Research on a Hybrid Prediction Model for Purchase Behavior Based on Logistic Regression and Support Vector Machine

In recent years, online retail has maintained rapid growth, and websites are rich in user behavior data. The operation behaviors of users on the e-commerce platform can reflect user preferences. How to use user behaviors to mine user preferences has become the focus of academia and industry, and many research results have been achieved. In many cases, by fusion training two or more different algorithms, the generalization ability of the algorithm can be significantly improved to improve the prediction effect. This paper combines the fusion of logistic regression and support vector machine algorithms to construct a hybrid prediction model for user buying behavior, and conducts an empirical study on the effectiveness of the model. The empirical results show that the fusion model has better prediction effect than the single model.