Analysis and Prediction of COVID-19 using Regression Models and Time Series Forecasting

In this paper, we are predicting and forecasting the COVID-19 outbreak in India based on the machine learning approach, where we aim to determine the optimal regression model for an in-depth analysis of the novel coronavirus in India. We are implementing the two regression models namely linear and p...

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Published in2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) pp. 989 - 995
Main Authors Shaikh, Saud, Gala, Jaini, Jain, Aishita, Advani, Sunny, Jaidhara, Sagar, Roja Edinburgh, Mani
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.01.2021
Subjects
Analytical models
COVID-19
Forecasting
India
linear regression
machine learning
polynomial regression
Predictive models
regression models
tableau
Time measurement
Time series analysis
time series forecasting
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DOI10.1109/Confluence51648.2021.9377137

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Summary:In this paper, we are predicting and forecasting the COVID-19 outbreak in India based on the machine learning approach, where we aim to determine the optimal regression model for an in-depth analysis of the novel coronavirus in India. We are implementing the two regression models namely linear and polynomial and evaluating the two using the R squared score and error values. The COVID-19 dataset for India is being used to serve the research of this paper. The model is predicting the number of confirmed, recovered, and death cases based on the data available from March 12 to October 31,2020. For forecasting the future trend of these cases, we are utilizing the time series forecasting approach of tableau. Furthermore, the time series forecasting method is being employed to forecast the total count of confirmed cases in the future.
DOI:10.1109/Confluence51648.2021.9377137