Both algorithms were trained, with highly acceptable results. Both algorithms had over 90% accuracy during training, with the KNN having 95.55% accuracy and the ANN scoring 96.79% over the training data. Figures 10 and 11 show the confusion matrices generated with the test data. While the KNN algorithm did have minor errors in classifying non-anomalous **data** and curves, the ANN had trouble classifying only curves. Thus, the number 0 represents healthy data; number 1 represents possible potholes, number 2 speed bumps, and number 3 harsh curves.  Lastly, both algorithms were given 85% of the whole dataset to train and 15% to test. These tests consisted of accuracy for training, and F1-Score for test data, as well as a confusion Matrix, to determine the specific classifications in which they did not perform well enough.