File size: 2,071 Bytes
be92098
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
---

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

```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}

}

```