基于机器学习技术的COVID - 19分析与预测

Supriya Raheja, Shaswat Datta
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摘要

本文介绍了使用机器学习技术对COVID-19数据的分析和预测。目前的工作讨论了不同的机器学习技术,即线性回归,逻辑回归,随机森林和决策树。COVID-19的爆发吸引了所有研究人员对冠状病毒的关注。为了关注COVID-19,本研究试图使用所有机器学习技术分析COVID-19数据。这项工作还引入了进一步预测的决策过程。在预测精度方面对这些技术进行了比较。©2021 Scrivener Publishing LLC。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis and Prediction on COVID‐19 Using Machine Learning Techniques
This paper presents an analysis and prediction of COVID-19 data using machine learning techniques. The present work discusses different machine learning techniques namely linear regression, logistic regression, random forest, and decision tree. The outbreak COVID-19 has attracted the attention of all researchers only on the corona virus. To focus on COVID-19, the present study attempts to analyze COVID-19 data using all machine learning techniques. The work also introduced a decision-making process for further prediction. The techniques are compared with respect to accuracy of prediction. © 2021 Scrivener Publishing LLC.
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