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In December 2019, a virus that has just recently been identified produced the infectious illness corona virus disease (COVID-19), which first appeared in Wuhan, China. Most COVID-19 virus-infected individuals will have a mild to severe respiratory infection and recover without the need for special care. Serious illnesses are more likely to strike older persons and those with underlying medical conditions including cancer, diabetes, cardiovascular disease, or chronic respiratory diseases. The early reports came from Wuhan (China), where the dead toll has risen to several lakhs and the recovery rate is comparably quite low. Through social interaction and intimate touch with others, this spreads. various regions of the world have various numbers of affected persons. The only vaccination that encourages "social distancing" is now being administered in our specific nation, India, during the lock down time. The problem caused by the spreading corona is a large economic loss along with the loss of innocent life. In this article, using machine learning techniques visualise the publically accessible dataset to map, discriminate, and separate the data in order to segregate the locations that are most susceptible, and do a basic regression to identify and estimate the likelihood that the counts from these locations will increase.
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Cite Article:
"Covid 19 Outbreak Analysis Using Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.2178 - 2180, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305211.pdf
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000205193
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator