METHOD AND SYSTEM FOR PREDICTING TASK COMPLETION OF A TIME PERIOD BASED ON TASK COMPLETION RATES OF PRIOR TIME PERIODS USING MACHINE LEARNING

A request is received from a client for determining task completion of a first set of tasks associated with attributes, the first set of tasks scheduled to be performed within a first time period. For each of the attributes, a completion rate of one or more of a second set of tasks is calculated that are associated with the attribute. The second set of tasks has been performed during a second time period in the past. An isotonic regression operation and/or temporal smoothing are performed on the completion rates associated with the attributes of the second set of tasks that have been performed during the second time period to calibrate the completion rates. Possible completion for the attributes of the first set of tasks to be performed in the first time period is calculated based on the calibrated completion rates of the second set of tasks.