Fashion Trends Prediction Tool
Prof. Satish Yedage
Dept.of Computer Engineering
K J College Of Engineering and Management Pune .
satishyedge2000@gmail.com
Kunal Sapkal
K J College Of Engineering and Management Pune .
kmsapkal007@gmail.com
Sanket Suradkar
K J College Of Engineering and Management Pune .
sandysuradkar12@gmail.com
Tejas Sonar
K J College Of Engineering and Management Pune .
tejas.sonar547@gmail.com
Sumit Rasal
K J College Of Engineering and Management Pune .
sumitrasal8975@gmail.com
Abstract – Fashion trend prediction plays a vital role in helping brands stay competitive by responding to rapidly changing consumer preferences. Traditional forecasting methods are increasingly being replaced by machine learning (ML) models that utilize vast datasets from past sales, customer preferences, and social media activity. These ML-based models provide a more accurate and dynamic approach to identifying emerging trends. This project focuses on developing an ML-driven model that predicts fashion trends by analyzing historical sales data and customer preferences. Techniques like clustering are used to segment consumers based on purchasing behavior, while neural networks and social media analysis help identify emerging styles and trends, as demonstrated in studies by Iqbal and Khan (2021) and Park and Kim (2022). Integrating multiple data sources, including customer feedback and external factors like weather patterns, enhances the prediction’s accuracy and adaptability. The power of ML lies in its ability to process real-time data, allowing brands to adjust quickly to market shifts. As consumer preferences evolve, these models continuously refine predictions, improving demand forecasting and inventory management. By aligning fashion offerings with emerging trends, this approach ensures a more personalized experience for customers and enhances brand competitiveness. Ultimately, integrating ML with customer-centric data allows for more precise forecasting, ensuring that fashion brands can predict trends and make data-driven decisions with confidence.
Keywords: Fashion trend forecasting, hybrid model, customer preferences, personalization, data analytics
