Inspiration

We love to eat.

What it does

The application takes in the various meals a user has eaten in the past. It then picks a few suggested meals that will help compensate for any detected deficiencies or surpluses in the user's diet.

How we built it

We took a large dataset of various meals and recipes and trimmed out the fat. We then implemented a quick searching algorithm to find meals, calculate nutritional data, and suggest new meals. It searches hundreds of thousands of meals in only a few seconds to give you some exciting options.

Challenges we ran into

Nutritional info is surprisingly hard to understand. None of us are professionals in the category, so getting good recommendations was a bit of a challenge. The data set was also large to work with, so it was difficult to make sure our application handled it in a speedy and efficient manner.

Accomplishments that we're proud of

Some of the page design as well as the efficiency of our application.

What we learned

Using data processing tools to filter and optimize searches on a large data set.

What's next for What's Cookin'?

Further expansion and refinement of the application. Its current state is very much proof of concept and we would love to flesh this out to a larger thing that can be easily used on a daily basis. Additionally, the dietary restrictions are still in a WIP state, so finalizing that feature is a top priority.

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