Covid19 Dashboard & Analysis Using Machine Learning

The COVID-19 pandemic has become a global public health emergency, affecting millions of people worldwide. Various research approaches are being developed using machine learning (ML) techniques and edge computing to identify virus molecules or anticipate the risk analysis of the spread of COVID-19. One of the key challenges in using these techniques is the lack of accurate predictions. To address this challenge, we present a large-scale project for COVID-19 prediction using machine learning. The main objective of our paper is to formulate a simple average aggregated machine learning method to predict the size, number, and length of COVID-19 cases in India. We used datasets from the World Health Organization (WHO), the CoWin website, research portals, and real-time healthcare systems to examine the COVID-19 outbreak in India. We analyzed these datasets using the autoregressive integrated moving average (ARIMA) model and a simple mean aggregated method based on three regression techniques: support vector regression, neural network, and linear regression.