KassoumHabibou/Mapping-Hunger-in-Nigeria

# Mapping Hunger in Nigeria: A Data-Driven Approach using Machine Learning ## Project Overview This repository contains the replication codes for a spatial machine learning analysis predicting child stunting rates across Nigerian Local Government Areas (LGAs). The project combines multiple national surveys (NDHS, MICS, and LSMS) with geospatial data to generate high-resolution predictions of stunting rates and evaluate the impact of nutrition interventions. ## Data Sources - **Survey Data**: - Nigeria Demographic and Health Survey (NDHS) - Multiple Indicator Cluster Survey (MICS) - Living Standards Measurement Study (LSMS) - **Geospatial Data**: - Survey cluster coordinates - Environmental covariates - Infrastructure and goephysical data ## Repository Structure ``` ├── input/ │ ├── data/ # Original survey datasets │ ├── paper/ # Papers and litterature ├── output/ │ ├── data/ # Cleaned datasets │ ├── img/ # Spatial Images ├── paper/ # Papers ``` ## Requirements - Python 3.8+ - R 4.0+ ## Installation 1. Clone this repository: ```bash git clone github.com cd nigeria-stunting-prediction ``` 2. Create a virtual environment: ```bash python -m venv env source env/bin/activate # On Windows: env\Scripts\activate ``` 3. Install dependencies: ```bash pip install -r requirements.txt ``` ## License This project is licensed under the MIT License - see the LICENSE.md file for details. ## Authors - IBRAHIM KASSOUM Habibou - HEMA Aboubacar ## Contact For questions or feedback, please open an issue or contact [ibrahimkassoumhabibou@gmail.com].