Web Scraping: Real-World Data Extraction Across Industries
Sukhmandeep Singh
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Posted Sep 4, 2024
Each scraper was designed for scalability, ease of re-use and supported real-world decision-making in education, travel, local marketing, and e-commerce.
📝 Summary:
This project showcases my expertise in building robust web scraping pipelines tailored to different industries. Using tools like BeautifulSoup, Selenium, and requests, I extracted structured data from real-world websites for market analysis, lead generation, customer insight, and academic research.
🔍 Methodology:
Tools: Python, BeautifulSoup, Selenium, requests, pandas, re
Techniques: DOM parsing, dynamic content handling, pagination, anti-bot countermeasures
Output Formats: CSV, JSON
Challenges Solved: CAPTCHA handling, user-agent rotation, inconsistent HTML structures
🧩 Included Projects:
✈ British Airways Review Scraper (Skytrax)
Extracted user reviews, ratings, and travel classes.
Applied sentiment analysis-ready formatting.
Used for evaluating passenger satisfaction and service trends.
🎓 Talentedge Course Catalog Scraper
Scraped course titles, descriptions, durations, fees, and instructors.
Enabled course comparison for career planning or partnership evaluation.
📍 Google My Business Profile Scraper (GMB)
Collected business names, categories, ratings, addresses from Google Maps results.
Built for local SEO and B2B outreach automation.
Used Selenium with geolocation & scrolling logic.
🚗 Cars24 Used Car Scraper
Scraped vehicle models, pricing, mileage, registration year, and locations.
Enabled dataset generation for a car price prediction project.
📈 Outcome:
Each scraper was designed for scalability and ease of re-use. These scripts supported real-world decision-making in education, travel, local marketing, and e-commerce. Demonstrated my ability to build modular, ethical, and accurate data collection solutions for both research and business use cases.