Data Science and Machine Learning Integrated Implementation Patterns for Cavernous Knowledge Discovery from COVID-19 Data

Pandemic is a well-known term for the year 2020. It's essentially a disease that spreads across a region or the entire planet. The entire planet appears powerless, and a jerk triggered by a virus outbreak has halted. On 11 March 2020, WHO announced Corona Virus disease 2019 (COVID-19) a pandemic. The outbreak or epidemic of the virus differs widelyfrom one nation to another. Society is the secret to solving the pandemic. Fever is one of the common, easily detectable symptoms of COVID-19. The COVID 19 in India is one of the most widespread pandemics caused by extreme acute corona viral syndrome2 (SARS-CoV-2) in coronvirus disease 2019 (COVID-19). On 30 January 2020, the original case of COVID-19 in India, arising in China, was registered. India currently has the highest number of confirmed cases in Asia and the second largest number after the United States of America in the world, with a combined number of confirmed cases exceeding the thresholds of 100,000 on 19 May and 1.000,000 confirmed cases on 17 July 2020. The largest one-day increase in COVID-19 cases of 78,761 cases was observed in Indian countries on 29 August 2020, surpassing the previous record in US cases of 77,368 on 17 July 2020. Nowadays, data science tasks are not limited to traditional data analysis with limited attributes and records. In current scenarios, the real-time datasets are huge with enormous attributes and suchdatasets are very complex to evaluate using classical data analysis tools. For example, the datasets of the medical domain integrate several attributes in which the information of symptoms, diagnosis, travel history, health parameters, and many others are evaluated. To deal with such types of datasets assorted database query tools and programming languages are used. © Published under licence by IOP Publishing Ltd.