id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-303523-m16vlv1q	Ogundokun, R. O.	MACHINE LEARNING PREDICTION FOR COVID 19 PANDEMIC IN INDIA	2020-05-26		.txt	text/plain	3889	250	50	Objective: The objective of the paper is to formulate a simple average aggregated machine learning method to predict the number, size, and length of COVID-19 cases extent and wind-up period crosswise India. In this study, the authors formulated a simple mean aggregated method by combining 3 popular regression models and predicted the sum of COVID-19 in India. As a substitute for epidemiologic spread procedure, the study employed 3 aggregated methods SVR, NN, and LR to predict the instantaneous movement of the conveyance dynamics and generate the real-time predictions of COVID-19 disease transversely the metropolises of India. In this study, the formulation of aggregated methods illustrates a substantial enhancement in the prediction of the COVID-19 disease in India. The study delivered a substantial enhancement in prediction precisions for COVID-19 disease in India when the postulated aggregated system was employed. The study postulated a simple-mean aggregated method for the prediction of COVID-19 disease in India.	./cache/cord-303523-m16vlv1q.txt	./txt/cord-303523-m16vlv1q.txt