Project 2

Analyzed socio-economic and health factors of countries to identify those needing humanitarian aid. Applied Principal Component Analysis (PCA) to reduce dimensionality and retain 87% of the data's variance. Used K-Means Clustering and Hierarchical Clustering to classify countries into clusters based on similarity. Visualized the results using a scree plot, dendrogram, and cluster maps, aiding strategic allocation of $120M funding. Recommended countries in critical clusters, enabling data-driven prioritization for the NGO’s aid programs. Enhanced decision-making efficiency using advanced analytics and unsupervised machine learning techniques.