Prediction of used car prices using back propagation neural network model based on mean encoding

Abstract

With the growth of the used car market and the development of e-commerce platforms, the need for accurate valuation of used car prices is becoming more urgent. Accuracy of price evaluation is the key to the success of used car transactions. At present, the common methods are manual experience method, Monte Carlo method, etc. Among them, manual experience method and multi-attribute decision method are more mature and widely used in traditional pricing method, but they have some disadvantages such as large computation amount and low accuracy. Aiming at the above problems, a BP neural network model based on mean encoding is designed in this paper. After extracting the features of the model, BP neural network is used to study the pre-processing data to predict the price for the network output. In this paper, a real used car trading dataset was used to test the model. The 2 R error is 0.976. Compared with the SVM and the decision tree model, this model is more accurate.