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Breast cancer is the second most prevalent type of cancer overall and the most frequently occurring cancer in women. The most effective way to improve breast cancer survival rates still lies in the early detection of the disease. An increasingly popular and effective way of doing this is by using machine learning to classify and analyze patient data to help identify signs of cancer. This paper explores a variety of machine learning techniques and compares their prediction accuracy and other metrics when using the Breast Cancer Wisconsin (Original) data set using 10-fold cross-validation methods. Of the algorithms tested in this paper, a support vector machine model using the radial basis function kernel outperformed all other models we tested and those previously developed by others, achieving an accuracy of 99%.