Online Clothing Recommendations and Virtual Trials using AI

This project uses computer vision and machine learning to create a virtual trial room and a recommendation system that will improve the e-commerce fashion experience. CNNs are utilized to forecast body form for more precise recommendations, and the recommendation engine uses collaborative and content-based filtering to offer fashion items based on user preferences, past purchases, and style. To provide clothing recommendations based on each user's tastes, style, and body type, the recommendation engine will make use of collaborative filtering and content-based filtering algorithms. At the same time, a computer vision-powered virtual trial room lets customers see how clothes fit by superimposing ensembles on userprovided images or avatars and modifying dimensions according to body measurements to create a realistic fit simulation. By analyzing user- provided pictures, we can enhance body form detection, improving fit accuracy and recommendation precision. Users will be able to upload photos, see suggestions, and virtually try on clothing in real time thanks to a responsive web interface. Data processing will be handled by the Flask or Django-built backend, which will also effortlessly interact with a PostgreSQL or MySQL database to store user and suggestion data. The system is built for high performance and scalability and is hosted on cloud infrastructure. By offering personalized recommendations and lowering returns with precise fit visualization, this integrated system seeks to increase user happiness. This website allows users to digitally try on clothing and make purchases, which eventually improves consumer happiness and lowers return rates.