FUTURE FORECATING USING SUPERVISED MACHINE LEARNING MODELS COVID 19

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FUTURE FORECATING USING SUPERVISED MACHINE LEARNING MODELS COVID 19

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FUTURE FORECATING USING SUPERVISED MACHINE LEARNING MODELS COVID 19

A.BHARATHI1, S.LAKSHMANRAM2 ,T.SUBASH3, S.SURESHKUMAR4, P.PRASAD5,

1Associate Processor in Information Technology, Nandha Engineering College, Perundurai -638052, Erode District, TamilNadu, India.

2,3,4,5Students of Information Technology, Nandha Engineering College, Perundurai -638052, Erode District, TamilNadu, India.

ABSTRACT :The global proliferation of COVID-19 has placed mankind in danger. Because of the disease's high infectivity and transmissibility, the resources of some of the world's most powerful economies are being taxed. The capacity of machine learning algorithms can anticipate the number of forthcoming patients impacted by COVID-19, which is now regarded as a possible threat to humanity. In this work, four conventional forecasting models, least absolute shrinkage and selection operator (LR), were utilized to anticipate the COVID-19 hazardous elements. Each model makes three sorts of predictions: the number of newly infected cases, the number of fatalities, and the number of recoveries. However, in the cannot predict an exact outcome for the patients.To address the problem, the suggested technique employing LR predicts the number of COVID-19 cases in the following 30 days as well as the influence of preventative measures such as social isolation and lockout on the spread of COVID-19.

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