This paper deals with the classification of clients by using machine learning methods in order to identify clients who would accept a term deposit service. The clients are classified in two groups: potential clients who accept and those who refuse the offered term deposit service. The information used to create a model of neural networks and the decision tree model were collected by telemarketing in the period between 2008 and 2013 by Portuguese banks. At the end of the marketing campaigns, the percentage of acceptations of the offered term deposit was 11.27%, which was not efficient enough for the banks. The decision tree model used in the paper was used for the classification problem while the neural networks model, in addition to the classification problem, also solved the forecasting problem. In order to increase the acceptance rate of the offered term deposit service in future campaigns, several neural network and decision tree models were created to finally choose the most successful model. The most important indicator of the success of the model was the overall classification accuracy rate on the validation sample. In addition to the results of all models, a confusion matrix of the best networks, decision trees, and their sensitivity analysis will be shown. The two best models will be compared in order to choose the best model recommended for use with the guidelines for the use of the model and for further research.