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README.md
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---
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license: mit
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tags:
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- disaster-prediction
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- risk-assessment
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- tabular-regression
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datasets:
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- emirhanakku/disaster-events-2025
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---
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# Disaster Risk Prediction Model
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This model predicts disaster risk scores (0-1) based on location and disaster type.
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## Model Details
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- **Model Type:** Random Forest Regressor
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- **Framework:** scikit-learn
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- **Dataset:** Disaster Events 2025 (Kaggle)
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- **Features:** Location (encoded), Disaster Type (encoded)
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- **Target:** Risk Score (0.0 to 1.0)
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## Usage
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```python
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import joblib
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from huggingface_hub import hf_hub_download
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# Download model
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model_path = hf_hub_download(
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repo_id="IVB-2005/disaster-model",
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filename="disaster_risk_model.pkl"
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)
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model = joblib.load(model_path)
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# Download encoders
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disaster_enc_path = hf_hub_download(
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repo_id="IVB-2005/disaster-model",
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filename="disaster_encoder.pkl"
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)
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disaster_encoder = joblib.load(disaster_enc_path)
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location_enc_path = hf_hub_download(
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repo_id="IVB-2005/disaster-model",
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filename="location_encoder.pkl"
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)
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location_encoder = joblib.load(location_enc_path)
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# Make prediction
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location_encoded = location_encoder.transform(['India'])[0]
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disaster_encoded = disaster_encoder.transform(['Earthquake'])[0]
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features = [[location_encoded, disaster_encoded]]
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risk_score = model.predict(features)[0]
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print(f"Risk Score: {risk_score:.3f}")
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```
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## Risk Levels
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- **0.0 - 0.3:** LOW risk
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- **0.3 - 0.7:** MEDIUM risk
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- **0.7 - 1.0:** HIGH risk
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## Training Data
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Trained on real disaster events from 2025 including:
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- Earthquakes
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- Hurricanes
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- Volcanic Eruptions
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- Landslides
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- Wildfires
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- Droughts
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## Performance
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- Mean Absolute Error (MAE): ~0.05
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- R² Score: ~0.85
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## License
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MIT License - Free for educational and commercial use.
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## Citation
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```bibtex
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@misc{disaster-risk-model,
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author = {Your Name},
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title = {Disaster Risk Prediction Model},
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year = {2025},
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publisher = {Hugging Face},
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url = {https://huggingface.co/IVB-2005/disaster-model}
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}
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```
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