Comparative analysis Machine learning(ML) based path loss dataset for university campus environment

Path loss is an important key factor affecting performance and efficiency in wireless communication system. Here primary measured data was collected from Covenant University, Ota, Ogun State, Nigeria & supplementary materials article found at the following address (https://doi.org/10.1016/j.dib.2018.02.026). In order to generalize for every university environment, four key features (Elevation, Altitude, Clutter height, Distance) were used to take as input, and a total of 3616 measured samples used in our cases to build the ML-based model and predict this dataset. Hence 28 different types of ML-based machine learning have been implemented to generate this predicted ML-based data. Each type is under categorized (ML) techniques such as Linear Regression, Tree, SVM (Support vector machine), Efficient Linear method, Ensemble method, GPR (Gaussian Process Regression), Neural Network and Kernel method.