Global Immunization Gap Prediction

What I did

• Analyzed 116,000+ global immunization records to identify patterns in vaccination coverage and support data-driven public health decisions.
• Cleaned and transformed complex survey data, engineered features, and addressed class imbalance using SMOTE to improve detection of low-coverage regions.
• Evaluated multiple models and identified XGBoost as top performer (F1-score: 96.7%, AUC: 0.99), generating insights to prioritize high-risk regions and optimize resource allocation.