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  4. Vol 7, Issue 9, 2023

Analysis of the Price Influence Factors of Used Audi Cars Based on Ridge Regression Model

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DOI: 10.23977/acss.2023.070915 | Downloads: 43 | Views: 343

Author(s)

Yi Xu 1, Shengyu Yan 2

Affiliation(s)

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

Corresponding Author

Yi Xu

ABSTRACT

This paper uses the ridge regression model to explore the factors affecting the price of second-hand Audi cars. A large number of used Audi car feature data were collected, including the Model, Year, Mileage and other characteristics, as well as their corresponding price. In general, since the development of these factors is homogeneous, so most of their data have multicollinearity problems. If OLS is used to estimate the parameters of the model, the parameters obtained may be difficult to objectively and accurately reflect the actual situation [6]. Using ridge regression model for modeling and prediction to solve the multicollinearity problem by introducing a regularization term. When building the model, this text considered the correlation between features and choose appropriate regularization parameters. The experimental results show that through the ridge regression model, this text analyzed the importance of the characteristics of the regression model, and found that the regression coefficient of Mileage Year and Tax is 5.17296619, -0.60579774 and 1.46868943 respectively, indicating that mileage, age and tax are important factors affecting the price of second-hand Audi cars [3]. This study provides a reliable method for predicting the price factors of the used Audi car market, which has an important reference value for both buyers and sellers.

KEYWORDS

Used Car; Mileage; Year; Ridge Regression Model

CITE THIS PAPER

Yi Xu, Shengyu Yan, Analysis of the Price Influence Factors of Used Audi Cars Based on Ridge Regression Model. Advances in Computer, Signals and Systems (2023) Vol. 7: 113-120. DOI: http://dx.doi.org/10.23977/acss.2023.070915.

REFERENCES

[1] Liu Hongyong, Hu Jian, Wang Peng, Guo Ji. Study on the influencing factors of real estate price in Sichuan Province based on ridge regression method [J]. The Practice and Understanding of Mathematics, 2014, 44 (12): 72-78.
[2] Wen-Zhu K , Lin L .Analysis and Prediction of the Factors That Influence the Price of the Second-hand House in Nanning[J].Journal of Guangxi Academy of Sciences, 2012.
[3] Zhang Kun, Zhou Yunlong. Main factors influencing the price of second-hand car and purchase precautions [J]. Automobile maintenance and repair, 2021, (02): 68-69.
[4] Li Yanan, Chen Jianguo. Application of Lasso and ridge regression in rice genome-wide prediction [J]. Journal of Hubei University (Natural Science Edition), 2020, 42 (04): 384-389.
[5] Liang Jie, Chen Jiahao, Zhang Xueqin, Zhou Yue, Lin Jiajun. Anomaly detection based on single-thermal encoding and convolutional neural networks [J]. Journal of Tsinghua University (Natural Science Edition), 2019, 59 (07): 523-529.
[6] Wang Yan. Analysis of the influencing factors of the retail sales of social consumer goods in Jiangsu Province based on ridge regression [J]. Journal of Xuzhou Institute of Technology (Social Science Edition), 2019, 34 (02): 52-60.

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