A Comparative Study of Different Machine Learning Models for COVID-19 Prediction in India

Abstract

Machine Learning (ML) can be deployed very effectively to track the disease, predict the growth of the epidemic and design strategies and policies to manage its spread. Several prediction models for COVID-19 are being used by officials to make relevant control measures. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction .In several technology domains, ML models have been used to define and prioritize adverse threat variables. This study applies an improved mathematical model to analyse and predict the amount of forthcoming COVID-19-affected patients in India. An ML-based improved model has been used to predict the threats of COVID-19 in India. . In this paper, we have performed a comparative study of four machine learning standard models like linear regression (LR), decision tree, multi-layer perception (MLP) and random forest to predict the threatening variables of COVID-19. The prediction models such as Decision tree, MLP and Random forest are evaluated on the basis of loss functions such as R2 score.