Home > Engineering > Computer Engineering > Special Issue > International Conference on Advances in Engineering, Science and Technology – 2021 > Covid-19 Prediction in India using Machine Learning

Covid-19 Prediction in India using Machine Learning

Call for Papers

Volume-5 | Issue-6

Last date : 26-Oct-2021

Best International Journal
Open Access | Peer Reviewed | Best International Journal | Indexing & IF | 24*7 Support | Dedicated Qualified Team | Rapid Publication Process | International Editor, Reviewer Board | Attractive User Interface with Easy Navigation

Journal Type : Open Access

Processing Charges : 700/- INR Only OR 25 USD (for foreign users)

First Update : Within 7 Days after submittion

Submit Paper Online

For Author

Research Area



Covid-19 Prediction in India using Machine Learning


Sarfraj Alam | Vipul Kumar | Sweta Singh | Sweta Joshi | Madhu Kirola



Sarfraj Alam | Vipul Kumar | Sweta Singh | Sweta Joshi | Madhu Kirola "Covid-19 Prediction in India using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology – 2021, May 2021, pp.8-12, URL: https://www.ijtsrd.com/papers/ijtsrd42458.pdf

Various computational models are used around the world to predict the number of infected individuals and the death rate of the COVID-19 outbreak [3]. Machine learning-based models are important to take proper actions. Due to the ample of uncertainty and crucial data, the aerodynamic models have been challenged regarding higher accuracy for long-term prediction of this disease [1]. By researching the COVID19 problem, it is observed that lockdown and isolation are important techniques for preventing the spread of COVID-19 [2]. In India, public health and the economical condition are impacted by COVID-19, our goal is to visualize the spread of this disease [5]. Machine Learning Algorithms are used in various applications for detecting adverse risk factors. Three ML algorithms we are using that is Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest Classifier (RFC). These machine learning models are predicting the total number of recovered patients as per the date of each state in India [8].

Data Analysis and Visualization, Logistic Regression, Support Vector Machine, Random Forest Classifier


IJTSRD42458
Special Issue | International Conference on Advances in Engineering, Science and Technology – 2021, May 2021
8-12
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

Thomson Reuters
Google Scholer
Academia.edu

ResearchBib
Scribd.com
archive

PdfSR
issuu
Slideshare

WorldJournalAlerts
Twitter
Linkedin