Android is one the most used mobile operating system worldwide. Due to its technological impact, its open source code and the possibility of installing applications from third parties without any central control, Android has recently become a malware target. Even if it includes security mechanisms, the last news about malicious activities and Android's vulnerabilities point to the importance of continuing the development of methods and frameworks to improve its security. To prevent malware attacks, researches and developers have proposed different security solutions, applying static analysis, dynamic analysis and artificial intelligence. Indeed, data science has become a promising area in cybersecurity, since analytical models based on data allow for the discovery of insights that can help to predict malicious activities. In this article, we propose to consider Android application layer and network layer features as the basis for machine learning models that can successfully detect malware applications, using open datasets from the research community. Finally, our models show malware detection rates of 99% and 81%.