METHOD AND SYSTEM FOR SUMMARIZING USER ACTIVITIES OF TASKS INTO A SINGLE ACTIVITY SCORE USING MACHINE LEARNING TO PREDICT PROBABILITIES OF COMPLETENESS OF THE TASKS

Activity data of a set of tasks as a training set is obtained from a list of communication platforms associated with the tasks. For each of the tasks in the training set, a set of activity metrics is compiled according to a set of predetermined activity categories based on the activity data of each task. The activity metrics of all of the tasks in the training set are aggregated based on the activity categories to generate a data matrix. A principal component analysis is performed on the metrics of its covariance matrix to derive an activity dimension vector, where the activity dimension vector represents a distribution pattern of the activity metrics of the tasks. The activity dimension vector can be utilized to determine an activity score of a particular task, where the activity score of a task can be utilized to estimate a probability of completeness of the task.