You can deploy this model and start earning money today!

IVB-2005/
disaster-model

disaster-prediction

risk-assessment

tabular-regression

emirhanakku/disaster-events-2025

mit

Views

No views yet

Disaster Risk Prediction Model

This model predicts disaster risk scores (0-1) based on location and disaster type.

Model Details

Model Type: Random Forest Regressor
Framework: scikit-learn
Dataset: Disaster Events 2025 (Kaggle)
Features: Location (encoded), Disaster Type (encoded)
Target: Risk Score (0.0 to 1.0)

Usage

python

import joblib
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(
    repo_id="IVB-2005/disaster-model",
    filename="disaster_risk_model.pkl"
)
model = joblib.load(model_path)

# Download encoders
disaster_enc_path = hf_hub_download(
    repo_id="IVB-2005/disaster-model",
    filename="disaster_encoder.pkl"
)
disaster_encoder = joblib.load(disaster_enc_path)

location_enc_path = hf_hub_download(
    repo_id="IVB-2005/disaster-model",
    filename="location_encoder.pkl"
)
location_encoder = joblib.load(location_enc_path)

# Make prediction
location_encoded = location_encoder.transform(['India'])[0]
disaster_encoded = disaster_encoder.transform(['Earthquake'])[0]

features = [[location_encoded, disaster_encoded]]
risk_score = model.predict(features)[0]

print(f"Risk Score: {risk_score:.3f}")

Risk Levels

0.0 - 0.3: LOW risk
0.3 - 0.7: MEDIUM risk
0.7 - 1.0: HIGH risk

Training Data

Trained on real disaster events from 2025 including:

Earthquakes
Hurricanes
Volcanic Eruptions
Landslides
Wildfires
Droughts

Performance

Mean Absolute Error (MAE): ~0.05
R² Score: ~0.85

License

MIT License - Free for educational and commercial use.

Citation

bibtex

@misc{disaster-risk-model,
  author = {Your Name},
  title = {Disaster Risk Prediction Model},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/IVB-2005/disaster-model}
}