An NTUA Diploma Thesis titled “Imbalanced learning analysis for driving behaviour prediction using naturalistic driving data” was recently presented by Antonis Kostopoulos. For the purpose of this Diploma Thesis data was collected through the telematics company OSeven, in order to classify and predict driving behaviour in terms of harsh accelerations and brakings occurences. More precisely this thesis intends to determine the most crucial predictors for the occurrence of harsh events, through a feature selection process and to identify two safety levels for harsh accelerations and brakings using Machine Learning techniques. The imbalanced classification results showcased that the total driving distance was the more impactful variable to harsh events occurence, whilst the best techniques for this particular imbalanced learning process, were achieved by Gradient Boosting and Multilayered Perceptrons algorithms.