This project focuses on developing a classification-based predictive machine learning model to forecast stock movements across global equity markets, specifically predicting whether equities increased or decreased in value. Utilizing a robust dataset of financial indicators and performance metrics, the analysis employs the eXtreme Gradient Boosting (XGBoost) algorithm to construct an efficient and interpretable classification framework. The primary objectives are to assess the model's accuracy in predicting stock movements based on historical data from diverse equity markets and to enhance its performance through hyperparameter tuning, handling imbalanced data, and analyzing feature importance. This approach highlights the potential of machine learning to support data-driven decision-making and investment analysis in global equity markets.

This project explores the relationship between Apple Inc.’s stock performance and the S&P 500, a widely recognized benchmark for the overall market. By examining the dynamics between these two entities, the analysis aims to understand the extent to which Apple's stock movements are influenced by broader market trends versus other specific factors.