About: A Python based Support Vector Regression Model for prediction of Covid19 cases in India

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A Python based Support Vector Regression Model for prediction of Covid19 cases in India
Creator	Chakraborty, Monisha Parbat, Debanjan
Source	Elsevier; Medline; PMC
abstract	The proposed work utilizes Support Vector Regression model to predict the number of total number of deaths, recovered cases, cumulative number of confirmed cases and number of daily cases. The data is collected for the time period of 1(st) March,2020 to 30(th) April,2020 (61 Days). The total number of cases as on 30(th) April is found to be 35043 confirmed cases with 1147 total deaths and 8889 recovered patients. The model has been developed in Python 3.6.3 to obtain the predicted values of aforementioned cases till 30(th) June,2020. The proposed methodology is based on prediction of values using support vector regression model with Radial Basis Function as the kernel and 10% confidence interval for the curve fitting. The data has been split into train and test set with test size 40% and training 60%. The model performance parameters are calculated as mean square error, root mean square error, regression score and percentage accuracy. The model has above 97% accuracy in predicting deaths, recovered, cumulative number of confirmed cases and 87% accuracy in predicting daily new cases. The results suggest a gaussian decrease of the number of cases and could take another 3 to 4 months to come down the minimum level with no new cases being reported. The method is very efficient and has higher accuracy than linear or polynomial regression.
has issue date	2020-05-31(xsd:dateTime)
bibo:doi	10.1016/j.chaos.2020.109942
bibo:pmid	32834576
has license	no-cc
sha1sum (hex)	a925f71128aa0b04daf89a82090157de1c9af7df
schema:url	https://doi.org/10.1016/j.chaos.2020.109942
resource representing a document's title	A Python based Support Vector Regression Model for prediction of Covid19 cases in India
has PubMed Central identifier	PMC7261465
has PubMed identifier	32834576
schema:publication	Chaos Solitons Fractals
resource representing a document's body	covid:a925f71128aa0b04daf89a82090157de1c9af7df#body_text
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