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

Is your Statement Purposeless? Predicting Computer Science Graduation Admission Acceptance based on Statement Of Purpose