  We present a quantitative, data-driven machine learning approach to mitigate
the problem of unpredictability of Computer Science Graduate School Admissions.
In this paper, we discuss the possibility of a system which may help
prospective applicants evaluate their Statement of Purpose (SOP) based on our
system output. We, then, identify feature sets which can be used to train a
predictive model. We train a model over fifty manually verified SOPs for which
it uses an SVM classifier and achieves the highest accuracy of 92% with 10-fold
cross-validation. We also perform experiments to establish that Word Embedding
based features and Document Similarity-based features outperform other
identified feature combinations. We plan to deploy our application as a web
service and release it as a FOSS service.
