Inspiration
Our team grew up avid Star Wars fans. As fans ourselves, we know how passionate the community of Star Wars is, and when given a chance to build a new tool to help fans interact with the community, we were very eager to get to work. We wanted to give fans a way to compare their interests to those of other Star Wars fans and allow them to find their own place in the galaxy of Star Wars fans.
What it does
The website asks for user input on their preferences for certain Star Wars-related categories and compares these answers to other Star Wars reviews within the database, reporting a value count and percentage of reviews that agree with the user.
The form on the website allows users to give their custom input on their own Star Wars favorites and compare those choices with other reviews. Upon submission, it provides the user with visual comparisons and insights into the dataset and where their choice fits with other fans. We also included a Node map, allowing the user to see the links between custom inputs in the dataset and find what favorites may be similar to their own choices.
How we built it
The website front-end was created through the streamlit library using Python alongside other libraries (pandas, altair, etc.). The node map visual was created through Plotly.
Challenges we ran into
The first challenge we faced was deciding what tools and coding languages to choose from. As we are all first-year students, we had limited coding knowledge. Most of us had only used Python-based programs, such as VS Code and Google Colab. We also found brainstorming an idea that felt novel enough difficult. We felt the Pop Culture data set would best suit us as a beginner team, but we also felt limited in what we could do with the data in terms of building a tool that would solve any real-world issues.
Accomplishments that we're proud of
We are proud of how our team has worked together to learn new software to build our project. Coming into our first datathon, we did not know what to expect and believed our limited experience would limit what we could build, but our team has worked really hard to learn Streamlit and Plotly, allowing our project to become possible.
What we learned
We learned how to use Streamlit to build projects with Python logic and learned Plotly to build a Node Map for our project, visualizing the data in a clear and aesthetically pleasing format. We also deepened our understanding of other Python libraries. We use sophisticated techniques, such as MinHash, LSH, and Jaccard scores to build a performant stack.
What's next
The next step for our project would be to publicly deploy the project using Streamlit. Another possible avenue for expansion would be to add additional datasets for different film series, allowing for fans of other franchises to interact with our website, serving as a social tool that will allow users to connect with over similar niche interests. The project could also be expanded to include a forum or chat room that allows users to communicate with those who share their interests and have a safe place to discuss their interests with similar fans in real-time.
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