PCA Project: Dimensionality Reduction on Synthetic Data This project demonstrates the application of Principal Component Analysis (PCA) on synthetic data to reduce dimensionality and visualize clusters in a simplified 2D space. The project consists of three main steps: generating synthetic data, applying PCA, and plotting the results.

Project Overview PCA is a powerful tool in machine learning for reducing the number of dimensions in a dataset while preserving as much variance as possible. Steps: Generate synthetic data with distinct clusters. Apply PCA to reduce the data from three dimensions to two. Visualize the results to observe how PCA groups the data by its most significant components.