This project is to build a model in a dataset of more than 41,000 records to predict the best number of customers buying term deposits of the bank, so as to achieve the purpose of reducing the bank's investment in marketing. Perform a Chi-square test to explore and understand the relationship between the predictor variable and the target variable in the dataset, and preprocess the dataset, such as normalizing the variables in the dataset. Use SAS to build multiple prediction models, such as Decision tree, Maximal decision tree, Logistic regression, Random forest, Support Vector Machines, Neural Networks and Auto-Neural Networks. Use Area Under Curve (AUC) and The misclassification rate compares these seven models and determines the best two models as Neural Networks and Auto-Neural Networks. Utilized:SAS Enterprise Miner,Decision tree, Logistic Regression, Random Forest, Support Vector Machines, Neural Networks,AUC,Misclassification Rate,Chi-square Test