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Big Data Automobile Price Prediction Based on Elastic Network Regression Model

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DOI: 10.23977/ferm.2023.061101 | Downloads: 26 | Views: 374


Shengyu Yan 1, Yi Xu 2


1 School of Software, Taiyuan University of Technology, Taiyuan, 030600, China
2 School of Digital Economy Industry, Guangzhou College of Commerce, Guangzhou, 511363, China

Corresponding Author

Shengyu Yan


At present, with the continuous improvement of people's living standards, cars have become an essential travel tool for every family, and may even become the third biggest life scene. At the same time, the number of cars flowing into the used car market is growing, and the used car trading market is also growing rapidly. However, the price of used cars is affected by many different factors, and there is no uniform pricing standard. In view of this, in the used car trading market, it is very important to accurately predict the price of used cars for both sellers and buyers. In this paper, the elastic network regression model is used to establish the used car price prediction model. The RMSE value of the test data is 0.089497. Among the model coefficients, the characteristics of model and year have the greatest impact on the used car price, which are 0.87491361and -0.74483197, respectively.


Elastic Network Regression; Used Car; Prediction


Shengyu Yan, Yi Xu, Big Data Automobile Price Prediction Based on Elastic Network Regression Model. Financial Engineering and Risk Management (2023) Vol. 6: 1-8. DOI:


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