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Open Access

Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model

Department of Computer Science and Engineering (CSE), Graphic Era Deemed to be University, Dehradun 248002, India
Department of CSE, IMS Engineering College, Ghaziabad 201009, India
Department of CSE, KIET Group of Institutions, Ghaziabad 201206, India
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Abstract

A novel coronavirus (SARS-CoV-2) is an unusual viral pneumonia in patients, first found in late December 2019, latter it declared a pandemic by World Health Organizations because of its fatal effects on public health. In this present, cases of COVID-19 pandemic are exponentially increasing day by day in the whole world. Here, we are detecting the COVID-19 cases, i.e., confirmed, death, and cured cases in India only. We are performing this analysis based on the cases occurring in different states of India in chronological dates. Our dataset contains multiple classes so we are performing multi-class classification. On this dataset, first, we performed data cleansing and feature selection, then performed forecasting of all classes using random forest, linear model, support vector machine, decision tree, and neural network, where random forest model outperformed the others, therefore, the random forest is used for prediction and analysis of all the results. The K-fold cross-validation is performed to measure the consistency of the model.

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Big Data Mining and Analytics
Pages 116-123
Cite this article:
Gupta VK, Gupta A, Kumar D, et al. Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model. Big Data Mining and Analytics, 2021, 4(2): 116-123. https://doi.org/10.26599/BDMA.2020.9020016

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Received: 17 June 2020
Revised: 10 August 2020
Accepted: 21 August 2020
Published: 01 February 2021
© The author(s) 2021

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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