Hydro-Power Allocation Optimization Model
Objective: To build a tool that helps energy planners understand how different hydro-power allocation decisions could affect job creation. The goal was to use data—not just assumptions—to support smarter policy and resource planning.
Methodology:
- Model Development: I trained a decision tree regression model to predict the number of jobs generated based on power allocation levels across regions. This allowed for more targeted and outcome-driven energy planning.
- Dashboard Build: To make the insights accessible, I created an interactive dashboard that let users input different allocation values and instantly see the projected impact on job creation.
- Data Focus: The project leaned on publicly available economic and energy datasets to tie power distribution directly to employment projections.
Key Outcomes:
- Predictive Insights: The model produced clear forecasts of job growth potential based on how hydro resources were distributed—helpful for planning both energy output and economic development.
- Visual Clarity: The dashboard turned raw data into something usable and decision-friendly, especially for non-technical stakeholders.
- Scalable Framework: While built around hydro-power, the same approach could be adapted to other resource allocation challenges—like budgeting, healthcare access, or education.
- Contribution & Future Scope: This project showed how machine learning can translate technical data into actionable insights for public good. It was a hands-on exercise in combining predictive modeling with human-centered design to support smarter, more transparent decisions. Future iterations could expand to include multi-resource tradeoffs or deeper regional modeling.
Explore More: Visit the project repository on GitHub for detailed insights: Hydro-Power Allocation and Economic Impact Analysis on GitHub