A python based support vector regression model for prediction of COVID19 cases in India

Debanjan Parbat and Monisha Chakraborty

Chaos, Solitons & Fractals, 2020, vol. 138, issue C

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 1st March,2020 to 30th April,2020 (61 Days). The total number of cases as on 30th 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 30th 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.

Keywords: COVID19; India; Support vector regression; Machine learining; Python; RBF; Data analysis (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077920303416
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920303416

DOI: 10.1016/j.chaos.2020.109942

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().