The aim of this thesis is to forecast the evolution of the prepayment rate in a mortgage portfolio. In the Netherlands, people with a loan have the possibility to repay (part of) their outstanding loan before the due date. These prepayments make the length of the portfolio of loans stochastic, which creates problems in the refinancing policy of the bank, and affects the Asset & Liability Management. Moreover, interest rate risk arises from prepayments, meaning that being able to forecast the prepayment rate can increase the performance of the hedging strategy of a bank. Given the magnitude of the mortgage portfolio in the balance sheet of a bank, estimating the prepayment rate is therefore crucial. There are two kinds of models in the literature, the optimal prepayment model, which sees prepayment as a consequence of rational behavior (e.g. prepayments are always exercised at an optimal time), and the exogenous model which also takes into account other macroeconomic variables, client specifics and loan characteristics. Our focus will be on the second kind of techniques, precisely we will approach the problem as a classification task that will be carried out with two different machine learning techniques: Random Forests and Artificial Neural Networks.Since prepayments are rare events, this leads to an imbalanced data set framework. The imbalance between classes creates complications in the development of the algorithm, hence ad hoc corrections are applied to solve them.