Abstract
With the rapid development of the economy, the automobile industry has developed as the world’s no. 1 automobile consumer market and the world’s largest consumer potential market. The growth of domestic auto production and sales has an obvious driving effect on auto finance. The penetration rate of auto finance has increased from 13% five years ago to nearly 40% now. The purpose of this paper is to optimize the model algorithm based on multi-source heterogeneous and XBOOST vehicle sales prediction model. In this paper, the application of multi-source heterogeneity and XBOOST algorithm to the auto sales prediction model is discussed by using the sample data of auto sales of A Auto company, and the relevant characteristics of customers are studied to establish the logistic regression model of sales prediction and take it as the standard. Finally, the optimal parameter combination is explored to optimize the model effect. Combined with the evaluation index of machine learning classification model, the performance of each model is compared. The results showed that the difference between forecast and actual sales volume was only 12.1%. This is helpful for the enterprise to provide reference for the forecast of car sales.
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Zhang, F., Yang, J., Guo, Y., Gu, H. (2021). Multi-source Heterogeneous and XBOOST Vehicle Sales Forecasting Model. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1283. Springer, Cham. https://doi.org/10.1007/978-3-030-62746-1_50
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DOI: https://doi.org/10.1007/978-3-030-62746-1_50
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