2.7 million Americans currently have atrial fibrillation (AFib), a heart issue that is described as a “quivering or irregular heartbeat." AFib can lead to other health issues, such as blood clots, stroke, and heart failure. Using AFib data from the MIT-BIH Atrial Fibrillation Database and the 2017 PhysioNet Challenge Dataset, as well as methods from Moody and Mark’s 1983 paper, we attempt to explore simple models using various classification methods and resampling techniques to detect AFib. Proportions of transition states between RR intervals are used as covariates in logistic regression, LDA, QDA, boosting, and XGBoost models. With 5-Fold Cross Validation, we can get up to 97.1% prediction accuracy and 97.0% sensitivity using the MIT-BIH dataset. Similar results can be seen with fewer covariates and dimension reduction techniques, which are important to note if implementation of these methods are used in real-time heart rate devices, such as Fitbits. Additionally, using covariates, such as RR interval variance and dimensions from the multi-dimensional scaling of pairwise differences in the Kolmogorov-Smirnov Tests, provide potential usefulness for AFib classification.