Project 2

"Global Aid Allocation Optimization using Data Analytics"

In the "Global Aid Allocation Optimization" project, we use data analytics to strategically allocate $120 million in funding for an international humanitarian NGO. We employ Principal Component Analysis (PCA) to reduce data dimensionality, K-Means Clustering to identify clusters of needy countries, and Hierarchical Clustering to establish a hierarchy of development needs. By analyzing socio-economic and health factors, we guide the CEO in focusing aid on countries facing the most dire circumstances, ensuring the funds are used effectively to drive global development.