This project aims to predict future trends of COVID-19 cases using data from Worldometer. By building and analyzing plots/graphs, we aim to understand potential future scenarios if the reporting and testing of cases continue.
- Data Extraction: Extracts COVID-19 data from the Worldometer website.
- Data Analysis: Analyzes the extracted data to identify trends.
- Prediction Model: Implements a simple classification model to predict future COVID-19 case trends.
- Visualization: Builds plots/graphs to visualize current and predicted trends.
- Predict the future trend of COVID-19 cases.
- Visualize artificial trends and patterns in the data.
- Clone the repository:
git clone https://github.com/eagleofthesteppes/MedXAI.git
- Install dependencies:
pip install -r requirements.txt
- Change to the data directory:
cd data/ - Run the data extraction script:
python sort_and_divide_data.ipynb
- Update the repository:
git pull origin main
- Data Extraction: The script
sort_and_divide_data.pyextracts data from the OWID Dataset and saves the CSV files in a subfolder. - Data Analysis: The script
analyze_data.pyreads the CSV file, performs analysis, and generates plots/graphs.
We welcome contributions! Please fork the repository and submit a pull request.
This project is licensed under the MIT License.
For any questions or suggestions, please open an issue or contact us at [nkumar7@ualberta.ca], [adityadi@ualberta.ca], [Ryan.Chattopadhyay1@gmail.com].
- Nitin - nkumar7@ualberta.ca
- Aditya - adityadi@ualberta.ca
- Ryan - Ryan.Chattopadhyay1@gmail.com
- Hazel - hcho3@ualberta.ca
- Tatsat - tatsat@ualberta.ca
- Tri - tridhatriv@gmail.com
- Mayank - mkhandel@ualberta.ca