Education, Science, Technology, Innovation and Life
Open Access
Sign In

Big Data Automobile Price Prediction Based on Elastic Network Regression Model

Download as PDF

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

Author(s)

Shengyu Yan 1, Yi Xu 2

Affiliation(s)

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

ABSTRACT

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.

KEYWORDS

Elastic Network Regression; Used Car; Prediction

CITE THIS PAPER

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: http://dx.doi.org/10.23977/ferm.2023.061101.

REFERENCES

[1] Hoerl A E, Kennard R W. Taylor & Francis Online: Ridge Regression: Applications to Nonorthogonal Problems - Technometrics - Volume 12, Issue 1[J]. Technometrics [2023-08-15]. 
[2] Tibshirani R. Regression shrinkage and selection via the lasso [J]. Journal of the Royal Statistical Society, Series B, 1996, 58(1). DOI:10. 1111/j. 2517-6161. 1996. tb02080. x. 
[3] Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. 2001[J]. Journal of the Royal Statistical Society, 2004, 167(1):192-192. DOI:10. 1111/j. 1467-985X. 2004. 298_11. x. 
[4] Jiang Shiqi, Dai Jiajia. An improved elastic net estimation of Logistic regression Model [J]. Mathematical Theory and Application [2023-08-15]. 
[5] Jeon S , Hong B , Chang V .Pattern graph tracking-based stock price prediction using big data[J].Future Generation Computer Systems, 2017, 80(MAR.):171-187.DOI:10.1016/j.future.2017.02.010.
[6] Han Qing, Wang Ziqi, Geng Wenjing. Research on Stock price based on Elastic net-autoregressive model [J]. Guangxi Quality Supervision Guide, 2020(10):2.

Downloads: 18955
Visits: 359608

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.