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State-wise prevalence of COVID 19 in India by using machine learning approaches
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| Author:
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, B.SREE KEERTHI MEGHANA, V. KAKULAPATI
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| Abstract:
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Coronavirus is one of the world's most critical issues, till date. Comprehension of causative variables such as mellitus, heart-related issues, asthma, blood pressure, etc., including the intrinsic transmission mechanisms of the disease, COVID 19 and its eradication are important for neurological investigation. Hence, the advance of appropriate modeling approaches and methods applied to current corona information on the pervasiveness of the pandemic and other serious illness aspects, is taking consideration. The prevalence of COVID 19 in India has reached epidemic proportions, and this disease is becoming a significantly increasing case in India. In this work, polynomial regression analysis methods employ to to forecast the number of COVID 19 corona patients. In this, we described a decision tree, polynomial and random forest classification of disease in COVID 19 incidences modelling and forecasting in India and a predicted prevalence of high level of confidence.
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| Keyword:
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polynomial, decision, prevalence, model, COVID 19, classification
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2020.SP2.295
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| Download:
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