Recent developments of data monitoring and analytics technologies in the context of wireless networks will boost the capacity to extract knowledge about the network and the users. On the one hand, the obtained knowledge can be useful for running more efficient network management tasks related to network reconfiguration and optimization. On the other hand, the extraction of knowledge related to user needs, user mobility patterns and user habits and interests can also be useful to provide a more personalized service to the clients. Focusing on user mobility, this paper presents a methodology that predicts the future Access Point (AP) that the user will be connected to in a Wi-Fi Network. The prediction is based on the historical data related to the previous APs which the user connected to. Different approaches are proposed, according to the data that is used for prediction, in order to capture weekly, daily and hourly user activity-based behaviours. Two prediction algorithms are compared, based on Neural Networks (NN) and Random Forest (RF). The methodology has been evaluated in a large Wi-Fi network deployed in a University Campus.