As high school students, we spend almost every day of our lives at school. With such a direct influence on our lives, we believed attempting to improve the educational infrastructure of a city would allow us to directly contribute to the same influence that will be felt by future generations.
What it does
We built a novel interactive dashboard enabling the analysis of multiple features detailing the educational infrastructure of New York (NY). Additionally, we built an unsupervised model for the automatic clustering of schools within NY to enable the rapid discovery of schools that require additional economic support.
How we built it
To build our product, we used a Dash (Plotly) based interactive Dashboard on top of Python 3.6. To enable rapid data processing, we applied the Pandas python library. To enable our Machine Learning models, we used the Scikit-Learn Python library for unsupervised learning.
Challenges we ran into
We had much trouble creating the graphs in the format the Dash package uses. This was overcome by mostly trial-and-error. Additionally, we spent a large amount of time preprocessing the data so it was interpretable by our statistics and Machine Learning algorithm.
Accomplishments that we're proud of
The successful collaboration enabled through the usage of a team-wide Nextcloud server for real-time collaboration. Additionally, we were proud of learning new technologies and Python libraries such as Plotly and utilizing the Dash framework to create a Web App.
What we learned
We learned to create interactive visualizations on Plotly/Dash and effectively use Pandas for the processing of large-scale, high-dimensional data. Additionally, we learned how to phrase a thorough research question for in-depth analysis.
What's next for Investigation of the Educational Infrastructure of NYC
We look to obtain similar data for the analysis of the Birmingham School Infrastructure. This will allow us to play a part in a direct contribution to the improvement of the educational infrastructure and resources in our home, Birmingham.