Default Prediction of Automobile Credit Based on Support Vector Machine
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초록
Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occurfrequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successfulprediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then therandom forest is used for feature selection, and then the sample data are randomly grouped. Finally, sixprediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic,decision tree, and artificial neural network (ANN) are constructed. The results show that these six machinelearning models can be used to predict the default of automobile credit. Among these six models, the accuracyof decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. Andrandom grouping can improve the efficiency of model operation to a certain extent, especially SVM.
Abstract
Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occurfrequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successfulprediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then therandom forest is used for feature selection, and then the sample data are randomly grouped. Finally, sixprediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic,decision tree, and artificial neural network (ANN) are constructed. The results show that these six machinelearning models can be used to predict the default of automobile credit. Among these six models, the accuracyof decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. Andrandom grouping can improve the efficiency of model operation to a certain extent, especially SVM.
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