This project applies machine learning techniques in Python to analyze data, visualize feature relationships, and perform classification using models such as Decision Tree and Support Vector Machine. Model performance is evaluated through confusion matrices and graphical analysis.
C-Rank Grade: C (Moderate) - 36/100
External PR Merge Rate: 9%
Response Time: -
First Timer Success: -
fraisasghar/Machine-Learning-Classification-using-Python has a recorded first-timer success rate of 0.0%. Repositories ranked C typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in fraisasghar/Machine-Learning-Classification-using-Python 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 36.3 places fraisasghar/Machine-Learning-Classification-using-Python in the C tier.
The external contributor pull request merge rate for fraisasghar/Machine-Learning-Classification-using-Python is 9.1%, based on public PR activity from non-core contributors.
fraisasghar/Machine-Learning-Classification-using-Python does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
This project applies machine learning techniques in Python to analyze data, visualize feature relationships, and perform classification using models such as Decision Tree and Support Vector Machine. Model performance is evaluated through confusion matrices and graphical analysis.
Collaborative maintainer environment in fraisasghar/Machine-Learning-Classification-using-Python. Reviews community pull requests with focus on project quality.
Discussions in fraisasghar/Machine-Learning-Classification-using-Python focus heavily on practical implementation feedback, code formatting standards, and issue reproduction details.
Unlinked PRs without issue context and changes that fail automated test suites face the highest review friction.
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/fraisasghar/Machine-Learning-Classification-using-Python?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 20:00 UTC (20:00 UTC in your timezone). Recommended submission window is Monday - Wednesday from 03:00 to 07:00 UTC (03:00 - 07:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~16 hours (11% approval). Large PRs (>500 lines) take ~115 hours (15% approval). Keep your diff compact for rapid review.
No cached good first issues currently tracked for fraisasghar/Machine-Learning-Classification-using-Python.
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Rate your first-hand PR experience (review speed, maintainer responsiveness, and onboarding ease) to help other contributors.
When evaluating whether to contribute to fraisasghar/Machine-Learning-Classification-using-Python, 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 fraisasghar/Machine-Learning-Classification-using-Python acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 9.1% were successfully merged into the primary branch.
fraisasghar/Machine-Learning-Classification-using-Python has a recorded first-timer success rate of 0.0%. Repositories ranked Moderate typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~-. 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 36.3 places fraisasghar/Machine-Learning-Classification-using-Python in the Moderate tier.
The external pull request merge rate is 9.1%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
fraisasghar/Machine-Learning-Classification-using-Python 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 fraisasghar/Machine-Learning-Classification-using-Python 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.