  In this study, we present a method to predict the Received signal strength
indication (RSSI) in an area of the base station. Traditional attenuated wave
propagation models are often time consuming as well as computationally complex,
depending on the unique factors of the medium. This study focuses on providing
a solution to predict signal quality using coordinate values of many points in
the considering area. We apply machine learning models such as linear
regression, Support Vector Machine (SVM) or Decision tree model, to directly
predict the RSSI of many points in the range of a base station without
computing the complex parameters of the attenuated propagation model. The
effectiveness of RSSI prediction was evaluated by mean square error (MSE) and
mean absolute error (MAE). The stage of training and testing machine learning
models in the research uses data that are the actual measurement results during
the research process.
