Project 2

Problem Statement :

Assume you are appointed as a Data scientist in any international humanitarian NGO, after the recent funding programmes, have been able to raise around $ 120 million. Now the CEO of the NGO call you to choose how to use this money strategically and effectively. The significant issues that comes while making this conclusion are mostly related to choosing the countries that are in the direst need of aid. Your job is to classify the countries using some socio-economic and health factors that determine the overall development of the country. Then you need to suggest the countries which the CEO needs to focus on the most. Apply Principal component analysis, K-Means Clustering & Hierarchical Clustering.

Conclusion: I had done this project step-by-step actions on how to approach this task using Principal Component Analysis (PCA), K-Means Clustering, and Hierarchical Clustering.

The steps include data preprocessing, PCA for dimensionality reduction, and clustering methods for identifying groups of countries.

We can further analyze and interpret the clusters to identify countries in the direst need of aid.

Interpretation:

• The countries within each cluster are considered similar based on the features used in clustering (e.g., socio-economic and health indicators).
• Countries in the same cluster are expected to share common characteristics, and the clusters represent groups of countries with similar development profiles.

To decide which countries need aid the most:

• Analyze the characteristics of each cluster, considering key socio-economic and health indicators.
• Prioritize aid to countries in clusters associated with higher needs or challenges.