More and more mobile devices transmit Wi-Fi signals which can be detected by access points. Consequently the number of visitors near access points can be measured. Often we are interested in the number of visitors only for a certain region. Therefore, in the context of master's thesis, methods and products for indoor localization were studied. We transformed the localization problem into classification of Wi-Fi signals problem. Based on existing methods (nearest base station, trilateration and RADAR) we developed three new methods. In order to evaluate these methods the sytem for capturing Wi-Fi signals was set up at the Faculty of Computer and Information Science in Ljubljana. The data was being collected for two months. Based on machine learning algorithms we developed a new method, which correctly predicts the region in 85,1 % cases. If regions are separated by walls, the classification accuracy is more than 93 %.
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