The project collects WiFi signal strength data from access points and trains a RandomForest classifier to predict room-level location. Users train the model by sampling WiFi signatures in different locations (e.g., bedroom, kitchen), then query the trained model for real-time location prediction. The cross-platform tool works on Linux, macOS, and Windows, providing both CLI and Python library interfaces.