  O-RAN introduces intelligent and flexible network control in all parts of the
network. The use of controllers with open interfaces allow us to gather real
time network measurements and make intelligent/informed decision. The work in
this paper focuses on developing a use-case for open and reconfigurable
networks to investigate the possibility to predict handover events and
understand the value of such predictions for all stakeholders that rely on the
communication network to conduct their business. We propose a Long-Short Term
Memory Machine Learning approach that takes standard Radio Access Network
measurements to predict handover events. The models were trained on real
network data collected from a commercial O-RAN setup deployed in our
OpenIreland testbed. Our results show that the proposed approach can be
optimized for either recall or precision, depending on the defined application
level objective. We also link the performance of the Machine Learning (ML)
algorithm to the network operation cost. Our results show that ML-based
matching between the required and available resources can reduce operational
cost by more than 80%, compared to long term resource purchases.
