An Indoor Positioning System (IPS) issues regression and classification challenges in form of an horizontal localisation and a floor detection. We propose to apply the XGBoost algorithm for both tasks. The algorithm uses vectors of Received Signal Strengths from Wi–Fi access points to map the obtained fingerprints into horizontal coordinates and a current floor number. The original application schema for the algorithm to create IPS was proposed. The algorithm was tested using real data from an academic building. The testing data were split into two datasets. The first data set contains signals from all observed access points. The second dataset consist of signals from the academic network infrastructure. The second dataset was created to eliminate temporary hotspots and to improve a stability of the positioning system. The tested algorithm got similar results as reference methods on the wider set of access points. On the limited set the algorithm obtained the best results.