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 . 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, URL: https://www.ijtsrd.com/papers/ijtsrd42458.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42458/covid19-prediction-in-india-using-machine-learning/sarfraj-alam
International Journal of Trend in Scientific Research and Development (IJTSRD)
Special Issue: International Conference on Advances in Engineering, Science and Technology – 2021
Organized by: Uttaranchal Institute of Technology, Uttaranchal University, Dehradun
Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD42458 | ICAEST-21 | May 2021
Page 8
Covid-19 Prediction in India using Machine Learning
Sarfraj Alam1, Vipul Kumar1, Sweta Singh1, Sweta Joshi1, Madhu Kirola2
1Student, 2Assistant Professor,
1,2Computer Science and Engineering, Uttaranchal Institute of Technology, Dehradun, Uttarakhand, India
ABSTRACT
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].
KEYWORDS: Data Analysis and Visualization, Logistic Regression, Support Vector
Machine, Random Forest Classifier
How to cite this paper: Sarfraj Alam |
Vipul Kumar | Sweta Singh | Sweta Joshi |
Madhu Kirola "Covid-19 Prediction in
India using Machine Learning" Published
in Inte