mattbardal/ginius

An interactive predictive model for gini indices built during the 2023 Calgary Hacks hackathon.

ginius: an interactive predictive model for gini indices

The goal of ginius was to find a way to portray data from the World Bank catalog in a fun and interactive way.

Learn more on the devpost page!

To run it locally

1. Start the back-end

• cd ml-backend
• python3 ml_model_api.py

2. Start the front-end

• cd frontend
• npm i
• npm start

Features

• Scale attributes to see effect of education attainment on wealth inequality
• Select from a list of countries (Canada, Australia, The Netherlands)
• Choice between two regression machine learning models (Random Forest Regressor / Support Vector Regressor)

Tech stack

Front-end

• React
• TypeScript
• Chakra-UI
• Recharts

Back-end

• Flask
• Sci-kit learn
• GraphQL 🪦

Supporting Libraries

• Pandas
• Numpy
• Matplotlib

Authors

• @mattbardal
• @keithradford
• @Sean-Jamieson
• @kalebchisholm
• @malwa355

_{[1] Education Statistics
[2] Poverty and Equity Database}