---
pretty_name: GitHub Scikit-learn Issues Dataset
language:
- en
license: other
size_categories:
- n<1K
task_categories:
- text-classification
- sentence-similarity
tags:
- github
- scikit-learn
- issue-tracking
- software-engineering
- open-data
- text
---

# GitHub Scikit-learn Issues Dataset

## 1. Overview

This dataset contains 300 issues collected from the public GitHub repository
[scikit-learn/scikit-learn](https://github.com/scikit-learn/scikit-learn). It was created for an
AI/data assessment (Weeks 9-12) to demonstrate dataset creation, a Transformers pipeline and
FAISS semantic search, with documentation of sourcing, limitations and compliance.

- Dataset: https://huggingface.co/datasets/BeNatural/github-scikit-learn-issues-dataset
- Source: https://github.com/scikit-learn/scikit-learn/issues
- Format: Parquet, one `train` split, 300 rows, 14 columns

## 2. Why this dataset

GitHub issues are real, human-written technical text (bug reports, feature requests, documentation
problems) paired with structured metadata (labels, dates, state, comment counts). This makes them
suitable for text classification, sentiment/zero-shot analysis and semantic search for similar
issues, and the scikit-learn project is well known, openly developed and well documented.

## 3. Data collection

- Platform: GitHub (public issues of `scikit-learn/scikit-learn`)
- Collection method: issues were retrieved from the GitHub REST API (`https://api.github.com/repos/scikit-learn/scikit-learn/issues`) using Python and the `requests` library in a Google Colab notebook. Requests used `state=all`, 100 records per page, sorted by creation date in ascending order (oldest first), with a short pause between pages. Pages were collected until 300 genuine issues had been gathered, and the result was trimmed to exactly 300. The records were then assembled into a pandas DataFrame, saved as CSV and Parquet, and published to the Hugging Face Hub.
- Collection date: 12 October 2026. The collection notebook was re-run on 5 November 2026 and returned the same 300 issues and the same summary figures.
- Authentication: requests were made to the public GitHub API without a personal access token, within GitHub's public rate limits. No credentials were used or stored.
- Scope: the dataset contains issues only. The GitHub issues endpoint also returns pull requests, so any record containing a `pull_request` field was removed (about half of the early records returned were pull requests). No record in the dataset has a `/pull/` URL.
- Selection: because results were sorted oldest first, the 300 records are the **oldest** issues in the repository (issue numbers 1 to 768, created between 2010-08-31 and 2012-04-13). They are not a random sample.

## 4. Dataset structure

| Column | Type | Description |
|---|---|---|
| `issue_id` | int | GitHub's global issue ID |
| `issue_number` | int | Issue number within the repository |
| `repository` | string | `scikit-learn/scikit-learn` |
| `title` | string | Issue title |
| `body` | string | Issue description (9 records are empty) |
| `state` | string | `closed` (295) or `open` (5) |
| `labels` | string | Comma-separated GitHub labels (empty for 235 records) |
| `created_at` | datetime | When the issue was opened (UTC) |
| `closed_at` | datetime | When the issue was closed (empty for open issues) |
| `comments` | int | Number of comments on the issue (count only; comment text is not included) |
| `author` | string | Public GitHub username of the issue author |
| `html_url` | string | Link to the original issue |
| `text_length` | int | Derived field: length of the issue text (see Section 5) |
| `time_to_close_hours` | float | Derived field: hours between `created_at` and `closed_at` (empty for open issues) |

## 5. Processing and augmentation

- Raw issue records were assembled into a structured table (one row per issue), keeping 14 selected fields from the many returned by the API.
- Pull requests were excluded so that only issues remain.
- Text cleaning: leading and trailing whitespace was removed from titles and bodies, a missing body was stored as an empty text, and the list of labels was joined into one comma-separated text field.
- Duplicates: records were de-duplicated on `issue_id` and records with empty titles were removed. No records needed removing (all `issue_id`, `issue_number` and `html_url` values are unique).
- Validation: the notebook checks that issue IDs are unique, no title is empty, and the row count is in the expected range.
- Derived fields added: `text_length` and `time_to_close_hours`.
- Missing values were **kept, not imputed**: 9 empty bodies, 235 records without labels, and 5 open issues without `closed_at` / `time_to_close_hours`.

