Inspiration
I was inspired to create this project as I wanted a simple yet interactive way to visualize COVID-19 Data. As the pandemic is slowly disappearing, it is interesting to see how it initially developed along with the correlation between Confirmed Cases, Active Cases, and Deaths. Thus, I created this data visualization project.
What it does
This project contains two animated map plots that allow the user to see the development of COVID-19 cases (Confirmed, Active, Deaths) over time globally. The user can also see another interactive graph and see the correlation between deaths and recovered cases on a global scale. Furthermore, an interactive scatterplot of the countries with the most deaths showcases how nations vary in the relationship between deaths and confirmed cases. Lastly, plots of the number of countries infected along with new cases per day showcase points of a significant increase in the time period of the pandemic.
How we built it
This project was built using Python, and the primary tool for visualization was plotly/plotly.express as this library allows for interactive visualization, making it more efficient to analyze the Covid-19 data.
Challenges we ran into
I'm not very experienced in data visualization as I'm more focused on AI/ML, so this was out of my comfort zone. The entire project was a challenge. However, the primary issue I ran into was altering the country-daily-cases dataset to create an animated map. I had to figure out a way to make the default date column in the data into a DateTime object, so this took up a lot of time in the project.
Accomplishments that we're proud of
Accomplishments I'm proud of include being able to make an interactive/animated map as this was the first time that I had used plotly to create maps.
What we learned
I became more familiar with plotly.express and how to create animated maps with the library.
What's next for Advanced CVOID-19 Visualization
The next thing for this project is to create a virtual infographic tool to display the visualizations in a more organized format along with more specific visualizations to analyze the trend of the virus.
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