1. A computer-implemented method, comprising: receiving, by a computing device, data relating to a plurality of employment positions, wherein the data relating to the plurality of employment positions is received from a plurality of employment position sources; pre-aggregating, by the computing device, the data relating to the plurality of employment positions received from the plurality of employment position sources; converting the pre-aggregated data into a set of sparse vectors from one or more sequence file formats; applying a machine learning technique to train a classifier data model using the set of sparse vectors, wherein the classifier data model is configured to determine an output employment source through which a job seeker will most likely be hired for an input employment position, and wherein the output employment source is determined based on an average number of applications per hire through the output employment source for similar employment positions to the input employment position; receiving a user input from the job seeker corresponding to an user-selected employment position; and applying the classifier data model to the user-selected employment position to determine the employment source through which the job seeker will most likely be hired for the user-selected employment position.