Income Inequality Prediction System
Built a Random Forest regression model to predict global income inequality using historical socioeconomic data. Conducted EDA, feature engineering, and model evaluation achieving R² = 0.996 on the test dataset. Deployed an interactive Streamlit web application for user input and inequality trend visualization.
Project snapshot
Practical delivery with clean architecture, measurable outcomes, and maintainable systems.
Categories
AI/ML
Income Inequality Prediction System — delivered for real usage
We built this project with disciplined engineering: clear scope, rapid iteration, and production readiness (security, testing, monitoring, and maintainability).
- Clear scope, milestones, and delivery plan.
- Production readiness: security, testing, and observability.
- Scalable structure for future features and growth.
Built a Random Forest regression model to predict global income inequality using historical socioeconomic data. Conducted EDA, feature engineering, and model evaluation achieving R² = 0.996 on the test dataset. Deployed an interactive Streamlit web application for user input and inequality trend visualization.