The paper presents a novel approach of predicting the battery energy consumption of electric city buses (e-buses) using a trip-based data-driven regression model. The model is parametrized based on the data collected by running a physical experimentally-validated e-bus simulation model. The main advantage of the proposed approach is that it relies on typically available average, trip-related data such as distance travelled, mean velocity, mean number of passengers, and mean and standard deviation of road slope, as opposed to the physical model that requires high sampling rate driving cycle data. Additionally, the data-driven model is executed significantly faster than the physical model, thus making it suitable for large-scale city bus electrification planning or on-line energy consumption prediction applications. The data-driven model development starts with applying feature selection techniques to identify the most relevant set of model inputs. Next, machine learning methods are employed to determine the most accurate and still simple model with a good interpretability. The validation results demonstrate that the finally selected four-input quadratic model achieves a high level of precision and generalization (i.e., accuracy with respect to unseen data). Finally, the model performance is compared with that of the original physical model to confirm its accuracy and effectiveness.