id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-286419-jyvj3mo2	Rahaman Khan, Hasinur	Countries are Clustered but Number of Tests is not Vital to Predict Global COVID-19 Confirmed Cases: A Machine Learning Approach	2020-04-29		.txt	text/plain	2934	169	56	title: Countries are Clustered but Number of Tests is not Vital to Predict Global COVID-19 Confirmed Cases: A Machine Learning Approach COVID-19 disease is a global pandemic and it appears as pandemic for each and every nation and territory in the earth.This paper focusses on analysing the global COVID-19 data by popular machine learning techniques to know which covariates are importantly associated with the cumulative number of confirmed cases, whether the countries are clustered with respect to the covariates considered, whether the variation in the covariates are explained by any latent factor. Regression tree, cluster analysis and principal component analysis are implemented to global COVID-19 data of 133 countries obtained from the Worldometer website as reported as on April 17, 2020. In this paper, we demonstrated how to implement the basic machine learning techniquesprincipal component, cluster analysis and regression tree to analyse global COVID-19 data that was extracted from the Worldometer website (Max Roser & Ortiz-Ospina, 2020) and reported as of April 17, 2020.	./cache/cord-286419-jyvj3mo2.txt	./txt/cord-286419-jyvj3mo2.txt