基于大数据挖掘的金融产品订购预测

onsumption is an important and primary factor in stimulating economic production and the most important link in production and sales. For financial product sellers, it is important to identify the user population and analyze the user characteristics of the ordered products. In this paper, the data set containing user characteristics and whether to order financial products is divided into training set and verification set. The user profile is displayed by data visualization. Based on random forest algorithm, xgboost algorithm, logic regression algorithm, naive Bayes algorithm and gradient lifting tree algorithm, a prediction model is established to determine whether users order. The best prediction model is determined through the comparison of model effects. The results show that the final model has high accuracy and can effectively help company managers or decision-makers to predict whether users choose to order products.