This is the notebook which preprocesses the data files released by John Hopkins University on GitHub and analyzes the data by drawing various plots such as confirmed cases vs date, top countries with the most cases, and more. It alsp predicts the confirmed cases of Covid-19 all over the world using machine learning algorithms such as SVM, polynomial regression, and linear regression.
- pandas
- numpy
- matplotlib
- seaborn
- sklearn
The dataset used in this project is the Covid-19 data released by John Hopkins University on GitHub. The dataset contains daily time series data of Covid-19 cases and deaths in various countries from January 2020 to present.
The dataset was preprocessed to include only the necessary columns such as confirmed cases, deaths, and date. The data was also cleaned and missing values were handled appropriately.
Three machine learning models were used in this project to predict the confirmed cases of Covid-19: SVM, polynomial regression, and linear regression. The models were trained using the preprocessed data and their accuracy was evaluated.
The data was analyzed by drawing various plots such as confirmed cases vs date, top countries with the most cases, and more. The plots were created using matplotlib and seaborn libraries.