of the sailboat, and the dataset is preprocessed by removing irrelevant fields and normalizing the data. A random forest model is constructed and evaluated against several models such as gradient boosting and neural networks through k-fold cross-validation. Random Forest performs well compared to other models. The ensemble approach of the algorithm effectively modeled the complex nonlinear relationships in the data. Rigorous validation ensures the generalizability of the model. The Random Forest model outperforms traditional manual assessments in terms of the accuracy of price assessments. This data-driven solution allows customers to value sailboats on their own and avoid paying excessive fees. It also allows sailboat companies to develop automated pricing systems to speed up operations. This research provides a powerful machine-learning approach for accurately predicting used sailboat prices. These techniques can be extended to other regression tasks. Further work includes refining the model and deploying real-world applications.
Keywords
machine learning, regression prediction, used sailboat prices, random forest
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Cite this article
Ju,M. (2024). Automated valuation of used sailboat prices based on random forest regression modeling. Applied and Computational Engineering,36,1-6.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation
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