Network operation execution using hybrid machine learning

A method, comprising:

• identifying, from a database, data points associated with a profile data structure for a first time interval;
• detecting, from a data source, one or more events indicative of modifying the data points associated with the profile data structure for a second time interval;
• reconfiguring, based on the one or more events, one or more machine learning models of a hybrid machine learning model;
• generating a predicted data point associated with the profile data structure for the second time interval using the data points and the one or more events as input into the hybrid machine learning model that is reconfigured;
• determining a variance in response to comparing the predicted data point for the second time interval to the data points identified for the first time interval;
• and
• causing a payroll processing system to execute one or more computer instructions by transmitting the variance to the payroll processing system, the payroll processing system using the variance to execute the one or more computer instructions.

A non-transitory computer readable medium including one or more instructions stored thereon and executable by a processor to:

• identify, from a database, data points associated with a profile data structure for a first time interval;
• detect, from a data source, one or more events indicative of modifying the data points associated with the profile data structure for a second time interval;
• adjust, based on the one or more events, a hybrid machine learning model by adjusting parameters of one or more machine learning models of the hybrid machine learning model;
• generate, using the hybrid machine learning model that is adjusted, a predicted data point associated with the profile data structure for the second time interval;
• determine a variance between the predicted data point for the second time interval and the data points identified for the first time interval;
• and
• present, via a graphical user interface, the data points, the one or more events, and the variance.