Machine learning techniques to predict geographic talent flow

A method comprising:

• storing a plurality of entity profiles;
• based on a particular period of time:
• analyzing a first subset of the plurality of entity profiles to determine a first number of entities that have moved to a particular region during the particular period of time;
• analyzing a second subset of the plurality of entity profiles to determine a second number of entities that have moved from the particular region during the particular period of time;
• causing data about the first number of entities and the second number of entities to be presented on a computer display;
• for each entity corresponding to the first number of entities:
• using an entity profile corresponding to said each entity to identify one or more first skills of said entity;
• updating a skill inflow counter for each skill of the one or more first skills;
• generating first skill data based on the skill inflow counter for each skill of the one or more first skills;
• causing the first skill data to be presented on the computer display;
• for each entity corresponding to the second number of entities:
• using an entity profile corresponding to said each entity to identify one or more second skills of said entity;
• updating a skill outflow counter for each skill of the one or more second skills;
• generating second skill data based on the skill outflow counter for each skill of the one or more second skills;
• causing the second skill data to be presented on the computer display;
• wherein the method is performed by one or more computing devices.

One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause:

• storing a plurality of entity profiles;
• generating training data based on a first subset of the plurality of entity profiles, wherein at least a portion of the training data is labeled indicating whether a corresponding entity has moved to or moved from a region;
• generating a machine-learned prediction model based on the training data;
• using the machine-learned prediction model to predict a first number of entities who will move to a particular region and a second number of entities who will move from the particular region;
• for each entity that is predicted to move to the particular region, and for each entity that is predicted to move from the particular region:
• analyzing an entity profile of said each entity to identify one or more skills that are associated with said each entity;
• and
• for each skill of a plurality of skills, determining a total number of instances, in the multiple entity profiles, of said each skill;
• and
• causing talent flow data, based on the first number of entities and the second number of entities, and skill data, based on the total number of instances of at least one skill in the plurality of skills to be presented on a computer display.