Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and billions of dollars are spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition. What's more, most of the previous attempts to model attrition at traditional campuses using machine learning have focused on small, homogeneous groups of students. In this work, we model student attrition using a dataset that is composed almost exclusively of information routinely collected for record-keeping at a large, public US university. By examining the entirety of the university's student body and not a subset thereof, we use one of the largest known datasets for examining attrition at a public US university (N = 66,060). Our results show that students' second year re-enrollment and eventual graduation can be accurately predicted based on a single year of data (AUROCs = 0.887 and 0.811, respectively). We find that demographic data (such as race, gender, etc.) and pre-admission data (such as high school academics, entrance exam scores, etc.) - upon which most admissions processes are predicated - are not nearly as useful as early college performance/transcript data for these predictions. These results highlight the potential for data mining to impact student retention and success at traditional campuses. [For the full proceedings, see ED599096.]