id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-327363-z30zoogs	Neve, D.	On Modeling of COVID-19 for the Indian Subcontinent using Polynomial and Supervised Learning Regression	2020-10-16		.txt	text/plain	4311	302	62	In the current analysis, COVID-19 modeling is done for the Indian subcontinent based on the data collected for the total cases confirmed, daily recovered, daily deaths, total recovered and total deaths. Then different regression models like Polynomial Regression, Forest Regression, Support Vector Regression, Naive Bayes, were used to predict the situation till September 7, 2020 and an optimal model was proposed. Regression models are statistical sets of processes which are used to estimate or predict the target or dependent variable based on dependent variables. In Figure 10 , we have applied Random Forest Regression between total confirmed cases and number of days. We apply machine learning models to data set for predicting future values. Naive Bayes regression failed due to less accuracy and Random Forest ended up overfitting the data set. Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model Regression Model based COVID-19 outbreak predictions in India	./cache/cord-327363-z30zoogs.txt	./txt/cord-327363-z30zoogs.txt