1st cases of COVID-19 were reported in March 2020 in Bangladesh and rapidly increased daily. So many steps were taken by the Bangladesh government to reduce the outbreak of COVID-19, such as masks, gatherings, local movements, international movements, etc. The data was collected from the World Health Organization. In this research, different variables have been used for analysis, for instance, new cases, new deaths, masks, schools, business, gatherings, domestic movement, international travel, new test, positive rate, test per case, new vaccination smoothed, new vaccine, total vaccination, and stringency index. Machine learning algorithms were used to predict and build the model, such as linear regression, K-nearest neighbours, decision trees, random forests, and support vector machines. Accuracy and Mean Square error (MSE) were used to test the model. A hyperparameter was also applied to find the optimum values of parameters. After computing the analysis, the result showed that the linear regression algorithm performs the best overall among the algorithms listed, with the highest testing accuracy and the lowest RMSE before and after hyper-tuning. The highest accuracy and lowest MSE were used for the best model, and for this data set, Linear regression got the highest accuracy, 0.98 and 0.97 and the lowest MSE, 4.79 and 4.04, respectively.
References
[1]
Abdollahi, J., Irani, A.J. and Nouri-Moghaddam, B. (2021) Modeling and Forecasting Spread of COVID-19 Epidemic in Iran until Based on Deep Learning.
[2]
James, G., Witten, D., Hastie, T. and Tibshirani, R. (2021) An Introduction to Statistical Learning with Applications in R. 2nd Edition, Springer.
[3]
Hastie, T., Tibshirani, R., Friedman, J.H. and Friedman, J.H. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Vol. 2, Springer, 1-758.
[4]
James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013) An Introduction to Statis-tical Learning. Vol. 112, Springer, 18.
[5]
Khakharia, A., Shah, V., Jain, S., Shah, J., Tiwari, A., Daphal, P., et al. (2020) Outbreak Prediction of COVID-19 Patients for Dense and Populated Countries Using Machine Learning. SSRNElectronicJournal. https://doi.org/10.2139/ssrn.3655759
[6]
Mohammad Masum, A.K., Khushbu, S.A., Keya, M., Abujar, S. and Hossain, S.A. (2020) COVID-19 in Bangladesh: A Deeper Outlook into the Forecast with Prediction of Upcoming per Day Cases Using Time Series. ProcediaComputerScience, 178, 291-300. https://doi.org/10.1016/j.procs.2020.11.031
[7]
Mohan, S.A.J., Abugabah, A.M.A., Kumar Singh, S., kashif Bashir, A., et al. (2021) An Approach to Forecast Impact of Covid‐19 Using Supervised Machine Learning Model. Software: PracticeandExperience, 52, 824-840. https://doi.org/10.1002/spe.2969
[8]
Ogundokun, R.O. and Awotunde, J.B. (2020) Machine Learning Prediction for COVID-19 Pandemic in India.
[9]
Satu, M.S., Howlader, K.C., Mahmud, M., Kaiser, M.S., Shariful Islam, S.M., Quinn, J.M.W., et al. (2021) Short-Term Prediction of COVID-19 Cases Using Machine Learning Models. AppliedSciences, 11, Article No. 4266. https://doi.org/10.3390/app11094266
[10]
Hastie, T., Tibshirani, R. and Friedman, J. (2008) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Edition, Springer.
[11]
WHO Coronavirus (COVID-19) Dashboard. https://covid19.who.int/
[12]
Xiang, Y., Jia, Y., Chen, L., Guo, L., Shu, B. and Long, E. (2021) COVID-19 Epidemic Prediction and the Impact of Public Health Interventions: A Review of COVID-19 Epidemic Models. InfectiousDiseaseModelling, 6, 324-342. https://doi.org/10.1016/j.idm.2021.01.001
