Improving graduation rates is one of the biggest missions in many universities across the country and it is surely the case on the campus of this institution. The work here presents a statistical tool box to use early academic performance as a predictor for graduation with logistic regression and machine learning techniques. The methods described in this paper utilized data from one academic cohort across 6 years to identify significant student academic characteristics that are related to graduation. The model can then be applied to current students finishing their freshmen year and assign probabilities to successfully graduate in a pre-determined framework. The study and the significant factors are specific to the institutions' campus but the model allows the study to be replicated on any campus to support graduation initiatives. Early interventions can be most beneficial for students to realign and reorganize their academic path as needed and in our study, results show that total credits accumulated by the end of first year and retention at the end of first year have a significant positive impact on graduation success.