Overview

This project utilises clustering algorithms to categorise countries based on key socio-economic and health metrics. The goal was to identify and prioritise countries in dire need of funds for maximum impact. The dataset used was from kaggle.

Dependencies

• Matlab 2020a: Main software for data processing and clustering.
• Plot2LaTeX: Script used for generating figures and visual representations (requires Inkscape).
• Clustering Algorithms: Algorithms sourced from the textbook Introduction to Pattern Recognition.

Methodology

The data underwent preprocessing where features were first grouped based on their inherent relationships into specific categories: health (child mortality, health, life expectancy, total fertility rate), trade (imports, exports), and finance (income, inflation, GDP). Features were then combined into broader categories, therefore, enhancing the comprehension of inter-feature relationships.

Following this grouping, the dataset was normalised using a min-max scheme to ensure all features contributed equally. A correlation matrix was constructed and visualised using a heatmap.

For clustering, the k-means algorithm was applied. The initialisation of the centroids was done using the rand_data_init.m function and iteratively refined clusters using the k_means.m function until convergence. The final result was the dataset partitioning into three distinct clusters, with each cluster showcasing its associated countries.

Key Findings

• A strong correlation exists between low income and high child mortality, a known concept in economics and public health.
• Countries exhibiting this correlation typically have underdeveloped economies and weaker healthcare infrastructures.
• Through clustering, the report identifies specific groups of countries that might be more in need of development aid. For instance, countries in Cluster 1 are suggested to be in greater need.
• Initial evidence indicates a potential for more in-depth clustering analysis. However, it's important to note that unsupervised analysis has its limitations, and drawing definitive conclusions can be challenging.

For a more detailed view of the analysis, and the conclusions thereof, have a closer look at the report.

Sample Output

Clustering visualisation:

Coutries within clusters:

           Cluster 1	           Cluster 2	           Cluster 3
          ----------	          ----------	          ----------
             Albania	 Antigua and Barbuda	             Belarus
              Belize	              Bhutan	             Algeria
              Angola	             Armenia	          Azerbaijan
             Bahrain	         Afghanistan	           Argentina
           Australia	             Austria	             Bahamas
                   .	                   .	                   .
                   .	                   .	                   .
                   .	                   .	                   .
               Tonga	             Tunisia	             Ukraine
             Vanuatu	             Vietnam	United Arab Emirates
          Uzbekistan	           Venezuela	               Yemen
              Zambia	         Timor-Leste	              Uganda
      United Kingdom	       United States	             Uruguay