This repository contains a Jupyter notebook (Linkedin Webscraping.ipynb) that provides a comprehensive guide to scraping LinkedIn profiles using Python. The notebook is designed for educational purposes to demonstrate how to legally and ethically extract professional data from LinkedIn.
The Linkedin Webscraping.ipynb notebook outlines methods for connecting to LinkedIn, navigating through user profiles, and extracting relevant data points without violating LinkedIn's terms of service. The primary focus is on using Python libraries to automate the collection of publicly available information which can be used for academic research, market analysis, or professional networking enhancements.
- Connection Setup: How to set up a connection to LinkedIn using session handling in
requestsorseleniumfor managing login sessions. - Profile Navigation: Demonstrates the steps to navigate from a LinkedIn user's initial profile page to other sections like experience, education, and skills using DOM parsing.
- Data Extraction: Detailed code examples for extracting specific data like names, job titles, educational backgrounds, skills, endorsements, and more.
- Data Storage: Guidelines on storing scraped data in a structured format such as CSV or a SQL database, ensuring data integrity and ease of access.
- Ethical Considerations: Discussion on the ethical implications of web scraping and how to ensure your scraping activities comply with legal standards and LinkedIn's robots.txt and terms of service.