Izu-33/vaccine-dropout-prediction

Machine learning model to predict vaccine dropout risk (DTP1–DTP3) across African countries using immunization and demographic data. # Vax Track AI - Vaccine Dropout Prediction This project develops a machine learning model to predict vaccine dropout risk (DTP1–DTP3) across African countries using immunization and demographic data. It also includes an interactive **Streamlit app** for real-time predictions. Live Deployment: Streamlit App --- ## 🚀 Features - Preprocessing pipeline with scaling, encoding, and SMOTE for class balance - Models trained with **Logistic Regression, Random Forest, SVM, and XGBoost** using GridSearchCV - Automatic region selection when a user chooses a country - Streamlit dashboard with metric cards (**Dropout Rate, Coverage Average, DTP3 Coverage**) - Visualizations including boxplots and interactive charts --- ## 📂 Project Structure ```bash ├── data/ # Raw and processed datasets ├── notebooks/ # Jupyter notebooks for exploration & training ├── app/ # Streamlit app │ ├── app.py │ └── utils.py ├── models/ # Saved models and preprocessing pipelines ├── requirements.txt # Python dependencies └── README.md # Project documentation ``` ## ⚙️ Installation Clone this repo: ```bash git clone github.com cd vaccine-dropout-prediction ``` Create and activate a virtual environment: ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` Install dependencies: ```bash pip install -r requirements.txt ``` ## ▶️ Usage ### Run Streamlit app ```bash streamlit run app/app.py ``` ## 📊 Example App View - Metric cards for Dropout Rate, Coverage Average, DTP3 Coverage - Country & region selection with automatic region mapping - Prediction probability shown with visual feedback ## 📌 Future Improvements - Deploy on Streamlit Cloud or Azure Web App - Add additional health & socioeconomic predictors - Expand dataset beyond Africa