First, the data was explored by plotting it in various ways along with their corresponding labels. Once familiarized with the data, we focused on feature selection, achieved through the use of tsfresh. After extracting numerous potential features, the best features were empirically selected. Following the prediction of speed and axis direction placement through clustering, additional feature engineering was performed. The model chosen for predictions was Random Forest Classification.