Title:Forecasting of Covid-19 Cases Using Machine Learning Approach
Volume: 16
Issue: 4
Author(s): Sachin Kumar and Karan Veer*
Affiliation:
- Department of Instrumentation and Control engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar,India
Keywords:
COVID-19, SVM model, polynomial, data analysis, rbf, machine learning.
Abstract:
Aims: The objective of this research is to predict the covid-19 cases in India based on
the machine learning approaches.
Background: Covid-19, a respiratory disease caused by one of the coronavirus family members,
has led to a pandemic situation worldwide in 2020. This virus was detected firstly in Wuhan city of
China in December 2019. This viral disease has taken less than three months to spread across the
globe.
Objective: In this paper, we proposed a regression model based on the Support Vector Machine
(SVM) to forecast the number of deaths, the number of recovered cases, and total confirmed cases
for the next 30 days.
Method: For prediction, the data was collected from Github and the ministry of India's health and
family welfare from March 14, 2020, to December 3, 2020. The model has been designed in
Python 3.6 in Anaconda to forecast the forecasting value of corona trends until September 21,
2020. The proposed methodology is based on the prediction of values using SVM based regression
model with polynomial, linear, rbf kernel. The dataset has been divided into train and test datasets
with 40% and 60% test size and verified with real data. The model performance parameters were
evaluated as a mean square error, mean absolute error, and percentage accuracy.
Results and Conclusion: The results show that the polynomial model has obtained 95% above accuracy
score, linear scored above 90%, and rbf scored above 85% in predicting cumulative death,
conformed cases, and recovered cases.
