Price Prediction Study of Used Sailboat Based on Random Forest Regression Model

Miao Liu; Yue Kong; Jun Liu

doi:10.54097/hset.v70i.12147

Authors

Miao Liu
Yue Kong
Jun Liu

DOI:

https://doi.org/10.54097/hset.v70i.12147

Keywords:

Random Forest Regression; Multiple linear regression; Used Sailboat.

Abstract

As an ancient means of transportation, second-hand sailing boats are very frequently traded in the market. To accurately price used sailboats, this paper uses multiple linear regression to obtain variables that significantly affect prices based on preprocessed data, and removes variables with lower correlation. Subsequently, the pricing of used sailboats was predicted using random forest regression. To visualize the results, two methods are used: feature importance, and comparing the predicted results with the real results. Finally, the influence of different geographical areas on the price of sailing use is studied to help and guide governments and enterprises to develop development strategies and find effective ways to develop cities and regions more effectively.

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References

Cai Liming, Lu Chunxia. Application of ARIMA model in predicting second-hand ship prices [J]. China Shipping, 2008 (06)

Luo Fucai. Research on Ship Assets Appraisal [D]. Dalian University of Technology, 2011

Liu Yang, Gao Jin. Constraints and Countermeasures for Mortgage Financing of Secondhand Ships [J]. Foreign Economic and Trade Journal, 2012,19 (01): 114-115

M Beenstock, A Vergottis.Econometric modeling of world shipping [M]. Chapman & Hall,1st edition, 1993.

Cai Gejing, Fu Haibin, Jiang Renbin, et al. Fishery Economy Prediction and Optimization Based on ARIMA Model [J]. Computer and Modernization, 2019 (4): 5.

AJia Pengxiang. LightGBM-based price prediction of used cars [D]. Jinan: Shandong Normal University, 2021.

Jia Pengxiang. LightGBM-based price prediction of used cars [D]. Jinan: Shandong Normal University, 2021.

Lu W X,Wu H C,Wan L Y. Prediction of precipitation by PLS based on fused random forest algorithm [J]. Statistics and Decision Making, 2020, 36(18): 27-31.

Research and application of online machine learning algorithm under big data [J]. Science and Technology Innovation Herald, 2020, 17 (23): 3.

Tao Y, Du J L. Long- and short-term memory network temperature prediction based on random forest [J]. Computer Engineering and Design, 2019, 40(3): 737-743.

Chen Zhenyu. Research and Discussion on Machine Learning Algorithms Based on Big data Technology [J]. Archives and Periodicals, 2020, 000 (021): 201.

Tao Yun Cao. A study of variable importance based on random forest [J]. Statistics and Decision Making, 2022, 38(4): 60-63.