- is to learn the best practices of data science from the industry while developing a COVID-19 analysis prototype.
- To trace the confirmed number, calculate the doubling rate and doubling time for 100+ countries.
- To generate SIR model simulation for 100+ countries.
The project focuses on the process of modeling (Python) and a methodology to approach a business problem.
The final result will be a dynamic dashboard - which can be updated by one click - of COVID-19 data with filtered and calculated data sets like the current Doubling Rate of confirmed cases
Techniques used are manual data gathering, Python Pandas, scikit-learn, Facebook Prophet, Plotly, Dash
For this, I have followed an industry-standard process (CRISP-DM) by focusing on the iterative nature of agile development
- Business understanding (what is our goal)
- Data Understanding (where do we get data and cleaning of data)
- Data Preparation (data transformation and visualization)
- Modeling (Statistics, Machine Learning, and SIR Simulations on COVID Data)
- Deployment (how to deliver results, dynamic dashboards in python)
The entire project follows the development flow of a rapid prototype project.
In final look, the dashboard is divided into 3 sections,
- Timelines confirmed, doubling rate Timelines confirmed:Number of confirmed cases registered for a particular country. Doubling rate: Rate at which a country's confirmed cases are doubling. Doubling rate filtered: Doubling rate data is filtered using 'Savgol filter' for better forecast.
- SIR model: S: Susceptible, I: Infected, R: Recovered An SIR model is an epidemiological model that computes the theoretical number of people infected with a contagious illness in a closed population over time.
- World map: Shows the confirmed cases for each country.
Final look of Dash Board!
