Github Scraper: A tool for Examining the Machine Learning
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Github is one of the most popular version control systems in use today, with over 100 million projects available to users. Because of this, it is one of the best sources to check on the current state of Computer Science. My Github Analyzer application scrapes thousands of machine learning projects in order to determine which machine learning libraries are most commonly used, and analyze various statistics about machine learning projects as a whole.
The Python library Scrapy was used to get data from Github. The web scraping consisted of two spiders:
- GithubLinksSpider.py - Use search terms to get project links
- GithubProjectsSpider.py - Use project links to get data for each project
Each search term on Github allows the user to view 100 pages of information with 10 project links per page. I used 5 search terms classified by language to find projects:
- Jupyter Notebook
- All languages
Each search term produced 1000 links, although the final result produced around 4235 unique results. Getting project links was difficult due to Github servers timing out requests unless the rate of requests per second was low. Fortunately, the scraping for individual projects was much more forgiving in that regard.
The following data was obtained by analyzing the readme of each project and searching for references to each library, as well as their common aliases.
This plot shows the popularity of each library. It demonstrates that most libraries aren’t used much, and the top 5 libraries have by far the most usage.
These 2 plots demonstrate the number of total commits the scraped projects have for each library. They demonstrate that the libraries with the most commits are very different than their base popularity would suggest. The Histogram shows that most libraries have under 10000 commits, although there are significant outliers that may influence the results of the bar plot.
This plot demonstrates that the libraries whose projects have the most stars are Pyevolve and NuPIC, while the rest have very few stars.
This plot groups the projects by license. It reveals that the vast majority of projects have no license, although mit, apache, and gpl have a few uses.
This plot graphs the relationship between commits and releases. It shows that a lot of projects have 0 or 1 releases, but once the number of releases is greater than 1, there seems to be a positive correlation between commits and releases.
The common pattern in the data is that the vast majority of projects on Github are small and don’t have any significant number of commits, stars, or other indicators of influence. The same seems to be true for different libraries, which have a few that most people use, but the rest have very little use. As a whole, Github seems to have a few big projects that get most of the activity from users, and the rest are small and inconsequential.
Another feature I would add is to scrape the commit history for each project in order to see how machine learning projects have changed over time and which contributors are the most active.