Systems and methods for selecting a machine learning model that predicts a subscriber network experience in a geographic area

Abstract

A device may receive network data identifying key performance indicators associated with a base station and user equipment (UEs), UE data identifying UE data records and UE locations, and geographic data identifying a geographic area and features of the geographic area. The device may correlate the network data, the UE data, and the geographic data to generate correlated data. The device may process, the correlated data, with a plurality of machine learning models, to generate a corresponding plurality of results, and may evaluate the plurality of results, with prediction models, to generate a set of results. The device may compare classification cost function weighted predictions and the set of results to generate comparisons, and may select a machine learning model, for the geographic area and from the plurality of machine learning models, based on the comparisons. The device may implement the machine learning model for the geographic area.