PCA – Top 10 Countries Component Analysis

This project visualizes the top 10 countries based on their Principal Component Analysis (PCA) values for the first two principal components (PC1 and PC2). It loads a pre-trained PCA model, extracts the data, and plots a grouped bar chart for comparison.

📌 Project Overview

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated variables into a smaller set of uncorrelated variables called principal components.

In this project:

• A saved PCA model (pca_model.pkl) is loaded.
• The top 10 countries are selected based on their ordering in the PCA results.
• PC1 and PC2 values are visualized in a side-by-side bar chart for easy comparison.

🛠️ Requirements

pip install matplotlib numpy