NGO-AID-Allocation

NGO Aid Allocation Case Study

Objective

Allocate $10 million in aid by identifying and categorizing countries based on socio-economic and health development indicators.

Techniques Used

• Unsupervised Learning: K-Means Clustering to group countries based on indicators.
• Data Cleaning: Addressed missing and inconsistent data.
• Exploratory Analysis: Visualized trends and clusters.

Tools & Libraries

• Python: Pandas, Matplotlib, Seaborn
• Machine Learning: Scikit-Learn

Key Outcomes

• Identified high-priority countries for aid allocation.
• Delivered actionable insights to maximize impact.