Inspiration
As someone going to an all-girl school and having quite a few friends in the LGBTQ+ communities, I am deeply concerned about gender equality in the society. With the increasing diversity of gender identities and sexual orientations nowadays, people tend to forget that these minority groups still face discrimination and unfair treatments in society, especially the workforce. So, I build this website to visualize some government statistics on employments and salaries, raising the question "Are we paid equal". Gender identity, however, is not the only factor that plays a role in pay. Industry, age and ethnicity all have an impact. Therefore, my visualizer also incorporates other elements that impact salaries, giving a comprehensive view of different income statuses, promoting equal pay across industries, gender identities and ethnicity groups.
What it does
It visualizes government statistics on employments and salaries; it also gives a predicted income for the user if they input their age, ethnicity and sex. This prediction is based on the supervised learning model that learned from a huge dataset (salaries by sex, ethnicity groups and ages in America from 2021 to 2023)
How we built it
I used Flask on Github, and used html to build the front end. A bit of javascript was also incorporated. I used plotly and pandas for data analysis and visualization.
Challenges we ran into
This is my first time participating in a hackathon, so I didn't know how exactly worked. I tried out a lot of platforms for frontend and backend developments, which took me a lot of time. I also had to learn a lot of syntax on the spot as my experience with python is limited and I have never used html or csv before.
Accomplishments that we're proud of
Being able to figure everything out by watching online tutorials; building my first project from scratch; grasped the basics of flask and html despite having no prior experience.
What we learned
how github works; html; pandas; visualization tools with plotly
What's next for Pay Gap Visualizer
I will collect more data from a more diverse sources (e.g. internationally), and make the site more interactive (users being able to share their opinions about pay gaps and talk to each other); I also plan to use unsupervised learning and reinforcement learning to do a deeper analysis of a larger dataset, finding patterns that I did not previously notice and producing a more accurate salary prediction.
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