TITLE:
Evaluations of Machine Learning Algorithms Using Simulation Study
AUTHORS:
Nasrin Khatun
KEYWORDS:
Linear Regression, K-Nearest Neighbours, Decision Tree, Random Forest, Support Vector Machine, Hyper-Tuning
JOURNAL NAME:
Open Journal of Statistics,
Vol.15 No.1,
February
18,
2025
ABSTRACT: 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.