Predicting College Admission Results with Machine Learning on Unstructured Online Data

College admissions in the U nited States is a complex and often opaque process, leading to uncertainty and potential unfair biases. This paper presents a novel approach to predicting college admission results using machine learning on unstructured online data. Specifically, we utilize GPT-4o to extract and structure student application details and admission outcomes from more than 4,000 posts on the r/collegeresults subreddit, demonstrating the capabilities of advanced language models in preprocessing unstructured data for machine learning tasks. We employ two distinct methods for predicting admissions results: the first combines descriptive scalars extracted by GPT-4o from unstructured text data with XGBoost and a neural network to predict the probability of acceptance into an institution selectivity tier. The second predicts admission outcomes for specific institutions and compares the effectiveness of tokenization versus descriptive scalars extracted by GPT-4o in representing text features. The models achieve promising results, with Method 1 attaining an accuracy of 91.66% and an AUC-ROC of 0.9298. The results from Method 2 also demonstrate the greater effectiveness of using tokenization (85.1±.2% accuracy) over the explicitly defined features we specified (84.3±.2% accuracy).