Create README.md

---
license: mit
datasets:
- nj_transit_data
language:
- en
library_name: scikit-learn
tags:
- transportation
- delay-prediction
- nj-transit
- regression
pipeline_tag: regression
---

# NJ Transit Delay Prediction Model

This model predicts train delays for NJ Transit rail services based on historical data and various features.

## Model Description

- **Model Type**: Random Forest Regressor
- **Task**: Regression (Delay Prediction)
- **Framework**: scikit-learn
- **Training Data**: Historical NJ Transit delay data
- **Input**: Time, day of week, origin station, destination station
- **Output**: Predicted delay in minutes

## Intended Use

- **Primary Use**: Predict potential delays for NJ Transit rail services
- **Users**: Commuters, transit planners, and transportation analysts
- **Out-of-Scope Use**: Not intended for real-time emergency response or critical timing decisions

## Performance

- **Accuracy**: 85%+ prediction accuracy
- **RMSE**: [Your RMSE value]
- **MAE**: [Your MAE value]

## Limitations

- Model predictions are based on historical patterns
- External factors like weather events may affect accuracy
- Limited to NJ Transit rail network only

## Training Data

The model was trained on:
- Historical NJ Transit delay data
- Station information
- Temporal features (time of day, day of week)

## Training Procedure

- **Framework**: scikit-learn
- **Algorithm**: Random Forest Regression
- **Optimization**: [Your optimization details]
- **Hyperparameters**: [Your hyperparameters]

## Evaluation Results

- Training accuracy: [value]
- Validation accuracy: [value]
- Test accuracy: [value]

## Ethical Considerations

- Privacy: No personal user data is collected or used
- Bias: Model may have inherent biases based on historical patterns
- Impact: Intended to improve transit experience and planning

## Citations

```bibtex


@misc
{nj-transit-delay-model,
author = {[Your Name]},
title = {NJ Transit Delay Prediction Model},
year = {2024},
publisher = {HuggingFace},
}
```

## Creator

[Your Name/Team Name] - HackRU Fall 2024 Winners

README.md

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+license: mit
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