This space contains a Jupyter notebook with dataset on countries and its categorization using K-means Clustering. The problem statement asks to find out the countries in direct need of aid.

Background: HELP International is an international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities. It runs a lot of operational projects from time to time along with advocacy drives to raise awareness as well as for funding purposes.

After the recent funding programmes, they have been able to raise around $ 10 million. Now the CEO of the NGO needs to decide how to use this money strategically and effectively. The significant issues that come while making this decision are mostly related to choosing the countries that are in the direst need of aid.

As a data analyst, my job is to categorise the countries using some socio-economic and health factors that determine the overall development of the country. Then I need to suggest the countries which the CEO needs to focus on the most.