Predicting the changes in the next release of software, during the early phases of software development is gaining wide importance. Such a prediction helps in allocating the resources appropriately and thus, reduces costs associated with software maintenance. But predicting the changes using the historical data (data of past releases) of the software is not always possible due to unavailability of data. Thus, it would be highly advantageous if we can train the model using the data from other projects rather than the same project. In this paper, we have performed cross project predictions using 12 datasets obtained from three open source Apache projects, Abdera, POI and Rave. In the study, cross project predictions include both the inter-project (different projects) and inter-version (different versions of same projects) predictions. For cross project predictions, we investigated whether the characteristics of the datasets are valuable for selecting the training set for a known testing set. We concluded that cross project predictions give high accuracy and the distributional characteristics of the datasets are extremely useful for selecting the appropriate training set. Besides this, within cross project predictions, we also examined the accuracy of inter-version predictions.