A quantitative analysis of 45 S&P 500 market leaders across 9 major sectors. This project utilizes K-Means Clustering (Unsupervised Machine Learning) to identify risk/return regimes automatically. It features a Facet-Wrapped visualization to demonstrate how volatility profiles differ significantly between Defensive sectors (Utilities, Staples) and Growth sectors (Technology). Built using R, ggplot2, and quantmod.

An unsupervised machine learning analysis of stock market data. This project uses K-Means clustering to automatically categorize assets into four distinct risk/reward regimes based on historical volatility and annualized returns. Built using R, quantmod and ggplot2.