International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
COVID-19 CASES PREDICTION USING MACHINE LEARNING SOMULA VENKATA MADHAVA REDDY1, KOMPALLI VENKATA RAMANA2,MATTA TARUN KUMAR3, MANDAPAKA RAMA KRISHNA4 1,2,3,4 UG Scholar, Dept. Of CSE, NRI Institute of Technology, A.P-521212.
---------------------------------------------------------------------***--------------------------------------------------------------------propagation of infectious illnesses. While MLR and BRR are ABSTRACT - The COVID-19 prevalent has caused
multivariate linear regression models, SVM is a supervised learning method that is frequently employed for regression analysis and classification.
catastrophic consequences for the economy and public health throughout the world. Informed decisions concerning effective allocations of resources can be made by politicians and public health experts with the aid of precise forecasts of the virus's progress. [1] With the use of Support Vector Machines (SVM), Multiple Linear Regression (MLR), and Bayesian Ridge Regression (BRR) algorithms, we present a method in this study to forecast the number of COVID-19 instances [1]. A more precise and thorough forecast of COVID-19 instances is provided by our suggested methodology, which takes into account a variety of socioeconomic and environmental variables that may have an impact on the propagation of the virus. Using choosing features approaches, we gathered information on the number of COVID-19 cases in various nations and areas and determined the most essential variables to anticipate COVID-19 cases. Our model was trained using the SVM, MLR, and BRR algorithms, and its performance was assessed using a wide range of metrics, such mean absolute error (MAE), coefficient of determination (R^2), and root mean squared error (RMSE). Our findings show that the quantity of COVID-19 cases may be forecasted with an elevated level of accuracy using the suggested approach. Our research offers perceptions into the variables that have the biggest influence on the total amount of COVID-19 cases, that may help health professionals and politicians in making wise choices and properly allocating resources.
The advantages of each of these algorithms are employed in our approach to generate an accurate and accurate forecast of COVID-19 situations. [2] Information on the overall number of COVID-19 cases across various nations and regions, together with several socioeconomic and environmental variables that may influence the virus' spread, were used to create our model [2]. Our findings demonstrate that our method predicts the number of COVID-19 cases with high accuracy, low root mean squared errors (RMSE), and elevated coefficient of determination (R^2) values.
2.TECHNOLOGIES USED: Python: Python has fundamentally altered how software is developed and how data is evaluated. Both beginner and experienced programmers favour it because of its simplicity, adaptability, and readability. Python's ability to smoothly connect to additional programming languages like C++ and Java is one of its unique characteristics, making it an effective tool for creating complicated systems. In order to further expand the capability of Python, a large number of independent libraries and tools are available. Python offers an assortment of uses, from web development to computational science, making it a useful tool in numerous sectors. Python will stay an important player in the programming and technology industries for years to come thanks to its rising popularity and robust developer communities.
KEYWORDS: covid-19, support vector machine, multiple linear regression, Bayesian ridge regression, Mean absolute error.
1.INTRODUCTION: The COVID-19 epidemic has significantly impacted both general health as well as the global economy while disturbing people's lives all across the world. To efficiently deploy resources and make educated decisions, policymakers must be able to forecast the anticipated number of COVID-19 cases. With the use of Support Vector Machines (SVM), Multiple Linear Regression (MLR), and Bayesian Ridge Regression (BRR) algorithms, we present a method in this study for predicting the number of COVID-19 instances.
LIBRARY FUNCTIONS IN PYTHON: • Modules are nothing more than files containing Python code. • A package is a directory for modules and sub packages. [fig.1]
SVM, MLR, and BRR are just a few examples of machine learning models that are frequently used to forecast the
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There is no unique context in the Python library.
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