Trip MealAdvisor

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  Landing Page

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  Choose familiar restaurant preferences

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  Suggested list of restaurants

Inspiration

We were frustrated with juggling Yelp's functionality and traveling to a new city. We wanted to eat at places that were like familiar restaurants at home, and not just at the places that were simply the highest rated.

What it does

Based on the user given home and current zipcode, finds new restaurants in current zipcode based on the home zipcode. (We attempted to use machine learning methods to complete the suggestions, but had to resolve to more rudimentary methods because we ate up a lot of time learning Flask).

How we built it

Built in Flask. Used the Yelp API to access restaurant information based on location and price range, then filtered through the user's preferred restaurants to return a list of suggestions.

Challenges we ran into

We were using Flask and web APIs for the first time.

Accomplishments that we're proud of

Built an app in 1.5 days that almost works! It was also super cool to actually see current restaurants that we actually like and frequently go to in our app.

What we learned

Learned how to use a simple web form and parse through web data.

What's next for Trip MealAdvisor

Build out functionality of the suggestions, using machine learning + collaborative filtering to process restaurants by tags and user reviews. Also make the web app prettier.

Built With

• css
• flask
• html
• python
• yelp