Semantic place prediction problem is the process of giving semantic names, such as school, home, office, to locations. Different than the localization problem where the coordinates of a place are predicted, the aim is to semantically characterize the location. While the GPS coordinates of a place can be utilized in solving the problem, phone usage patterns of the users in that location can be used as well. In this paper, the aim is to semantically classify locations of smart phone users utilizing the data collected from wireless interfaces and the motion sensors available on the phones with machine learning algorithms. The efficiency of features extracted from raw data is analysed in terms of metrics such as accuracy, using different classification algorithms. The results show that, while random forest and decision tree algorithms achieve 66% accuracy with only temporal features, adding features from device properties and activity features increases the accuracy up to 99%.