Trends in fashion are rapidly changing consumer preferences, cultural shifts, and digital relationships. Instagram, Twitter, and TikTok serve as the primary platforms driving and disseminating fashion trends, filled with vast real-time data on user engagements, brand influence, and current trends. This study investigates the impact of social media in predicting fashion trends using machine learning techniques. .Using Natural Language Processing (NLP) and image recognition techniques, data on everything fashion that is accessible on social media are gathered and examined. These include sentiment analysis, tracking of hashtags, and assessing the impact of influencers to determine the trending styles. A predictive model is created using machine learning algorithms. The results find that social media data, if successfully processed, can give accurate and timely prediction about fashion trends. AI-driven analytical tools, therefore, have the potential to assist fashion brands, retailers, and designers in making data-informed decisions. The research thus adds to the growing field of fashion technology by bringing machine learning and digital trend analysis together to provide an opening for automated and scalable fashion forecasting solutions.
Fashion trends, Social media analytics, Machine learning, Predictive modeling, Natural language processing, image recognition, Sentiment analysis, Influencer marketing
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