@article{article_1625642, title={Machine Learning Based Real Time Indoor Location Tracking System with WiFi RSSI}, journal={Turkish Journal of Science and Technology}, volume={20}, pages={389–400}, year={2025}, DOI={10.55525/tjst.1625642}, author={Yağcıoğlu, Mert}, keywords={Indoor location prediction, machine learning, Wi-Fi, RSSI, random forest.}, abstract={The increasing demand for indoor localization has led to the development of advanced methods to accurately determine user locations within buildings. This study introduces an indoor localization system that uses WiFi signal strengths (RSSI) sniffed from nearby access points (APs) to estimate a user’s location in real-time. A machine learning approach based on the Random Forest classifier is trained on pre-collected WiFi RSSI data and tested on dynamic inputs to achieve high prediction accuracy. The system seamlessly integrates with a graphical user interface (GUI) that allows users to load live signal data and visualize their locations on a floor plan of the building. To address the challenges of mismatched feature sizes between the training and prediction datasets, feature selection and preprocessing methods are applied to ensure model compatibility. Experimental results demonstrate that the system achieves an overall accuracy of 100% in room-level localization with a latency of 5 seconds. The proposed solution provides a cost-effective, scalable, and user-friendly framework for indoor localization with potential applications in smart homes, health monitoring, and security systems.}, number={2}, publisher={Fırat University}