Human mobility prediction has received considerable attention because it helps addressing many practical problems in mobile networks. Most existing techniques focus on regular mobility prediction by studying the periodic mobility pattern of users. However, they fail to detect users' irregular mobility patterns, like attending a sporadic event. We address this problem by proposing techniques to predict event attendance based on the following basic idea: if a user is interested in events related to a topic, he may also attend future events related to this topic. In our solution, to learn how users are likely to attend the future events, three sets of features are identified by analyzing users' past activities, including semantic, temporal, and spatial features. Then, the supervised learning models are trained to predict event attendance based on the extracted features. To evaluate the performance of the proposed techniques, we collect a dataset based on Meet up that contains semantic descriptions of all events organized over a period of two years. Evaluation results show that the supervised classifiers built by all features outperform those built by individual features, and semantic features are more effective than temporal features and spatial features for predicting event attendance.