The main goal of this thesis is to evaluate different machine learning models in order to classify buyers of an electric or a hybrid vehicle and to identify the characteristics of the buyers in the European Union. Machine learning algorithms and techniques were adopted to analyze the dataset and to create models that could predict, with a certain accuracy, the customer’s willingness to buy an EV. Identification of the characteristics of the buyers were based on the identified most important features from the machine learning models and statistical analysis. The research consisted of exploratory and explanatory methods (mixed method) with quantitative and qualitative techniques. Quantitative technique was applied to convert categorical values to ordinal and nominal numeric values, to establish cause and effect relationship between the variables by using statistical analysis and to apply machine learning methods on the dataset. The quantitative results were then analyzed by using quantitative and qualitative techniques in order to identify the characteristics of the buyers. The data analytics part relied on a publicly available large dataset from the EU containing transport and mobility related data. From the experiments with logistics regression, support vector machine, random forest, gradient boosting classifier and the artificial neural network it was found that ANN is the best model to identify who won’t buy an EV and gradient boosting classifier is the best model to identify who would like to buy and EV. ML based feature importance identification methods (MDI, permutation feature importance) were used to analyze the characteristics of the buyers. The major buyer’s characteristics found in this thesis are environmental concern, knowledge on car sharing, country of residence, education, control traffic, gender, incentive, education and location of residence. Authors have recommended green marketing as the potential enablers towards a faster and larger adoption of electrical vehicles in the market as environmental impact was found as the most significant behavior of the buyer. Finally, for the future researchers, the authors have recommended fine-tuning the algorithms extensively in order to achieve better accuracy and to collect primary data based on the most important features identified in this thesis.