Visualisation and prediction of Covid-19 data using random forest regression


  • D. Sumathi School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh 522237, India
  • T. Poongodi School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh 203201, India
  • P. Suresh School of Mechanical Engineering, Galgotias University, Greater Noida, Uttar Pradesh 203201, India
  • S. Karthikeyan KPR Institute of Engineering and Technology, Arasur, Tamil Nadu 641407, India
  • N. Sree Chand1 School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh 522237, India



artificial intelligence, linear regression, machine learning, random forest, visualization, XGBoost


The outbreak of COVID-19 has spread among several parts of the world. The data pool increases tremendously, which needs excellent attention by researchers of various domains to analyze and determine the measures to handle it. Hence, researchers worldwide are looking into Artificial Intelligence (AI) to resolve the challenges due to this COVID-19. It could be stated that AI can examine huge data mounds so that several new findings can be determined. AI could be deployed in various fields, such as the pharmaceutical industry, the analysis and development of vaccines and antibodies, and drug designing. Due to the impressive progress that AI has made in the latest few years, still, it proves to be the essential quality of the technology and evidence of humans' creativity towards the contribution of developing tools and products which could be statistically and computationally complex. It is observed that AI technology aids in tracing the outburst, patient diagnosis and fastening the procedure of finding a treatment. This work provides an overview of COVID-19, along with the convergence of technologies that could be applied in various sections to handle this pandemic. An extensive exploration of multiple techniques and models has been implemented for the prediction of COVID-19 has been done. Additionally, numerous models have been deployed for predictions of covid states. It has been inferred that XGBoost showed considerable progress in the prophecy.


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How to Cite

D. Sumathi, T. Poongodi, P. Suresh, S. Karthikeyan, & N. Sree Chand1. (2023). Visualisation and prediction of Covid-19 data using random forest regression. Journal of Current Science and Technology, 13(2), 221–236.



Research Article