Covid-19 Future Predictions using Machine Learning Algorithms

Abstract

The ongoing destructive pandemic of Coronavirus Disease (COVID-19) has been the biggest virus that affected more than 190 countries and territories across the world. It seems uncontrollable in many countries and some countries have taken and implemented proper safety measures to eradicate the virus and is under process. We have used machine learning-based prediction tools. As various machine learning algorithms have proved their importance for forecasting and making future decisions. This paper aims to study, analyze and visualize the spreading of the virus in India and the world considering confirmed cases, recovered cases, and fatalities and how in real-world situations we can use machine learning models. It helps to evaluate the spread and pattern of COVID-19 in India by performing Linear Regression, and Support Vector Machine and evaluating parameters using MAE & MSE score, which is the goodness of fit measure. In training a model, the selection of the best learning model is challenging as the data has anomalies because data is not standardized. Therefore, proper study and analysis of the data should be done so that it is easy to understand and act accordingly. Using datasets from Johns Hopkins University the data has been analyzed, obtained from January 22, 2020, till May 17, 2021, for the world. Using this analysis, we can predict the confirmed cases for the following 10 days. The result proves that Linear Regression is much more accurate than the Support Vector Machine.