ANALYZING GLOBAL POPULATION GROWTH: K-MEANS CLUSTERING FOR REGIONAL DEMOGRAPHIC INSIGHTS
Keywords:
K-Means Clustering, Global Population Growth, Demographic Analysis, Data SegmentationAbstract
Global population growth significantly impacts various aspects of development, including socioeconomic planning and environmental sustainability. This study applies the K-Means clustering algorithm to analyze and categorize global population growth rates, aiming to uncover patterns and trends among different regions. The research uses a dataset comprising population growth rates from multiple countries over a specific timeframe. Data preprocessing steps, such as normalization and outlier handling, were conducted to ensure the accuracy of the analysis. The optimal number of clusters was determined using the elbow method, and the clustering results were validated using silhouette scores to ensure reliability.The analysis identified three distinct groups of countries based on their growth dynamics: rapid growth, moderate growth, and population decline. Countries experiencing rapid growth include Pakistan and Nigeria, while nations with moderate growth include India and the United States. In contrast, countries with population decline are represented by Japan and some European nations, such as Germany. These insights provide valuable information for policymakers and researchers to develop targeted strategies for addressing demographic challenges. This study demonstrates the effectiveness of the K-Means algorithm in analyzing complex population growth data and its potential applications in demographic and socioeconomic research.
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