Team Members: Christy Kim, Weihong Qi, Jason Wang, Jiaqi Zhu
The goal of this project was to understand and address global gender inequality and child labor by exploring their interconnections with data from World Bank, the International Labour Organization (ILO), UNICEF, and Facebook API. Students employed Elastic Net regression and advanced feature engineering techniques to build predictive models for the Global Gender Gap Index (GGI) and child labor rates. Key findings include an R² of 97.54% for GGI predictions and the identification of socio-economic factors such as the Human Development Index and labor force participation as critical child labor indicators. The team also developed an interactive dashboard, enabling users to visualize trends and correlations. These results will provide valuable insights to policymakers, supporting more targeted and effective strategies to address global inequality and reduce child labor.