  Mobile app stores are the key distributors of mobile applications. They
regularly apply vetting processes to the deployed apps. Yet, some of these
vetting processes might be inadequate or applied late. The late removal of
applications might have unpleasant consequences for developers and users alike.
Thus, in this work we propose a data-driven predictive approach that determines
whether the respective app will be removed or accepted. It also indicates the
features' relevance that help the stakeholders in the interpretation. In turn,
our approach can support developers in improving their apps and users in
downloading the ones that are less likely to be removed. We focus on the Google
App store and we compile a new data set of 870,515 applications, 56% of which
have actually been removed from the market. Our proposed approach is a
bootstrap aggregating of multiple XGBoost machine learning classifiers. We
propose two models: user-centered using 47 features, and developer-centered
using 37 features, the ones only available before deployment. We achieve the
following Areas Under the ROC Curves (AUCs) on the test set: user-centered =
0.792, developer-centered = 0.762.
