In this project we are going to analyze the Covid-19 data using Python and use the graphing libraries as most of the data in these data sets are way too huge for any one to understand without the visual representation. Any data available to counter the increasing number of deaths at an alarming rate will help humans by interpreting and processing the meaning of the data by creating various parameters and building a graph to understand the vast data and predict what the future may hold in for the humans. We will be implementing it in Python to find the number of confirmed cases using 3 regressions models for predicting number of confirmed cases using support vector machine, Bayesian ridge, and polynomial regression was used to predict the growth range of confirmed new cases, new deaths, and new cured cases which will be compared to see which among the algorithms predict the best with the most accurate or optimal values.
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Mervin-Abraham/Covid-19-Prediction-and-Analysis
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Time Series Prediction on COVID-19 using Support Vector Machine (SVM), Bayesian Ridge, and Polynomial Regression