christianadebambo/uganda-hwc-policy
Domain:
peace and securityCreator:
chr
Host:
# Uncertainty-Aware Decision Support for Human-Wildlife Conflict in Uganda
## Overview
We develop a reproducible pipeline for human–wildlife conflict (HWC) decision support in Uganda’s Kasese District, combining:
- Severity modelling (logistic regression, TabTransformer)
- Calibration and uncertainty (Platt scaling, temperature scaling, conformal prediction)
- Uplift modelling (multi-arm T-learners with XGBoost)
- Off-policy evaluation (IPS, overlap weighting, doubly robust)
## Data
Dataset: _kasese-hwc-data-2021-combined-2021-2022-partly-cleaned.csv_
## Installation
Clone this repository:
```bash
git clone github.com
cd uganda-hwc-policy
```
Create a virtual environment and install dependencies:
```bash
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```
## Usage
Open the notebook:
```bash
jupyter notebook uganda-hwc-policy.ipynb
```
The notebook runs end-to-end:
- Loads raw CSV (update path if running locally)
- Cleans and engineers features
- Trains models
- Saves outputs under _outputs/_
_**N.B: Adjust file paths accordingly**_
## Outputs
Key artefacts saved in _outputs/_:
- _reliability_valid.png_ - calibration diagram
- _logistic_top_weights.csv_ - logistic regression weights
- _tabtransformer_perm_importance.csv_ - feature importance
- _uplift_recommendations_by_parish.csv_ - parish-level recommendations