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Research on Used Car Valuation Problem Based on Machine Learning

At present, the number of motor vehicles in China is increasing day by day, and the second-hand car trading market is also expanding. Due to the characteristics of “one vehicle, one condition”, it is necessary to take into account the basic configuration and use of the car body in the evaluation of used cars, and the price of used cars is difficult to estimate. Firstly, this paper classifies the used car transaction cycle based on six machine learning models AdaBoost, GBDT, XGBoost, LightGBM, CatBoost and Decision tree, filters out the key factors affecting the used car transaction on five types of data sets, compares and filters the key factors based on multiple machine learning models, and constructs a prediction model based on LightGBM prediction model based on these key factors. Moreover, we have conducted extensive experiments to demonstrate that the model can achieve a better expected effect on the formulation of used car sales strategy.