A synthetic dataset of electric vehicle charging sessions and financial risk profiles across two primary categories for machine learning research. It includes simulated behavioral data such as charging duration and energy consumption alongside financial risk indicators.
Use Cases
- Predict energy demand using the charging duration and energy consumption features.
- Train classification models to identify high-risk profiles using the financial risk labels.
- Analyze the correlation between charging frequency and financial risk scores.
Strengths
- Includes synthetic charging session parameters such as duration and energy consumption.
- Features financial risk labels mapped to individual charging behavior profiles.
- Provides simulated data for testing ML models on paired infrastructure and financial datasets.