Popularity of the Android has made it a main target for security threats. Some other Third-party applica- tions are getting overwhelmed with malware applications. An effective way of detecting and therefore preventing the spread of malware is certainly necessary. Machine learning methods are being actively explored by researchers for malware detection using static and dynamic features extracted from android application package (APK) file. In this paper, we evaluate four classifiers- Decision Tree, K-Nearest Neighbors, Linear SVM and Random Forest for detecting malware and benign android apps from static features.