Prediction Method of Used Car Price Based on Weighted Combination Model
In order to improve the accuracy and reliability of used car price prediction,a weighted combination method based on neural network and Bayesian optimization LightGBM algorithm is proposed to predict the price of a used car with multiple influ-encing factors.First,data preprocessing is performed on the original data set.Secondly,feature engineering is conducted on the pre-processed data set to obtain data sets suitable for training neural networks and tree models.Then,several machine learning algo-rithms optimized by neural network and Bayesian optimization are employed for training to obtain the network model.Finally,the models are combined and compared with a single model.The prediction results show that the weighted combination model of the pro-posed neural network and the improved LightGBM algorithm has stronger predictive ability than the single model and other combina-tion models.
LightGBM algorithmneural networkBayesian optimizationmachine learningcombined model prediction
国家科技期刊平台
NSTL
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