Abstract: Systems and methods are disclosed for determining optimal resource allocation options for network systems. The method includes receiving real-time data associated with a plurality of systems; generating one or more features associated with a first system of the plurality of systems based on at least a portion of the real-time data; generating, via input of one or more features into a machine learning model, a prediction that the first system is approaching a capacity threshold; determining one or more probabilities associated with respective one or more second systems of the plurality of systems, each of the one or more probabilities indicating a preferability of the respective second system for reallocating one or more resources from the first system; and simulating the reallocation across the plurality of systems based on the one or more probabilities associated with the respective one or more second systems.

Abstract: Various embodiments of the present disclosure provide event prediction using conditional probability modeling that improves the functionality of a computer in various aspects. The techniques comprise receiving a request that identifies a class domain for an entity group, identifying a subset of the entity group that satisfies defined criterion associated with the class domain and an interval of time, determining a set of binary indicator data structures for respective entities of the subset based on the class domain and an entity data element set for the respective entities, applying a conditional probability model to the set of binary indicator vectors to determine an event score for a defined event associated with the entity group, and initiating the performance of a prediction-based action associated with the defined event based on the event score.

Abstract: Systems and methods are disclosed for determining optimal resource allocation options for network systems. The method includes receiving real-time data associated with a plurality of systems; generating one or more features associated with a first system of the plurality of systems based on at least a portion of the real-time data; generating, via input of one or more features into a machine learning model, a prediction that the first system is approaching a capacity threshold; determining one or more probabilities associated with respective one or more second systems of the plurality of systems, each of the one or more probabilities indicating a preferability of the respective second system for reallocating one or more resources from the first system; and simulating the reallocation across the plurality of systems based on the one or more probabilities associated with the respective one or more second systems.