Predicting and Analysing the Behaviour of COVID-19
DOI:
https://doi.org/10.32628/CSEIT217213Keywords:
Covid-19 ,Prediction, Polynomial Regression, Ridge Regression, Support Vector Machine.Abstract
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.
References
- World Health Organization- https://www.who.int/
- World Health Organization- https://www.euro.who.int/en/health-topics/health-emergencies/coronavirus-covid-19/novel-coronavirus-2019-ncov
- COVID-19 Data Repository by the Center for Systems Science and engineering (CSSE) at John Hopkins University: https://github.com/CSSEGISandData/COVID-19
- Akib Mohi Ud Din Khanday,”Machine learning based approaches for detecting COVID-19 using clinical text data” 2020, Springer
- Furqann Rustam,“COVID-19 Future Forecasting Using Supervised Machine Learning Models”, 2020,IEEE
- Hamed Jelodar,”Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach”, 2020, IEEE
- Mohammad (Behdad) Jamshidi,” Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment” 2020, IEEE
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