Project Title: Extract Trending Products Daily from Depop
I know a bit about web scraping and a tiny bit about databases, but I’m taking on a project that’s larger than anything I’ve done before and need some help.
Depop (https://www.depop.com/) is a social e-commerce site where people sell second-hand clothing to each other (think instagram meets eBay).
Each user has a profile where they list items that they try and sell to other users.
I help my girlfriend run her Depop shop and would like to do some analysis on what items are trending on the platform so that we can increase her sales.
This will essentially involve some large-scale data scraping and data analysis.
The way I am thinking of approaching this is:
– Come up with a list of c.1,000 Depop users who have a large amount of weekly sales.
– Use the Depop API to get all the items that each of these users has (each user can have hundreds of items).
– Save all these items down in a database.
– The API provides a value that tells you whether the item is sold or not, so each item in the database will be marked “sold” or “unsold”
– Repeat this process each day.
– If an item that was “unsold” the previous day changes to “sold”, grab the URL from the API and scrape the item description
– The item description will contain up to five “#’s, which describe the item type – this is key
– I can separate the ‘#’s out from rest of the item description, so at the end of each day I’ll have a list of items that were sold that day and what ‘#’s were in their description – which should be a good measure of what’s trending
If this sounds interesting to you then do get in touch.
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