Method and System for Predictive Modeling for Dynamically Scheduling Resource Allocation

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 work scheduling, employee compensation, and employee tenure;
• perform iterative analysis on the sample data using machine learning to construct a predictive model;
• populate, using the predictive model, a database with predicted values of employee tenure in relation to work scheduling and employee compensation;
• convert the predicted values of employee tenure in the database into percentages of observed values of employee tenure for a selected group of employers over a specified time period to create indices of employee tenure;
• and rank order the selected employers according to their indices of employee tenure.

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 work scheduling, employee compensation, and employee tenure;
• second program code, stored on the computer-readable storage media, for performing iterative analysis on the sample 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 values of employee tenure in relation to work scheduling and employee compensation;
• fourth program code, stored on the computer-readable storage media, for converting the predicted values of employee tenure in the database into percentages of observed values of employee tenure for a selected group of employers over a specified time period to create indices of employee tenure;
• and
• fifth program code, stored on the computer-readable storage media, for rank ordering the selected employers according to their indices of employee tenure.