A machine learning method performed by a communication network monitoring device, the method comprising: receiving an incoming signaling record having radio signal attributes from a user equipment, UE, in a cellular communication network; determining whether the UE incoming signaling record includes Global Positioning Signaling, GPS, data; and respectively, either: responsive to determining that the UE incoming signaling record includes the GPS data, training a machine learning model by using the radio signal attributes and the GPS data, the machine learning model for determining a location of future UE in the communication network; or responsive to determining that the GPS data is not included in the UE incoming signaling record, determining a geolocation for the UE by applying at least the radio signal attributes to a previously trained machine learning model; wherein the radio signal attributes include a Timing Advance, TA, value; and wherein, responsive to determining that the UE incoming signaling record does not include the GPS data, the method further comprises: determining a corrected TA value for the TA value included in the incoming signaling record by applying the radio signal attributes included in the UE incoming signalling record to the previously trained machine learning model; and determining the geolocation for the UE by further applying the corrected TA value to the previously trained machine learning model. receiving an incoming signaling record having radio signal attributes from a user equipment, UE, in a cellular communication network; determining whether the UE incoming signaling record includes Global Positioning Signaling, GPS, data; and respectively, either: responsive to determining that the UE incoming signaling record includes the GPS data, training a machine learning model by using the radio signal attributes and the GPS data, the machine learning model for determining a location of future UE in the communication network; or responsive to determining that the GPS data is not included in the UE incoming signaling record, determining a geolocation for the UE by applying at least the radio signal attributes to a previously trained machine learning model; wherein the radio signal attributes include a Timing Advance, TA, value; and wherein, responsive to determining that the UE incoming signaling record does not include the GPS data, the method further comprises: determining a corrected TA value for the TA value included in the incoming signaling record by applying the radio signal attributes included in the UE incoming signalling record to the previously trained machine learning model; and determining the geolocation for the UE by further applying the corrected TA value to the previously trained machine learning model. responsive to determining that the UE incoming signaling record includes the GPS data, training a machine learning model by using the radio signal attributes and the GPS data, the machine learning model for determining a location of future UE in the communication network; or responsive to determining that the GPS data is not included in the UE incoming signaling record, determining a geolocation for the UE by applying at least the radio signal attributes to a previously trained machine learning model; wherein the radio signal attributes include a Timing Advance, TA, value; and wherein, responsive to determining that the UE incoming signaling record does not include the GPS data, the method further comprises: determining a corrected TA value for the TA value included in the incoming signaling record by applying the radio signal attributes included in the UE incoming signalling record to the previously trained machine learning model; and determining the geolocation for the UE by further applying the corrected TA value to the previously trained machine learning model. determining a corrected TA value for the TA value included in the incoming signaling record by applying the radio signal attributes included in the UE incoming signalling record to the previously trained machine learning model; and determining the geolocation for the UE by further applying the corrected TA value to the previously trained machine learning model.