Business Analytics for Used Car Price Prediction with Statistical Models
Authors
Yufei Chen1, †, Chenle Li2, †, Minglu Xu3, *, †
1College of liberal arts, Xi’an University, Xi’an, Shaanxi, 710075, China,2198971840@qq.com
2Art & Science, University of Toronto, Nanjing, Jiangsu, 210000, China,joanne.lichenle@gmail.com
3School of business, Macao university of science and technology, Xu Zhou, Jiang Su, 221000, China,1176789750@qq.com
†
These authors contributed equally
*Corresponding author. Email: guanghua.ren@gecacademy.cn
Corresponding Author
Minglu Xu
Available Online 15 December 2021.
- DOI
- 10.2991/assehr.k.211209.090How to use a DOI?
- Keywords
- Car Price Prediction; Model; Statistics; Business Analytics; Clustering
- Abstract
With the development of the used car market, the demand for a more accurate and scientific price prediction model of used cars becomes urgent. This paper uses multiple linear regression, decision tree and random forest to build up the automobile price forecasting model. We use means to cluster cars and find out that some factors like power, kilometers, gearbox have an influence on the price. According to the analysis, we find out that random forest has the best prediction performance, make sure R2 reaches 0.92 will be enough.
- Copyright
- © 2021 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Yufei Chen AU - Chenle Li AU - Minglu Xu PY - 2021 DA - 2021/12/15 TI - Business Analytics for Used Car Price Prediction with Statistical Models BT - Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021) PB - Atlantis Press SP - 542 EP - 547 SN - 2352-5428 UR - https://doi.org/10.2991/assehr.k.211209.090 DO - 10.2991/assehr.k.211209.090 ID - Chen2021 ER -