Extended E-commerce parser (Selenium and bypassing anti-bot protection)

Objective: Develop a robust web scraper to collect real-time product data from dynamic e-commerce platforms (such as eBay) for price monitoring and analytics.

Main challenges:

Dynamic content: Data was loaded through complex JavaScript/AJAX requests rather than being simply present in HTML.

Anti-bot systems: Platforms used advanced algorithms to block automated actions.

Unstable layout: The structure of the pages (DOM) could change, causing regular hard-coded parsers to break instantly.

My solution:

Bypassing protection: I used Selenium with flexible stealth configurations for the webdriver. To make the script appear like a real person, I added natural behavior simulation (random delays between clicks, scrolling), which allowed data collection without the risk of being blocked.

Code resilience (Fallback Selectors): I implemented a system of dynamic fallback selectors. If the online store slightly changed its design or layout, the script did not crash with an error but automatically switched to a backup method of element searching and continued working.

Automatic navigation: I set up reliable pagination, allowing the autonomous collection of hundreds of listings from multiple pages in a single run.

Deep data cleaning: Raw data from online stores often contains junk. I applied regular expressions (Regex) to clean the text (for example, extracting the pure price without currency and spaces) and used Pandas to sort the final dataset by ascending price.

Technologies used: Python, Selenium (Stealth), Pandas, Regex (Regular expressions).

Result:
The client received not just a script, but a reliable tool. The output consisted of perfectly formatted, sorted, and production-ready CSV files that could be immediately uploaded to analytical systems or databases.