To analyze open source projects and determine common features that influence the continued growth in contributions and popularity.
| Patrick Wilky | Joseph Wagner | Dustin Yang | Patrick Steveson |
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Why: What value can the consumer get from this?
- Gain insight to what makes a GitHub repository successful.
- Avoid the pitfalls of an unsuccessful GitHub repository.
- Monitor repo success metrics (whatever those are)
How do we know something is successful? How do we measure success?
- Funding, Contributors, Bug Fixes, Pull Requests
- General outreach
- marketing
- social media
- workshops
- newsletters
- user adoption
- Pandas - Data organization, wranging, cleaning
- tslearn - Calculate dtw values of each repo
- scipy - Run PCA and hierarchical clustering
- scikit-learn - Run clustering models and preprocessing
- numpy - Organize and clean data
- requests - Access APIs
- os - Access lists of repo files
- zipfile - Zip a file
- category_encoders - Encode categorical variables into ordinal variables
Initial Assumptions: Run hierarchcial clustering on Dynamic Time Warping values of the repo to check if there are unique clusters of successful and unsuccessful repo.
Results: Two main clusters: 1 cluster showed more activity then the other. This does not mean it is more successful than the other. More analysis required to confirm our assumptions.
Future Ideas:
- Run DTW on issue and pull request association and see if it'll generate different results when clustering
- Run DTW on sentiment to confirm if average sentiment per cluster to check if the results were meaningful
- Run DTW on different time periods and check if better clusters are formed
- Gather more data and run different models(Neural Networks or other unsupervised learning models)
- Investigate sub cluster within each cluster
- Run other NLP methods like: Topic Modeling - Context of the corpus(all comments in issues or pull request)
- Run NLP on tweets or reddit if the repo has one
- Binning by features(age or repo, stars, issues, commits, pull requests etc...) and run a cluster model
- Used unsupervised learning and did not have explanatory variables
- Used data from issues, pull request, stars, and commits to feed into our unsupervised clustering model
Dyanmic Time Warping and Hierarchical Clustering
Srape Github's Data using GraphQL
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