Visualizing Trends and Correlation Between Fashion Features for Product Design Prediction Using Classification and Sentiment Analysis

To demonstrate the interrelation of fashion elements for design forecasting, the research examines classification and sentiment analysis methodologies. The study combines survey data with information from social media and e-commerce sites to find important emotional and behavioral patterns that affect how people make buying decisions. The research employs deep learning, Logistic Regression, and Random Forest models to predict design trends and user preferences. The research methodology focuses on improving fashion analytics through feature selection and user segmentation and visual storytelling methods to enhance strategic decision-making.