Electronics & Phones & Spec Data Scraper (Only Serious and Fast delivery freelancer contact with fixed price, Dont do bid and later change price)
Budget: $30 – $250 USD
I’m after a one-off scrape that combines everything sold
on https://www.thebigphonestore.co.uk/ with the matching specification pages on www.gsmarena.com.
Scope of data
• From The Big Phone Store grab
Brand,
Model,
storage (GB),
Colour,
Manufacturer Code,
EAN,
every product image and any extra details you can reliably capture.
• For the same model on GSMArena pull the full specification table plus all available images.
• Only the products currently live on the site need to be covered; no scheduled runs.
Delivery format
The final dataset must come as a clean CSV—one row per SKU—with columns for each field and image file name. All images have to be downloaded locally, stripped of metadata, renamed logically (e.g., brand_model_colour_01.jpg) and placed in folders that mirror the CSV references. The goal is to upload later without Google flagging any copyright issues, so please make sure filenames, EXIF and alt text are neutral.
What I expect to receive
1. The CSV file.
2. A zipped folder structure containing every image.
3. The full scraper code (Python with BeautifulSoup/Scrapy, or Node if you prefer) and a short README so I can rerun it if needed.
4. A brief report of models that failed to match between the two sites, if any.
If this sounds clear and you have experience bypassing anti-scraping measures, let’s get started.
on https://www.thebigphonestore.co.uk/ with the matching specification pages on www.gsmarena.com.
Scope of data
• From The Big Phone Store grab
Brand,
Model,
storage (GB),
Colour,
Manufacturer Code,
EAN,
every product image and any extra details you can reliably capture.
• For the same model on GSMArena pull the full specification table plus all available images.
• Only the products currently live on the site need to be covered; no scheduled runs.
Delivery format
The final dataset must come as a clean CSV—one row per SKU—with columns for each field and image file name. All images have to be downloaded locally, stripped of metadata, renamed logically (e.g., brand_model_colour_01.jpg) and placed in folders that mirror the CSV references. The goal is to upload later without Google flagging any copyright issues, so please make sure filenames, EXIF and alt text are neutral.
What I expect to receive
1. The CSV file.
2. A zipped folder structure containing every image.
3. The full scraper code (Python with BeautifulSoup/Scrapy, or Node if you prefer) and a short README so I can rerun it if needed.
4. A brief report of models that failed to match between the two sites, if any.
If this sounds clear and you have experience bypassing anti-scraping measures, let’s get started.