metadata
license: mit
tags:
- disaster-prediction
- risk-assessment
- tabular-regression
datasets:
- emirhanakku/disaster-events-2025
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
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
@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}
}