UBI CAR INSURANCE RATE GRADE DETERMINATION BASED ON K-S ALGORITHM

Under the support of Internet of vehicles technology, UBI based auto insurance premium rate rating judgment has certain guiding significance for realizing accurate pricing of auto insurance premium rate and personalized demand. In this paper, K-S algorithm based on the same training set is proposed as an improved car insurance rate rating model, and the rating of the samples to be tested is determined by weighting the independent rating results of the two algorithms. The empirical results show that K-S algorithm can improve the ability to avoid the defects of kNN algorithm and SVM algorithm to some extent, and it is easy to realize in the process of establishing UBI car insurance rate classification model, with higher judgment accuracy and better classification effect.