## 6. Initial data summary

| Item | Value |
|---|---|
| Rows / columns | 300 / 14 |
| Date range | 2010-08-31 to 2012-04-13 |
| State | 295 closed, 5 open |
| Issues with labels | 65 of 300 (22%) |
| Most common labels | New Feature (21), Enhancement (11), Bug (7) |
| Comments per issue | median 4, mean 7.2, max 59 |
| Time to close | median about 1,195 hours (about 50 days); highly skewed (max about 125,700 hours) |
| Distinct authors | 84; the top 3 (amueller, fabianp, mblondel) wrote 132 of the 300 issues |

## 7. Intended uses

1. **Transformers pipeline:** apply a pretrained model (e.g. sentiment or zero-shot classification) to titles/bodies and examine the outputs.
2. **Semantic search:** embed issue text and build a FAISS index to retrieve similar issues for natural-language queries.

Not intended for: profiling individual contributors, or drawing conclusions about the modern scikit-learn project.

## 8. Limitations and bias

- **Temporal bias:** all issues are from 2010-2012, the earliest days of the project. Topics, labels and writing style differ from current issues, and many early issues were written by a handful of core developers.
- **Small sample:** 300 rows, not representative of the repository's full history.
- **Author concentration:** a few authors account for a large share of records.
- **Sparse labels:** 78% of issues have no label, so label-based analysis covers a minority.
- **Short or empty text:** some bodies are empty or very short, which weakens classification and embedding quality.
- **Open vs closed:** only 5 issues are open, so resolution-time analysis is dominated by closed issues.
- **Model limitations:** general-purpose sentiment models often misread technical text; semantic search quality depends on the embedding model.
- **No comment text:** only the number of comments is included.

## 9. Ethics, legal and compliance

- **Privacy:** issues are public, but they include public GitHub usernames and can contain links or occasional email-like text. Usernames have been kept as they appear publicly on GitHub. The dataset is not used to identify or profile individuals.
- **Attribution:** content originates from the scikit-learn GitHub project and its contributors; every record links back to its original issue via `html_url`.
- **Licensing:** the scikit-learn software is BSD-3-Clause. This licence is for the software repository and is not assumed to cover user-written issue text, so this dataset is marked `license: other` and is shared for educational and research purposes only. Users should check GitHub's Terms of Service and the original project's terms before reuse.
- **Terms of service:** data was collected from publicly available content through the official GitHub API.
- **Organisational policy alignment:** the dataset was collected, stored and documented in line with the organisation's data storage, management and compliance policies provided for this assessment: public source data only, no private data, attribution to the original source, no credentials needed or stored, and versioned storage.
- **Security:** no API token or other credential was used, so none is stored in the notebooks or this repository.

## 10. Risks and corrective actions

| Risk | Mitigation |
|---|---|
| Personal data in public issue text | Keep only public handles; document handling; remove or anonymise `author` if required; do not target individuals |
| Temporal / sampling bias | Document the 2010-2012 window; in future, collect a random or time-stratified sample |
| Licence ambiguity of user content | Mark as `other`, link back to originals, educational use only |
| Missing labels / bodies | Keep missing values visible, report counts, avoid over-interpreting |
| Misleading model output | Manually review sample predictions and state model limits |
| Data loss | Dataset on the Hugging Face Hub with version history; notebooks and outputs backed up per policy |

## 11. Storage, versioning and reproducibility

- Published on the Hugging Face Hub (git-based, so every commit is versioned): https://huggingface.co/datasets/BeNatural/github-scikit-learn-issues-dataset
- Notebooks: three notebooks (data collection and augmentation, Transformers pipeline, FAISS semantic search) are submitted with this assessment as evidence.
- Local copy: the CSV and notebooks are kept as a backup copy alongside the project files.
- Version: v1.0, collected 12 October 2026 and re-verified on 5 November 2026. Any later changes to the data or processing should be recorded here.

## 12. References

- scikit-learn repository: https://github.com/scikit-learn/scikit-learn
- scikit-learn issues: https://github.com/scikit-learn/scikit-learn/issues
- GitHub REST API (issues): https://docs.github.com/en/rest/issues
- Hugging Face LLM Course, Creating your own dataset: https://huggingface.co/learn/llm-course/chapter5/5?fw=pt
- Hugging Face LLM Course, Behind the pipeline: https://huggingface.co/learn/llm-course/chapter2/2?fw=pt
- Hugging Face LLM Course, Semantic search with FAISS: https://huggingface.co/learn/llm-course/chapter5/6?fw=pt
- Datasheets for Datasets (Gebru et al.): https://arxiv.org/abs/1803.09010