One or more non-transitory computer-readable media storing computer-executable instructions that upon execution by one or more processors cause the one or more processors to perform the steps of a method comprising: receiving performance data regarding user devices and network components of a wireless carrier network from multiple data sources, the performance data including one or more of network component performance data, user device performance data, social media data, alarm data, trouble ticket data, or key performance indicator data; identifying at least one network quality of service, QoS, issue affecting the network performance in one or more poor performing geographical areas, wherein the network QoS issue negatively impacts the performance data to fall below a predetermined threshold; analyzing the performance data using a trained machine learning model to determine a predicted root cause for the network QoS issue affecting the network performance, the trained machine learning model employing multiple types of machine learning algorithms to analyze the performance data; providing the predicted root cause for presentation via a user interface; and refining the machine learning model based on user feedback regarding an accuracy of the predicted root cause, the refining including retraining the machine learning model based on at least one of a training corpus that is modified based on the feedback or one or more modified algorithm selection rules; characterized in that the steps further comprise: providing a network fix prioritization for each of the network QoS issues to implement a solution that resolves the predicted root cause for the network QoS issue; and refining the machine learning model based on user feedback regarding an accuracy of the network fix prioritization, the refining including retraining the machine learning model based on at least one of the training corpus that is modified based on the feedback or one or more modified algorithm selection rules. receiving performance data regarding user devices and network components of a wireless carrier network from multiple data sources, the performance data including one or more of network component performance data, user device performance data, social media data, alarm data, trouble ticket data, or key performance indicator data; identifying at least one network quality of service, QoS, issue affecting the network performance in one or more poor performing geographical areas, wherein the network QoS issue negatively impacts the performance data to fall below a predetermined threshold; analyzing the performance data using a trained machine learning model to determine a predicted root cause for the network QoS issue affecting the network performance, the trained machine learning model employing multiple types of machine learning algorithms to analyze the performance data; providing the predicted root cause for presentation via a user interface; and refining the machine learning model based on user feedback regarding an accuracy of the predicted root cause, the refining including retraining the machine learning model based on at least one of a training corpus that is modified based on the feedback or one or more modified algorithm selection rules; characterized in that the steps further comprise: providing a network fix prioritization for each of the network QoS issues to implement a solution that resolves the predicted root cause for the network QoS issue; and refining the machine learning model based on user feedback regarding an accuracy of the network fix prioritization, the refining including retraining the machine learning model based on at least one of the training corpus that is modified based on the feedback or one or more modified algorithm selection rules. providing a network fix prioritization for each of the network QoS issues to implement a solution that resolves the predicted root cause for the network QoS issue; and refining the machine learning model based on user feedback regarding an accuracy of the network fix prioritization, the refining including retraining the machine learning model based on at least one of the training corpus that is modified based on the feedback or one or more modified algorithm selection rules.