We tested several classifiers from which the best were Random Forest, Naïve Bayes, Multilayer Perceptron, Logistic Model Tree, Hoeffding Tree (HT), and Logistic Regression. Each algorithm was tested using crossvalidation, which divides the dataset into a given number of folds and tests each fold on the remaining folds, so each fold is tested multiple times. WEKA then averaged the results for each of these tests to return the accuracy, which reduces variance in the model. After testing models with different numbers of folds for the cross-validation on different algorithms, we found that the HT with five folds performed the highest. This model had 86.8% accuracy and was also ranked among the highest in the other metrics, when compared against the other models. We exported this model from WEKA and used it for testing data from other independent datasets.