Online Job Portal for Job Seekers with PHP
Nandini Gajbhiye, Anushka ukey, Prof. Harshad Kubade
Machine Learning, Weather Forecast, COVID-19, Prediction, Climate Change, Artificial Intelligence, Support Vector Machine, Linear Regression, Graphical Representation, Tkinter GUI
The Machine Learning approach for various diseases has always proven as the best choice as it has a wide range of supervised and unsupervised algorithms. The use of adaptive information technology in areas such as data science and machine learning can help combat this epidemic. In this research work we made use of a widely known and dependent algorithm guide Support Vector Machine (SVM) for both classification and regression problems. This algorithm helps to make decisions quickly using hyper plane technique and it works on extreme boundary facts in order that the same searching records may be categorized further and the records can be placed into new records factors of the appropriate category. In this project, we incorporate predictions for future trends in continuous data. The goal of this study is to predict COVID -19 cases according to the climate and atmospheric trade and for weather forecasting using machine learning, Linear Regression and Variation of Functional Regression algorithms were used. Decision tree models was found to perform poorly in the prediction of COVID-19 cases considering particulate matter and atmospheric parameters as predictors. Model results suggests the possibility of predicting virus infection using machine learning. This will guide policy makers in proactive monitoring and control
Article Details
Unique Paper ID: 160539

Publication Volume & Issue: Volume 10, Issue 1

Page(s): 634 - 638
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