Open AccessIndian State or City Covid-19 Cases Outbreak Forecast utilizing Machine Learning Models
Author(s) -
Brijesh Patel,
Sheshang Degadwala
Publication year - 2021
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit4217255
Subject(s) - exponential smoothing , support vector machine , anticipation (artificial intelligence) , machine learning , covid-19 , decision tree , lasso (programming language) , variety (cybernetics) , computer science , econometrics , artificial intelligence , predictive modelling , operations research , statistics , mathematics , medicine , disease , pathology , world wide web , infectious disease (medical specialty)
Several episode expectation models for COVID-19 are being used by officials all over the world to make informed decisions and maintain necessary control steps. AI (ML)-based deciding elements have proven their worth in forecasting perioperative outcomes in order to enhance the dynamic of the predicted course of activities. For a long time, ML models have been used in a variety of application areas that needed identifiable evidence and prioritization of unfavorable factors for a danger. To cope with expecting problems, a few anticipation strategies are commonly used. This study demonstrates the ability of ML models to predict the number of future patients affected by COVID-19, which is now regarded as a potential threat to humanity. In particular, four standard evaluating models, such as Linear Regression, Support Vector Machine, LASSO, Exponential Smoothing, and Decision Tree, were used in this investigation to hypothesis the compromising variables of COVID-19. Any one of the models makes three types of predictions, for example, the number of recently Positive cases after and before preliminary vexing, the amount of passing's after and before preliminary lockdown, and the number of recuperations after and before lockdown. The outcomes demonstrate with parameters like R2 Score, Adjust R2 score, MSE, MAE and RMSE on Indian datasets.