Open AccessPredicting and Analysing the Behaviour of COVID-19
Author(s) -
Gaurav Singh,
Shivam Rai,
Himanshu Shekhar Mishra,
Manoj Kumar
Publication year - 2021
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit217213
Subject(s) - mean squared error , mean absolute error , covid-19 , regression , support vector machine , machine learning , regression analysis , polynomial regression , computer science , statistics , artificial intelligence , mathematics , medicine , disease , pathology , infectious disease (medical specialty)
The prime objective of this work is to predicting and analysing the Covid-19 pandemic around the world using Machine Learning algorithms like Polynomial Regression, Support Vector Machine and Ridge Regression. And furthermore, assess and compare the performance of the varied regression algorithms as far as parameters like R squared, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. In this work, we have used the dataset available on Covid-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at John Hopkins University. We have analyzed the covid19 cases from 22/1/2020 till now. We applied a supervised machine learning prediction model to forecast the possible confirmed cases for the next ten days.