Country Data Analysis University Projects #Data Science#Machine Learning
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Overview
Application of Principal Component Analysis (PCA) to country-level socioeconomic data, demonstrating how dimensionality reduction reveals hidden patterns and enables meaningful visualization of high-dimensional data.
Key Concepts
- Preprocessed and standardized country data
- Computed covariance matrix and eigenvalue decomposition
- Identified principal components explaining most variance
- Created visualizations in reduced dimensions
- Analyzed country clusters and outliers
Technologies
R, PCA, Dimensionality Reduction, Data Visualization, ggplot2, Statistics