Analysis of Machine Learning Algorithm for Fashion Trends

Fashion trends are inherently dynamic, driven by social, cultural, and economic factors. Machine learning (ML) offers powerful tools to analyze large datasets, identify patterns, and predict emerging trends. This paper explores the application of ML algorithms in forecasting fashion trends, focusing on key techniques such as supervised learning, unsupervised learning, and natural language processing (NLP). By comparing algorithms like decision trees, support vector machines, k-means clustering, and neural networks, this study highlights their strengths and limitations. The findings suggest that the integration of ML with domain expertise significantly enhances trend prediction accuracy, offering potential benefits for designers, retailers, and consumers.