Foodar

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  UI for Location Input

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  Output for Recommendations

Inspiration

According to a study conducted by the National Library of Medicine in 2018, North American college students were more prone to unhealthy eating habits, which over prolonged periods of time, led to severe mental health issues. Having recently started university, we’ve also begun to understand the significance a healthy diet plays in critical thinking, keeping up with classwork, and even maintaining a social life. Although, is it really feasible for newly transitioning university students to pay mind to their eating habits, considering the expectations and responsibilities placed on them? What about those who may still remain unfamiliar with the best, safest, and most accommodating places to eat around them? Is eating healthy easy for such audiences?

What it does

Currently, this project takes in any US location, and produces the name, location, and website of the top 3 restaurants with those given parameters.

How we built it

We built this project with Python (data collection and processing) and Angular (for the frontend).

Challenges we ran into

We worked with ideas that much of the group did not have much experience with, so we had to spend more time learning new concepts before we applied it to the project.

Accomplishments that we're proud of

We are proud on what the webpage accomplishes as of now, as we have used cleaned datasets to provide users with relevant restaurants based on their current location.

What we learned

This project was a great learning experience for all of us, primarily learning data science applications of really large datasets with Python libraries, and then how to use those libraries for a webpage with Javascript.

What's next for Foodar

We feel this journey isn't over yet. Specify the Search We want to add more parameters to the search to make it more specific to the user’s location, and perhaps travel time limitations

User Preferences We want to customize the experience for our users. Thus, adding more parameters to the search (i.e. spice levels, cuisine type) will ensure a personalized experience for the users.

Restaurant Profiles To take this a step further, restaurants with large demonstrated interest can establish their own customizable profiles on the webpage for consumers to view.

Built With

• angular.js
• kaggle
• python
• typescript