It is a Project that using Machine Learning techniques focuses on the study and prediction of the evolution of the coronavirus pandemic.
John Hopkins University data was used as the data set.
For mathematical operations: pandas and numpy For graphs: matplotlib For ML techniques: sklearn
The data we are called to process is for confirmed cases, deaths and the number of patients who have recovered.
A csv file with the latest data from around the world is uploaded daily to Github.
We were therefore asked to process all of the above data in order to predict the future number of confirmed cases for a 7-day window.
For the prediction we used the following mathematical regression models:
-> SVM - Support Vector Machine
-> Bayesian Regression
-> Polynomial Regression
Our goal was to store our data one-dimensionally so that it could be properly captured in any graph or diagram we wanted to create.