Manuscript Number : CSEIT228134
Analysis and Prediction of COVID-19 Pandemic in India
Authors(2) :-K. M. Ravikumar, D. Chandrasekhar The real-time data has become a dominant aspect for understanding past, present, and future situations. Machine Learning (ML) is one platform that uses a variety of algorithms to provide the correlation between the given data, visualize the current scenario, and predict the future forecast, which is the most crucial part. The entire world is currently experiencing a devastating situation due to the outbreak of a novel coronavirus known as COVID19. The COVID19 at present has proved that it is a potential threat to human life. To contribute to controlling the spread and rising number of active cases in India, this study demonstrates the future forecasting of the total number of active cases in India in the upcoming days. Future Forecasting is performed using the ARIMA model (autoregressive Integrated moving average by combining Facebook) A prophet who gives us the highest precision. Real-time data Collection is done from different sources depending on the data preprocessing and data wrangling is done. The record is that it is divided into a training set and a test set. Finally, the model was trained and checked for accuracy. After the test with training, the model is ready to predict future predictions. The model also records predicted and actual values help him achieve higher accuracy in the future.
K. M. Ravikumar COVID19, ARIMA Model, Machine Learning, Time Series Analysis, Forecasting, R-Square score, root mean square error, mean squared error
Publication Details Published in : Volume 8 | Issue 1 | January-February 2022 Article Preview
PG Scholar, Department of CSE, Sarada Institute of Science Technology and Management, Ampolu road, Srikakulam, Andhra Pradesh, India
D. Chandrasekhar
Professor, Department of CSE, Sarada Institute of Science Technology and Management, Ampolu road, Srikakulam, Andhra Pradesh, India
Date of Publication : 2022-02-28
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 229-235
Manuscript Number : CSEIT228134
Publisher : Technoscience Academy
Journal URL : https://res.ijsrcseit.com/CSEIT228134
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