The COVID-19 pandemic has dramatically changed our lives, and it is important that we stay vigilant. Data visualization is key in understanding the way the pandemic has affected people all over the world, as well as keeping track of how we can do better to overcome these tough times.
What it does
Our project utilizes datasets provided by Merck to answer some key questions: Which country is vaccinating a larger percent of its population? What are the factors that influence vaccine administration (e.g., politics, economy, demography)? Using these questions as guidance, we visualized the data using Python libraries such as matplotlib, and we also created an interactive GUI to compare and contrast vaccination percentages across multiple countries.
How we built it
We used JavaFX to create the interactive GUI and Google Colab to visualize the data in Python.
Challenges we ran into
We did run into some challenges displaying the labels on the GUI. At first, we tried using a loop so that the user could select any number of countries to display in the graph. However, the labels on the x-axis appeared to be stacked on top of each other. As a temporary solution, we limited the number of countries that the user was to compare to 5.
Accomplishments that we're proud of
We were really glad to have taken important skills learned in class (Jupyter Notebooks, JavaFX) and apply them to a real-world product. Being able to visualize data is such an important tool, and we've learned a lot about how to present data in a way that might present any possible patterns or intriguing finds.
What we learned
One important way to compare data is to preprocess it using standardization - this makes it easier to observe relationships between features of the data that were recorded in different units.
What's next for Interactive COVID-19 Data Visualization
We would love to add more data to our GUI so that it can be more interactive, as well as provide more information about the relationships between different features of the dataset.