This project involves extracting data from GitHub repositories to analyze open-source trends and generate insights about repository popularity, programming language usage, and contributor activity.
Using Python and libraries such as BeautifulSoup, Requests, and Pandas, this project scrapes repository data from GitHub, processes it, and visualizes the results to identify trends in the open-source ecosystem.
The project aims to answer key questions:
- Which repositories are the most popular based on stars and forks?
- What are the trends in programming language usage?
- How do contributors engage with repositories?
- Programming Language: Python
- Web Scraping: BeautifulSoup, Requests
- Data Manipulation: Pandas, NumPy
- Visualization: Matplotlib, Seaborn
- Automation (Optional): Selenium
- Extracted data points:
- Repository Name
- Owner
- Stars
- Programming Language
- Description
- Used pagination to scrape multiple pages of repositories.
- Handled GitHub rate limits using headers and retry logic.
- Cleaned and structured the scraped data using
Pandas. - Analyzed data to identify popular repositories, language trends, and contribution patterns.
- Generated visualizations to present insights, including:
- Bar charts for programming language usage.
- Scatter plots for stars vs. forks.
- Heatmaps for contributor activity.
- Efficient web scraping for large datasets.
- Detailed data cleaning and preparation for analysis.
- Scalable design with modular functions for reuse.
- Meaningful visualizations for actionable insights.
- Rate Limiting: Implemented retry logic and exponential backoff.
- Dynamic Content: Used Selenium for JavaScript-rendered pages.
- Large Datasets: Optimized data storage and processing workflows.
- Integration with GitHub APIs for more reliable and faster data collection.
- Real-time dashboards using tools like Streamlit or Tableau.
- Extended analysis to repository performance over time.