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View all- Веселяк ВГрицюк Ю(2024)Методи машинного навчання в епідеміологічних дослідженняхScientific Bulletin of UNFU10.36930/4034040834:4(59-67)Online publication date: 23-May-2024
Analysis and Prediction of COVID-19 Data using Machine Learning Models
Pages 296 - 301
Abstract
The COVID-19 pandemic has devastated the world by killing millions of people. The goal of this project is to utilize advanced machine learning models to analyze the global COVID-19 data and predict the future patterns of the COVID-19 pandemic. The COVID-19 data were downloaded from John Hopkins University and Worldometer between January 23, 2020, and April 26, 2021. Advanced machine learning techniques including polynomial regression, support vector regression, and Holt's double exponential smoothing were developed to predict the daily cases for three countries: the USA, India, and Brazil. Based on our experimental results, the USA and Brazil were predicted to decline in the total number of daily cases. However, the daily COVID-19 cases in India were predicted to increases as a new variant of the virus was massively spreading throughout the country. In conclusion, advanced machine learning techniques were efficient and effective to analyze and predict the global COVID-19 data. Our models show good prediction results for all three countries. Brazil and the USA were expected to decline in daily COVID-19 cases as more vaccines become available to their residence. However, India was predicted to increase in daily COVID-19 cases.
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Published In
October 2021
393 pages
ISBN:9781450390439
DOI:10.1145/3497623
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Published: 04 February 2022
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ICCPR '21
ICCPR '21: 2021 10th International Conference on Computing and Pattern Recognition
October 15 - 17, 2021
Shanghai, China
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View all- Веселяк ВГрицюк Ю(2024)Методи машинного навчання в епідеміологічних дослідженняхScientific Bulletin of UNFU10.36930/4034040834:4(59-67)Online publication date: 23-May-2024
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