Research And Application Of Influencing Factors Of Second-hand Car Preservation Rate

In recent years,China's economy has grown rapidly,and domestic car ownership is also growing steadily.Due to the change of consumption concept,more and more people choose to buy second-hand cars as travel tools.The demand for second-hand car market is increasing and its market scale is also expanding.Due to the rapid implementation of the "Internet +" strategy,the second-hand car trading market in China has entered a new stage of development.All the data indicate that the development of second-hand car market in China is good and has a very broad space for development.However,compared with the second-hand car market in foreign countries,the second-hand car market in China started late,and there are still some problems.In the second-hand car transaction,it is crucial to make a reasonable valuation of the second-hand car's hedging rate for both used car buyers and dealers.Therefore,quickly and accurately assessing the value-added rate of used cars not only accelerates the speed of the used car market,but also maximizes the interests of buyers and sellers.At present,the evaluation of used cars is mainly carried out by the evaluators based on their own experience,and the evaluation is somewhat random.The research of data mining or other methods to establish used car evaluation models are still in the preliminary stage of exploration,so far,a second-hand vehicle evaluation model with convenient operation and high accuracy has not been found.This thesis will learn from the experience of foreign vehicle price assessment,use data mining method to predict the second-hand car hedging rate,and establish a reasonable prediction model of the second-hand car hedging rate.Based on the method of web crawler,more than 20,000 second-hand vehicle source information was collected from the automobile home website,and 25 key factors which may affect the price of second-hand cars were selected,including brand name,time of registration,and mileage.Firstly,the variables were initially screened by traditional feature selection,Boruta algorithm and Lasso regression.A total of 15 variables are selected for further analysis.Then,the random forest regression algorithm and GBDT algorithm in machine learning method are mainly selected to establish the prediction model of the preservation rate of used cars.Finally,the ten-fold cross-validation method is used to calculate the prediction results of the two models.By comparing the errors of the two models,it is found that the prediction errors of random forests are smaller,the prediction accuracy is higher and the prediction effect is better.The results indicate that it is worth popularizing to use the random forest regression method to establish the prediction model of the preservation rate of used cars.Judging from the importance variables,the time of branding,brand,mileage and car system are the main factors affecting the value of used car.