Outfit Recommendation System | IJCT Volume 12 – Issue 5 | IJCT-V12I5P76

The Outfit Recommendation System is a web-based fashion assistant that provides personalized suggestions for outfits, colors, and hairstyles. It bases its recommendations on the user’s skin tone, gender, occasion, and hair texture. The system simplifies the outfit selection process and helps boost users’ confidence by offering stylish choices that match their features and the event. The application was developed using Python and Flask for the backend, while HTML, CSS, Tailwind CSS, and JavaScript were used for the frontend. This setup creates an interactive and visually appealing user experience. It uses a rule-based recommendation system to match user inputs with appropriate dress colors, outfit styles, accessories, and hairstyle ideas. Each hairstyle suggestion comes with a link to a YouTube tutorial, making it easy for users to recreate the looks. For example, the system might suggest a teal or coral outfit with ethnic accessories for a user going to a festival, or it might recommend a navy formal suit for a job interview. Future updates will include features like AI-driven skin tone detection, machine learning for better personalization, and a database to store user preferences and history. By blending fashion knowledge with modern web technology, the Outfit Recommendation System provides a smart and user-friendly platform that connects personal styling with intelligent support.

The Outfit Recommendation System shows how technology and fashion can work together to create a personalized and user-friendly styling assistant. It makes outfit selection easier by offering recommendations based on an individual’s skin tone, gender, occasion, and hair texture. With a rule-based approach, it provides useful and relevant suggestions without needing large datasets or complex algorithms. Using Python and Flask for backend processing, and HTML, CSS, Tailwind CSS, and JavaScript for the frontend, the project creates an interactive and responsive web application. Including YouTube hairstyle tutorial links adds practicality, giving users a complete styling experience. The project’s modular design promotes clarity, efficiency, and ease of use, allowing users to make confident fashion choices that suit their look and occasion. The system performs well in generating quick and relevant recommendations, proving its efficiency and real-world usefulness. It also sets the stage for integrating more advanced technologies in the future, such as AI-based skin tone detection, machine learning-driven personalization, and database connections for storing user preferences. In summary, the Outfit Recommendation System not only simplifies outfit selection but also highlights the potential of smart web-based fashion solutions. It sets the stage for future innovation in digital fashion guidance, linking personal style, self-confidence, and contemporary technology.

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