Semantic place prediction problem is the process of giving semantic names to locations. While the localization problem is about predicting the exact position, i.e. the coordinates, of a place, the aim here is to semantically characterize the location, such as home, school, restaurant. In order to solve the problem, phone usage patterns of crowds and the performed activities at different places can be utilized. In this study, we aim to semantically classify places visited by smart phone users utilizing the data collected from sensors and wireless interfaces available on the phones as well as phone usage patterns, such as battery level, and time-related information, with machine learning algorithms. For this purpose, we collected data from 15 participants at Galatasaray University for a duration of 1 month, in April 2016, and two of the users continued to collect 1 more month of data, which makes it 17 participants in total. We extract various set of features from the collected data and analyse the efficiency of features with different classification algorithms such as, decision tree, random forest, k-nearest neighbour, naive Bayes and multi-layer perceptron. We observe that, by fusing features extracted from different sources of data, better success rates are achieved. Moreover, we explore the relationship between places and activities, which was not explored in previous studies, and show that activities are important source of information for characterizing the places. Additionally, we observe that, while a generalized classifier performs reasonably well, using person-specific data and classification can help to improve the success rate.