LEVERAGING DIGITAL TRANSFORMATION AND SOCIAL MEDIA ANALYTICS FOR OPTIMIZING US FASHION BRANDS' PERFORMANCE: A MACHINE LEARNING APPROACH

This study explores how machine learning algorithms can optimize the performance of US fashion brands by analyzing the relationship between digital transformation, social media analytics, and customer engagement. Using a Kaggle dataset, models including linear regression, random forest, gradient boosting, and neural networks were evaluated to predict brand performance. Neural networks achieved the highest accuracy (R-squared: 0.92), while gradient boosting balanced performance and interpretability (R-squared: 0.88). Results highlight the critical role of customer engagement in driving brand success and demonstrate the potential of machine learning for actionable insights. This research provides a robust framework for data-driven strategies in the fashion industry.