What we learned
We wanted to choose a project related to public health that could be further analyzed with data science, so we chose to look at malaria.
What our project does
Our project computes malaria statistics for each African country, condenses all of it into a single dataset, and generates interactive data visualizations so users can better understand the data.
How we built our project
Building this, we used Google Colab as our IDE, found our datasets on Kaggle, and wrote it in Python. We used pandas, geopandas, and plotly express libraries.
Challenges we faced
We had many issues downloading and importing geopandas on our original IDE, VS Code. We also ran into problems converting one dataset to an SHP file. In addition, we had trouble finding datasets and had to change our research question a few times.
What we are most proud of
Our group is most proud of our data visualizations.
What we learned
We learned to collaborate effectively, quickly solve problems, and divide our work.
What is next for our project
In the future, it would be interesting to model how our changing climate might affect malaria. We also are interested in tracking mosquito populations to see if there is a correlation.
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