Title: Machine learning approach for data analysis and predicting coronavirus using COVID-19 India dataset

Authors: Soni Singh; K.R. Ramkumar; Ashima Kukkar

Addresses: Department of Computer Science and Engineering, Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, India ' Department of Computer Science and Engineering, Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, India ' Department of Computer Science and Engineering, Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, India

Abstract: According to the World Health Organisation (WHO), the COVID-19 virus would infect 83,558,756 persons worldwide in 2020, resulting in 646,949 deaths. In this research, we aim to find the link between the time series data and current circumstances to predict the future outbreak and try to figure out which technique is best for modelling for accurate predictions. The performance of different machine learning (ML) models such as sigmoid function, Facebook (FB) prophet model, seasonal auto-regressive integrated moving average with eXogenous factors (SARIMAX) model, support vector machine (SVM) learning model, linear regression (LR) model, and polynomial regression (PR) model are analysed along with their error rate. A comparison is also done to evaluate a best-suited model for prediction based on different categorisation approaches on the WHO authenticated dataset of India. The result states that the PR model shows the best performance with time-series data of COVID-19 whereas the sigmoid model has the consistently smallest prediction error rates for tracking the dynamics of incidents. In contrast, the PR model provided the most realistic prediction to identify a plateau point in the incident's growth curve.

Keywords: COVID-19; pandemics; analysis on India; machine learning; prediction; comparison; support vector machine; SVM.

DOI: 10.1504/IJBIDM.2024.135126

International Journal of Business Intelligence and Data Mining, 2024 Vol.24 No.1, pp.47 - 73

Received: 18 Jan 2022
Accepted: 30 May 2022
Published online: 01 Dec 2023 *

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