Method and System for Workforce Elasticity Indexing

A machine learning predictive modeling system, comprising:

• a computer system;
• one or more processors running on the computer system, wherein the one or more processors aggregate sample data regarding a plurality of factors associated with employment;
• perform iterative analysis on the data using machine learning to construct a predictive model;
• populate, using the predictive model, a database with predicted employment values for predefined geographic regions;
• convert the predicted employment values in the database into percentages of observed employment values for the predefined geographic regions over a specified time period to create indices of workforce elasticity for each geographic region;
• and rank order the predefined geographic regions according to their indices of workforce elasticity.

A computer program product for machine learning predictive modeling, the computer program product comprising:

• a persistent computer-readable storage media;
• first program code, stored on the computer-readable storage media, for aggregating sample data regarding a plurality of factors associated with employment;
• second program code, stored on the computer-readable storage media, for performing iterative analysis on the data using machine learning to construct a predictive model;
• third program code, stored on the computer-readable storage media, for populating, using the predictive model, a database with predicted employment values for predefined geographic regions;
• fourth program code, stored on the computer-readable storage media, for converting the predicted employment values in the database into percentages of observed employment values for the predefined geographic regions over a specified time period to create indices of workforce elasticity for each geographic region;
• and
• fifth program code, stored on the computer-readable storage media, for rank ordering the predefined geographic regions according to their indices of workforce elasticity.