100 Days of ML Coding
C-Rank Grade: D (Risky) - 20/100
External PR Merge Rate: -
Response Time: <1h
First Timer Success: -
Avik-Jain/100-Days-Of-ML-Code has a recorded first-timer success rate of 0.0%. Repositories ranked D typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in Avik-Jain/100-Days-Of-ML-Code 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.
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 20.5 places Avik-Jain/100-Days-Of-ML-Code in the D tier.
The external contributor pull request merge rate for Avik-Jain/100-Days-Of-ML-Code is 0.0%, based on public PR activity from non-core contributors.
Avik-Jain/100-Days-Of-ML-Code does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
100 Days of ML Coding
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/Avik-Jain/100-Days-Of-ML-Code?utm_source=github&utm_medium=badge)
Enter your branch size and test coverage below to forecast review turnaround and get 1-click git splitting commands.
No cached good first issues currently tracked for Avik-Jain/100-Days-Of-ML-Code.
Rate what actually matters after opening a pull request here.
Rate your first-hand PR experience (review speed, maintainer responsiveness, and onboarding ease) to help other contributors.
When evaluating whether to contribute to Avik-Jain/100-Days-Of-ML-Code, 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 Avik-Jain/100-Days-Of-ML-Code acknowledge new external contributions in approximately <1 hour. 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.
Avik-Jain/100-Days-Of-ML-Code 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.
The initial maintainer response time averages ~<1 hour. 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 20.5 places Avik-Jain/100-Days-Of-ML-Code in the Risky tier.
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.
Avik-Jain/100-Days-Of-ML-Code 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 Avik-Jain/100-Days-Of-ML-Code 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.