Inspiration: After learning of our task and how we could help the Big Brothers Big Sisters of America organization, we were instantly inclined to combine machine learning in our efforts to help littles pair with bigs, as so many littles remain unpaired today. This is when we truly realized the goal and scope of our project, inspired especially by a meeting with one of our mentors.
What it does: Our project provides an in-depth, comprehensive survey for littles in a mobile setting, addressing many problems that may plague littles currently. We then collect the data obtained from the survey in our database, hosted by Firebase. Of course, this information will be vital in pairing littles with bigs and saving some valuable time. Furthermore, there is an admin portal feature in which the survey's results are displayed on a map of the United States. This map shows where bigs need to focus their attention most across the country, allowing them to spread and utilize their resources accordingly.
How I built it: Our team built this by making a JSON file of questions and question types (keys and values) that we could read in from another file. Incorporating Firebase to keep track of the data obtained allowed us to store valuable information provided by the littles. Based on the database collection and a series of other inputs (such as happiness of a certain region, COVID-19 impact on that region, and average survey response on a scale from 1-10), we were able to produce a machine learning model, displayed in the form of a map of the United States, in which the needs of the littles are highlighted accordingly. Finally, to make the UI appealing for our audience full of littles, we used some basic free CSS templates and styled it using html and css elements.
Challenges I ran into: Some challenges that we ran into were how to organize all the survey questions that we had compiled earlier. Initially, we actually hard-coded all the survey questions into an html file. Realizing how inefficient we were, we applied the idea of loose coupling, refactoring our survey questions to a JSON file and allowing the logic of our survey to simply run through the data. It was challenging to think of a way to make our code more efficient in that manner, but once we refactored it we made our lives a lot easier!
Accomplishments that I'm proud of: I'm proud that our team was able to come together and finish a hackathon together. The odds were against us, given all of our busy schedules and the remote nature of this hackathon (and all work in general). Additionally, a lot of us were participating in our very first hackathons, so I'm glad that we were able to develop something as a team, something that is effective and an innovative solution to the problems littles face. Accomplishing our first hackathon as a group was just a start, and I hope we, as a group, can work towards bigger and better solutions in the near future.
What I learned: From this experience, we learned many valuable skills and processes. First and foremost, the ability to collaborate as a team on this project taught us how efficient good communication can be and expanded our horizons on what we can accomplish as a team. We also learned and improved on many skills such as debugging (especially someone else’s unfamiliar code), integration of useful external resources such as firebase for user authentication and High-charts for incorporating our machine-learning model, being able to work with GitHub from the command-line, etc. It is also worth mentioning that we learned to persevere and stay determined, given that it was easy to give up and quit at many times given our busy college lives and other setbacks/delays.
What's next for Project Protect & Equip: We hope that our project can connect as many littles to bigs as possible, and that we can have a positive impact on the way Big Brothers Big Sisters of America organization provides aid for littles' development and growth. We seek to participate in more hackathons in the future, continuing in our growth, learning from new experiences, and contributing positively to society.