Throughput Analysis with Effect of Dimensionality Reduction on 5G Dataset using Machine Learning and Deep Learning Models

5G or ZTE the latest improvement to the existing 4G communication standard. These technologies could be evaluated by various metrics called performance indicators among which throughput plays a major role. Throughput is the measure of the rate of data transferred to the device. Higher the throughput, better is the performance of the network. This work models and analyses the throughput obtained with the variations observed on the identified parameters on which it depends on. Here the problem is analysed as a regression problem and hence regressor models are applied. Multiple models ranging from statistical to probabilistic and machine learning to deep recurrent networks are analysed with a 10 fold cross validation. Also, the effect of dimensionality reduction is applied to the dataset and the performance is observed. It is noticed from the work that the top performing models are consistent in performance measured using the regression metrics.