In recent years, computer programs used to score essays have been explored extensively, with many different approaches being developed. Most of these approaches use Natural Language Processing (NLP) techniques (Ade-Ibijola et al., 2012), a field of machine learning often used to analyze and understand text. These approaches fall under the name of Automated Essay Scoring (AES), which typically assesses essay quality with a single score (Ke and Ng, 2019). This paper proposes a natural language processing (NLP) model which predicts the quality of a college application essay, which is proximally measured through a college’s acceptance rate. Key essay factors include the number of grammar mistakes, sophistication of writing, repetition, and the text of the essay. Multiple different models were tested. A Random Forest Classifier relying solely on grammar, sophistication of writing, and repetition metrics achieved the best performance, yielding an accuracy of 89.7%. The second-best model was a combination of an LSTM and a logistic regression model. Other models significantly underperformed, yielding accuracies in the range of 40%-60%. Ultimately, our model may help a number of students going through the college application process to understand where their essay may stand compared to other students.