Dimensionality reduction using PCA
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 19
- Forks
- 6
- PR merge metrics
- No merged PRs in 30d
Description
We currently have 115 attributes for every changeset. It is practically impossible how all these features look like. But, we could use techniques of Dimensionality reduction and project them into 2 dimensions.
We have powerful Principal component analysis (PCA) as part of `scikit-learn`:
- http://scikit-learn.org/stable/modules/decomposition.html
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by locating where the 115 changeset attributes are assembled and how the feature data is exposed to notebooks. Read the scikit-learn decomposition documentation, then determine how a two-dimensional PCA projection should be produced and presented. Done means the changesets can be projected into two dimensions, with a reproducible notebook or entry point showing the result.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, scikit-learn
- Domain
- data-visualization, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100