Inspiration

Modern day online clothes shopping can be incredibly overwhelming. With various storefronts, styles, and suppliers available on thousands of sites around the world it can be hard to find the exact clothes you want. Our team has created a product that uses modern data science techniques to bridge this gap between you and the clothes you've been searching for.

What It Does

Style Finder combines two important aspects of fashion to simplify and refine the retail process. First, Style Finder utilizes user generated data to match articles of clothing, which helps to avoid any fashion disasters. If you find a shirt you like, Style Finder will show you a list of pants, shoes, or accessories that will all work well with the shirt, it does the thinking for you. Secondly, by using user supplied demographics, and a few simple quizzes, Style Finder is able to determine what kind of clothing fit your style. In fact, after observing your tendencies, Style Finder will be able to recommend a complete, custom outfit, just for you!

Style Finder is not designed to be a standalone store, but rather a central hub that directs users to the seller once the app has assisted in making their selections. In the future, an integrated storefront could be added to simplify the process, but this would require prior negotiations with sellers.

How It Works

Style Finder was built as a web app that works well on both desktop and mobile. The front-end is built with HTML, JavaScript, CSS, Bootstrap and jQuery. The bulk of the app is in the back-end which is built with PHP. There is a MySQL database that holds all the relational data necessary for the app to function.

The development of Style Finder was complicated by the inherent difficulty of obtaining user data for the analysis software to work effectively. The team generated sample user data for the purposes of demonstration, but to reach its full potential, much more data is necessary.

What's Next

To continue the development of Style Finder, more data is needed in order to refine the algorithms. Part of this gap can be filled by adding in more brands of clothing from their websites, which requires more time to be invested by the development team. To truly develop the site, it needs users to generate organic data, however; this is a strategy that would require the site to get into the hands of the public. The team looks forward to seeing this evolution going forward.

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