🛠 Daraz Web Scraping Project — Real-Time Product Data with Selenium Project Overview: I built a custom web scraping solution using Selenium to extract product data from Daraz.pk, one of South Asia’s largest e-commerce platforms. The goal was to collect real-time product listings across multiple pages — including names, prices, ratings, reviews, and seller info — and export everything into a clean CSV file for analysis.

Tools Used:

Python

Selenium WebDriver

ChromeDriver

CSV for structured data output

Challenges I Solved:

✅ Dynamic Content Loading: Daraz uses lazy loading and JavaScript-rendered elements, so I implemented smart waits and scroll logic to ensure complete data capture.

✅ Pagination Handling: I automated navigation across multiple pages while keeping the scraping logic efficient and error-free.

✅ Bot Detection Avoidance: To mimic human behavior and avoid getting blocked, I added randomized delays and interaction patterns.

✅ Data Cleaning: I filtered out duplicates, handled missing fields, and ensured the final CSV was clean and ready for analysis.

Client Value: This kind of scraping can help businesses track competitor pricing, monitor product trends, or build their own internal product databases. I always focus on making the scraping logic scalable, reliable, and easy to adapt for future needs.