Utilizing dynamic data sampling to implement machine learning models for projecting recurrence of periodic events

A non-transitory computer-readable medium storing executable instructions, which when executed by at least one processor, cause the at least one processor to perform operations comprising:

• determining that a target account qualifies for an account definition based on an account event occurring within a defined threshold time period, the account event being a periodic event of an unknown frequency;
• based on determining the target account qualified for the account definition, identifying a set of features associated with the target account;
• and
• based on the set of features, utilizing a recurrence prediction machine learning model to generate a recurrence score that indicates a likelihood that the account event will repeat within the target account.

A system comprising:

• one or more memory devices comprising a recurrence prediction machine learning model;
• and
• one or more processors configured to cause the system to:
• determine that a target account qualifies for an account definition based on an account event occurring within a defined threshold time period, the account event being a periodic event of an unknown frequency;
• based on determining the target account qualified for the account definition, identify a set of features associated with the target account;
• and
• based on the set of features, utilize a recurrence prediction machine learning model to generate a recurrence score that indicates a likelihood that the account event will repeat within the target account.