Prediction of Bank Product Subscription Behavior Based on Random Forest Algorithm

Based on the background of bank customers' purchase of products, this study uses random forest to predict whether customers buy bank products and discusses the advantages of random forest compared with other commonly used machine classification models. By selecting the product data set of Aliyun Tianchi Bank customers and using python for data processing, this study optimizes the nestimators, maxfeatures and other important parameters in random forest, so as to achieve the optimal effect of random forest classification. Meanwhile, by comparing KNN, logistic regression, support vector machine, Single decision tree model, the confusion matrix and ROC curve were used to evaluate the model performance. The final experimental results show that the random forest model with optimized parameters has better classification effect than other binary classification models.