An unsupervised machine learning project that uses K-Means and PCA to group countries based on socio-economic and demographic indicators for insightful global comparisons.
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This project applies K-Means clustering and Principal Component Analysis (PCA) to group countries into distinct clusters based on their socio-economic and development indicators.
By analyzing features such as GDP, literacy rate, population, life expectancy, and more, the project helps reveal patterns among nations and enables data-driven regional comparisons. The interactive web application was built using Streamlit for ease of exploration and visualization.
The dataset was cleaned, missing values handled, and numerical features scaled using standardization to ensure effective distance calculations in clustering. Key steps included:
PCA was used to reduce the dataset to two principal components for visualization while retaining maximum variance from the original features.
The dimensionality reduction simplified the data while preserving meaningful relationships between countries.
K-Means clustering was applied to the transformed data. The optimal number of clusters was determined using the Elbow Method and Silhouette Score.
The final clusters were visualized in a 2D PCA plot, colored by cluster label. The interactive visualization allows users to explore how countries are grouped based on similar traits.
January 2025
Defined project goals and collected country-level data from World Bank and UN datasets.
February 2025
Cleaned data, handled missing values, and standardized features for clustering analysis.
March 2025
Applied PCA for dimensionality reduction and implemented K-Means clustering with optimal parameters.
April 2025
Created interactive visualizations and deployed Streamlit application for public exploration.
Countries with high GDP, high literacy rates, and long life expectancies. Typically Western nations and some in East Asia.
Countries with rapidly growing economies, improving infrastructure, and increasing quality of life indicators.
Countries with moderate development indicators, limited infrastructure, but potential for economic growth with policy improvements.
Countries with low GDP per capita, lower literacy rates, and shorter life expectancies, requiring significant development assistance.
This analysis helps international organizations optimize aid distribution based on cluster-specific needs rather than regional generalizations.
Countries can identify peer nations in the same cluster to analyze their successful development policies and adapt them locally.
Investors can use cluster analysis to identify countries poised to transition between development stages for strategic investment.
This project analyzed 10-year historical data to identify countries that have successfully transitioned between clusters, particularly those that moved from Cluster 3 (Developing) to Cluster 2 (Emerging).
These insights provide a roadmap for countries seeking to accelerate their development trajectory using data-driven policy approaches.
This project demonstrates proficiency in preprocessing country-level datasets, performing unsupervised learning with K-Means and PCA, and building interactive web apps using Streamlit for global data analysis.