A curated list of awesome Machine Learning frameworks, libraries and software.
C-Rank Grade: C (Moderate) - 35/100
External PR Merge Rate: 18%
Response Time: 15d
First Timer Success: 27%
josephmisiti/awesome-machine-learning has a recorded first-timer success rate of 27.3%. Repositories ranked C typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in josephmisiti/awesome-machine-learning respond to incoming external pull requests in approximately 358.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
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 34.7 places josephmisiti/awesome-machine-learning in the C tier.
The external contributor pull request merge rate for josephmisiti/awesome-machine-learning is 18.1%, based on public PR activity from non-core contributors.
josephmisiti/awesome-machine-learning does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
Established Python ecosystem in josephmisiti/awesome-machine-learning. Maintains structured code review standards for external contributors.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/josephmisiti/awesome-machine-learning?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
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).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~233 hours (23% approval). Large PRs (>500 lines) take ~1718 hours (15% approval). Keep your diff compact for rapid review.
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No cached good first issues currently tracked for josephmisiti/awesome-machine-learning.
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When evaluating whether to contribute to josephmisiti/awesome-machine-learning, 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 josephmisiti/awesome-machine-learning acknowledge new external contributions in approximately 14 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 18.1% were successfully merged into the primary branch.
josephmisiti/awesome-machine-learning has a recorded first-timer success rate of 27.3%. Repositories ranked Moderate typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~14 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
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 34.7 places josephmisiti/awesome-machine-learning in the Moderate tier.
The external pull request merge rate is 18.1%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
josephmisiti/awesome-machine-learning does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.
All metrics displayed for josephmisiti/awesome-machine-learning 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.