Unsupervised Learning to Identify Clusters on World Happiness Data

About Dataset: The data includes indicators of economic, social, and emotional well-being, such as GDP per capita, life satisfaction, social support, and life expectancy, for the year 2023.

Objective: The aim was to group countries with similar characteristics using KMeans clustering and PCA to simplify and visualize the data.

Optimal Clusters: The elbow method identified 3 and 5 as the optimal number of clusters. Countries were grouped based on similarities in economic and well-being indicators.

Key Findings: High Values: Countries with high GDP, life satisfaction, and life expectancy clustered together. Low Values: Different clusters were formed by countries with lower values in these indicators. Influencing Factors: Perceptions of corruption and negative affect were significant in clustering patterns.

Conclusion: The analysis provides insights into global differences in economic and well-being indicators, useful for shaping health and economic policies.

To view full project visit the following GitHub Link https://github.com/AmirMufti/Unsupervised-ML-Technique-KMeans-Clusterting-.git