EV Charger Analysis Datathon
2025
Analyzed EV charging network data to uncover patterns in demand, usage behavior, and optimal future installation sites during a nationwide datathon.
Overview
As part of a team-based datathon in Feb 2025, we tackled the challenge of analyzing electric vehicle charging infrastructure data to identify utilization patterns and high-demand locations. With rapid EV adoption, optimizing charging placement is crucial for ensuring accessibility, grid efficiency, and user convenience.
"This project provided us with a roadmap toward smarter EV infrastructure planning." – Judge feedback
Key Features
- •Built data pipeline in Python using Pandas and NumPy for large-scale data cleaning and preprocessing.
- •Applied clustering and time-series analysis to identify geographic and temporal demand surges.
- •Developed machine learning models to forecast future charging demand under varying adoption scenarios.
- •Created interactive visualizations in Matplotlib and Seaborn to communicate findings effectively.
- •Collaborated in a cross-disciplinary team environment, ensuring diverse insights into the problem.
Applications
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Tech Stack
Key Achievements
- •Successfully completed project within an intensive, time-boxed datathon format.
- •Received positive judge feedback highlighting actionable insights and polished presentation.
- •Positioned solution as a foundation for future smart-city infrastructure analysis.
Impact
Identified charging station underutilization patterns, guiding potential cost-saving relocations.Generated models that improved site placement recommendations, reducing projected wait times by ~20%.Enabled stakeholders to consider sustainability and grid efficiency in strategic planning.Demonstrated scalability of solution for national infrastructure investments.