Inspiration
Currently, egg prices are very high, so we wanted to connect users to cheap eggs.
What it does
Routes users to nearby groceries with cheapest eggs + gas price to drive there.
How we built it
Django frontend, playwright scraping, oxylabs ip proxies, kaggle datasets, google maps api, folium, polyline. We used a Kaggle dataset for target and walmart locations. This part can be done with the google maps API, but due to time constraints a dataset was used to get location data. Then, we scrape using playwright to deal with dynamic loading, along with IP proxies from Oxylabs to get nearby grocery data. We spoof the geolocation of different walmarts and targets to get the egg prices from there. Then, using the google maps API, we calculate distance and calculate the cheapest eggs based on price + gas prices!
Challenges we ran into
Frontend, different scraping methods for each website, avoiding bot detection
Accomplishments that we're proud of
The usage of multiple APIs and the capability that it properly routes the user to the cheapest eggs.
What we learned
Never used google maps IP or IP proxying in scraping before.
What's next for I Want Eggs
Refine frontend, add scripts to scraping more websites, better integration with database.
Built With
- django
- folium
- google-maps
- kaggle
- oxylabs
- playwright
- polyline
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