What does the 8.0 C-Rank™ score (D Tier) represent?

The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 8.0 places eclarson/MachineLearningNotebooks in the D tier.

Are there Good First Issues available in eclarson/MachineLearningNotebooks?

eclarson/MachineLearningNotebooks does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.

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eclarson/MachineLearningNotebooks

Q: How fast can I expect code review feedback on my pull request?

Maintainers in eclarson/MachineLearningNotebooks respond to incoming external pull requests in approximately 0.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

Q: What is the external contributor pull request merge rate for eclarson/MachineLearningNotebooks?

The external contributor pull request merge rate for eclarson/MachineLearningNotebooks is 0.0%, based on public PR activity from non-core contributors.

170

D•Risky(8/100)JUJupyter Notebook

Open-source repository analyzed with precision C-Rank telemetry.

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AI Maintainer Review Guidelines

Review Persona

Empathetic Technical Mentor

Warmth Score

7.5/10

Patience Score

8.2/10

Nitpick Rate

20%

Collaborative maintainer environment in eclarson/MachineLearningNotebooks. Reviews community pull requests with focus on project quality.

What Contributors Actually Say

Discussions in eclarson/MachineLearningNotebooks focus heavily on practical implementation feedback, code formatting standards, and issue reproduction details.

Hidden Friction Signals

Unlinked PRs without issue context and changes that fail automated test suites face the highest review friction.

Unwritten Rules

•1) Keep PR scope strictly aligned with the linked issue.
•2) Ensure local linters pass before opening a review.
•3) Maintain full test coverage for modified logic.

Top PR Submission Do's

•Ensure code complies with the project coding style
•Keep PRs scoped to a single concern
•Include context and link to the related issue

Top PR Friction Pitfalls (Don'ts)

•Do not submit PRs without linking an issue
•Do not break existing tests without fixing them
•Do not mix unrelated refactors in a single PR

Response Velocity

No reviewer activity recorded

Average Response Latency

Tracks hours until a maintainer leaves a review, comment, or PR response.

Merge Efficiency

0.0%

Selective PR acceptance rate

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success

0.0%

No first-timer merges recorded in window

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers

CRITICAL BUS FACTOR

Single maintainer

No active GitHub maintainers found (may review internally on Gerrit or internal tools)

Diagnostic Health HUD

0.0%

Merge Gauge

0.0%

1st-Timer

Community Vibe0/100

Embed C-Rank Badge

Show contributors that your repository actively reviews and merges external pull requests.

[![GetMerged C-Rank](https://getmerged.abhishekco.de/api/badge/eclarson/MachineLearningNotebooks)](https://getmerged.abhishekco.de/eclarson/MachineLearningNotebooks?utm_source=github&utm_medium=badge)

PR Acceptance & Review Speed Forecast

Run Pre-flight PR CheckPrecomputed via Live PR Events

Overall Merge Odds

N/A

External PR acceptance

First-Timer Rate

N/A

First PR success rate

Median Response (p50)

N/A

50% receive response in

Tail Latency (p90)

N/A

90% receive response in

Active Maintainers: 0 High staleness risk (no recent merges)

Optimal PR Submission Window

Maintainer review activity heatmap & prime window prediction

Tuesday - ThursdayConfidence: NaN%

Peak Review Hour

14:00 UTC

Calculating local time...

Primary Window

14:00 - 18:00 UTC

Calculating local time...

Active Maintainers

Heuristic Estimate

Detected: UTC

24-Hour Maintainer Activity Heatmap (UTC)

LowPeak

00:0006:0012:0018:0023:00

Maintainers are most responsive around 14:00 UTC (14:00 UTC in your timezone). Recommended submission window is Tuesday - Thursday from 14:00 to 18:00 UTC (14:00 - 18:00 UTC local).

PR Size vs Review Speed

Best Pull Request Size for eclarson/MachineLearningNotebooks

Sweet Spot: < 150 lines

Small (<150 LOC)Recommended

16havg review

81% Merge Approval

Medium (150–500 LOC)Normal

1.5davg review

62% Merge Approval

Large (>500 LOC)Slow

4.8davg review

29% Merge Approval

Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~16 hours (81% approval). Large PRs (>500 lines) take ~115 hours (29% approval). Keep your diff compact for rapid review.

Interactive Test: Test your planned branch diff against eclarson/MachineLearningNotebooks's ~150 LOC sweet spot before opening a PR.

PR Merge Doctor · Pre-Submission Diff Check

Simulate Your PR Merge Speed

Enter your branch size and test coverage below to forecast review turnaround and get 1-click git splitting commands.

⚡ Sweet spot: ~150 LOC

Active Good First Issues (0)

View on GitHub

No cached good first issues currently tracked for eclarson/MachineLearningNotebooks.

View all good first issues directly on GitHub

Looking for more Jupyter Notebook beginner tasks?Explore Jupyter Notebook GFI

Contributor Community Vibe Feedback

Rate what actually matters after opening a pull request here.

Have you contributed to this repo?

Rate your first-hand PR experience (review speed, maintainer responsiveness, and onboarding ease) to help other contributors.

3 ratings required

Maintainer helpfulness

Review speed

Beginner friendliness

Contributor Compatibility & Review Speed Analysis for eclarson/MachineLearningNotebooks

When evaluating whether to contribute to eclarson/MachineLearningNotebooks, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.

Currently, maintainers of eclarson/MachineLearningNotebooks acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 0.0% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to eclarson/MachineLearningNotebooks

Is eclarson/MachineLearningNotebooks welcoming to first-time open-source contributors?

eclarson/MachineLearningNotebooks has a recorded first-timer success rate of 0.0%. Repositories ranked Risky typically provide actionable feedback during code reviews and actively nurture new community contributors.

How fast can I expect code review feedback on my pull request?

The initial maintainer response time averages ~-. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

What does the 8.0 C-Rank™ score represent?

The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 8.0 places eclarson/MachineLearningNotebooks in the Risky tier.

What is the external contributor pull request merge rate for eclarson/MachineLearningNotebooks?

The external pull request merge rate is 0.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.

Are there beginner Good First Issues open in eclarson/MachineLearningNotebooks?

eclarson/MachineLearningNotebooks does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for eclarson/MachineLearningNotebooks are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.