Analysis, Prediction and Evaluation of COVID-19 Datasets using Machine Learning Algorithms
Journal: International Journal of Emerging Trends in Engineering Research (IJETER) (Vol.8, No. 5)Publication Date: 2019-10-15
Authors : Kolla Bhanu Prakash S. Sagar Imambi Mohammed Ismail T Pavan Kumar; YVR Naga Pawan;
Page : 2199-2204
Keywords : COVID-19; Decision Tree Classifier; Gaussian Naïve Bayesian Classifier; KNN+NCA; Logistic Regression; Machine Learning; Multilinear Regression; SVM; XGBoost Classifier;
Abstract
COVID-19, Corona Virus Diasease-2019, belongs to genus of Coronaviridae. A virus with no vaccine creating unpredictable havocs in the human lives and financial and economic systems in every country throughout the world. It is precariously halted everything in the society mercilessly. An analysis on COVID-19 datasets to understand which age group is mostly effected due to COVID-19. Different prediction models are built using machine learning algorithms and their performances are computed and evaluated. Random Forest Regressor and Random Forest Classifier outperformed the other machine learning models like SVM, KNN+NCA, Decision Tree Classifier, Gaussian Naïve Bayesian Classifier, Multilinear Regression, Logistic Regression and XGBoost Classifier.
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