Analysis on Prediction of COVID-19 with Machine Learning Algorithms

Abstract

During the pandemic, the most significant reason for the deep concern for COVID-19 is that it spreads from individual to individual through contact or by staying close with the diseased individual. COVID-19 has been understood as an overall pandemic, and a couple of assessments is being performed using various numerical models. Machine Learning (ML) is commonly used in every field. Forecasting systems based on ML have shown their importance in interpreting perioperative effects to accelerate decision-making in the potential course of action. ML models have been used for long to define and prioritize adverse threat variables in several technology domains. To manage forecasting challenges, many prediction approaches have been used extensively. The paper shows the ability of ML models to estimate the amount of forthcoming COVID-19 victims that is now considered a serious threat to civilization. COVID-19 describes the comparative study on ML algorithms for predicting COVID-19, depicts the data to be predicted, and analyses the attributes of COVID-19 cases in different places. It gives an underlying benchmark to exhibit the capability of ML models for future examination.