Open AccessAnalytical study on COVID-19 to predict future infected cases ratio in India using Machine Leaning
Author(s) -
Hiral R. Patel,
Hiral Patel,
Ajay Patel,
Satyen M. Parikh
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1022/1/012022
Subject(s) - python (programming language) , covid-19 , visualization , computer science , data science , analytics , data visualization , open source , visual analytics , data analysis , machine learning , artificial intelligence , data mining , infectious disease (medical specialty) , software , medicine , pathology , virology , outbreak , programming language , operating system , disease
COVID-19 is real a worldwide terrific problem. This paper focuses on the different aspects of data analytics and visualization by using various datasets supported by authorized sources. It also discusses the practical aspects using open source tools and python library support. Here chapter focuses on comparative analysis also. It also visualize analytical aspects by different aspects such as country wise, date wise and so on. In this paper, the COVID infected cases and its reaction on people will be discussed. This case study will predict the COVID-19 infected cases and death ratio with symptoms in future. This paper focus on data visualization, data analytics and comparative study based on practical aspects. Machine Learning plays a vital role to predict the cases by providing learning instances.