This repository contains a collection of projects focused on web scraping, extracting data from various websites for analysis, visualization, or further processing. Web scraping is an essential skill for gathering data from the web, and these projects demonstrate the use of Python libraries to efficiently collect and structure data for meaningful insights.
The goal of this repository is to provide a valuable resource for anyone interested in learning how to scrape, clean and analyze data from websites.
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Extracting data from static web pages
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Navigating and parsing HTML and XML structures
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Handling dynamic content with Beautiful Soup
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Managing request headers and delays to avoid scraping limits
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Data cleaning and formatting after extraction
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Saving data in various formats (CSV, JSON, etc.) for analysis
Beautiful Soup: For parsing HTML and extracting content
Requests: For sending HTTP requests to access web pages
Pandas: For organizing and cleaning scraped data
Selenium: (optional) For handling dynamic or JavaScript-heavy sites
NumPy: For numerical data handling in post-scraping tasks
Matplotlib & Seaborn: For visualizing trends in the scraped data
This repository serves as a practical guide for anyone looking to build web scraping skills using Python.