Inspiration
Women won't see equal pay until 2056 at the current rate we're at. But some countries like Colombia and Iceland closed their gaps in only 10 years. I got inspired to figure out what they did differently and help other countries replicate success instead of wasting decades.
What it does
The Pay Gap analyzes 60+ years of gender wage gap data across 50+ countries and uses machine learning to predict future trends. It identifies which countries are succeeding and reveals policies that worked. Governments and activists can see exactly what to implement.
How I built it
Frontend: Next.js with React for UI, Recharts for visualizations Backend: Python Flask API for ML predictions Data Processing: Pandas to clean and process data points from OECD (https://www.oecd.org/en/data/indicators/gender-wage-gap.html) Machine Learning: Scikit-learn - Linear regression model
Challenges I ran into
- Training accurate ML models with limited historical data for some countries
- Building responsive charts that could handle
Accomplishments that I'm proud of
- Successfully predicted wage gap trends with real data
- Created visualizations that make 60 years of data understandable
What I learned
- How to work with messy, real world economic data
- Linear regression is powerful and can do many cool things
- Some countries like Colombia and Iceland have already solved this problem, other countries just need to learn from them
What's next for The Pay Gap
- Add industry specific wage gap analysis (Healthcare, tech, finance)
- Expand to other inequality metrics such as race, age, disabilities
- Deploy website to the web
Log in or sign up for Devpost to join the conversation.