We wanted to create some type of data visualization software and we needed large dumps of data in order to fully encompass our vision. Capital One's Nessie API gave us the tools and extraction methods to gather large sets of data in order to implement into our visualization systems.
What it does
The software that we created makes request calls using the Nessie API. The data dump that we get is then processed and parsed in order to extract the specific attributes and elements we wish to display. These lists of various data elements are then formulated and grouped together in various plots and graphs. The objective was to inform Capital One on how their resources and branches are performing. It gives them the option to monitor and predict what type of changes will occur in the near future.
How I built it
Using python we made request calls to the Nessie API that enabled us to acquire lists and dictionary of Capital One's assets such as branches, account informations, and atm locations. Then we processed and slightly manipulated the form of the data so that we could implement PlotLy to display our graphs and plots. PlotLy ran on a local machine to produce the graph objects which can then be viewed on their dashboard website.
Challenges I ran into
While using the Nessie API we encountered small bugs that slightly halted our progress. However we were able to work with the Capital One team in order to find ways around these problems/potentially fix them. Additionally many of the plot construction templates included a lot of varying attributes that needed to be set, so we were unable to fully customize the display.
Accomplishments that I'm proud of
Together as a team we weren't that experienced with data management and json requests. However we worked together and appropriately asked for help and were able to come up with a valid project. In this process we learned a lot about how to code large projects in a group as well as the different api's available for us to use.
What's next for Capital One Data Visualizer
In the future we can optimize the data parsing efficiency so it can work in real time. Additionally we can look further into PlotLy to fully understand all the options and customizations that are available to use. We could potentially work with Capital One as well in implementing some of our algorithms and general software ideas in order for them to provide a better customer experience.
Qualtrics - Best use of data visualization
Capital One - Best usage of the Capital One API
BNY Mellon - Investment/Finance focused