Abstract
The proposed method examines newly developed predicting models in-depth and forecasts the numerous cases of confirmed, recovered, and fatality caused through coronavirus in India. To improve the COVID-19 impact analysis, machine learning techniques such as decision tree, multiple linear model (MLR), random forest, Support Vector Machine algorithm (SVM), and Artificial neural network model were utilized for enhancing precision. With an 0.9992 R2 score, the projected number of cases matches the actual numbers quite well. A follow-up on the vaccination and its effects is required for research and the development of new ways to protect us from the disease. Also, using XGBoost, the accuracy has been improvised. Importing the matplotlib package is used to visualize the COVID-19 data. Finally, before and after the vaccine, a performance analysis was implemented.
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Abirami, T., Annuncia Marena, Y., Jayadharshini, P., Madhuvanthi, T. (2023). Analyzing the Impact of COVID-19 and Vaccination Using Machine Learning and ANN. In: Dey, N. (eds) Data-Driven Approach for Bio-medical and Healthcare. Data-Intensive Research. Springer, Singapore. https://doi.org/10.1007/978-981-19-5184-8_9
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DOI: https://doi.org/10.1007/978-981-19-5184-8_9
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