Stress detection is important for ensuring overall mental well-being of an individual. Literature suggests several approaches for prediction or classification of stress. However, the performance of these approaches varies a lot across subjects and tasks. Moreover, perception of stress is highly subjective and hence it is difficult to create a generic model/devices for prediction of stress. In this study, we have proposed an approach for creating a generic stress prediction model by combining the knowledge and variety from multiple public datasets containing galvanic skin response (GSR) data recorded during different context and activities. Most significant features are selected from these recorded signals and a voting based approach was finally adopted to develop a model for predicting mental stress. Proposed model has been validated using test data as well as a set of completely unseen data collected in our lab. We achieved an average classification accuracy of 89% (F-score 0.87) for test data and similar performance for completely unseen data as well. Results show that the proposed model outperforms the training models created using individual datasets. In addition, our model is created using skin response data recorded using off-the-shelf devices. Thus, our proposed model with selected feature set can be used for monitoring stress in real life scenarios and to create mass-market stress prediction products.