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GithubSuccessPredictor

This repository consist of two main projects. 1- GithupAPi Fetcher 2- Github Success Predictor.

The Fetcher was constructed to use the api to gather data about multiple projects on github. We choose random projects based on their stars and pull requests. 1000 project's meta data for five languages where fetched and organized in a dataset file. This dataset file is freely available for research purpose here -> http://junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects

Then the dataset was used to create and test on mltiple machine learning algorithms. The best model with best recal and accuracy was selected and feed to the Github Success Predictor Project. This project uses the WEKA library and the model generated by the dataset we coleected to create a prediction model of github. This is able to take your repository meta deta and classify that into either successfull or un-successfull project. All these classification are ML Based and represent the latest trends.

Future Update: 1- Instead of filling in all the parameters, The software takes a link to your repository and fetches your projects meta deta and uses that to classify. Restriction (Will require internet connection)

About

This repository consist of two main projects. 1- GithupAPi Fetcher 2- Github Success Predictor.

junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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GithubSuccessPredictor
This repository consist of two main projects.
1- GithupAPi Fetcher
2- Github Success Predictor.
The Fetcher was constructed to use the api to gather data about multiple projects on github. We choose random projects based on their stars and pull requests. 1000 project's meta data for five languages where fetched and organized in a dataset file. This dataset file is freely available for research purpose here -> http://junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Then the dataset was used to create and test on mltiple machine learning algorithms. The best model with best recal and accuracy was selected and feed to the Github Success Predictor Project. This project uses the WEKA library and the model generated by the dataset we coleected to create a prediction model of github. This is able to take your repository meta deta and classify that into either successfull or un-successfull project. All these classification are ML Based and represent the latest trends.
Future Update:
1- Instead of filling in all the parameters, The software takes a link to your repository and fetches your projects meta deta and uses that to classify. Restriction (Will require internet connection)
About
This repository consist of two main projects. 1- GithupAPi Fetcher 2- Github Success Predictor.
junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Resources
Readme
GPL-3.0 license
Activity
Stars
2 stars
Watchers
2 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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GithubSuccessPredictor
This repository consist of two main projects.
1- GithupAPi Fetcher
2- Github Success Predictor.
The Fetcher was constructed to use the api to gather data about multiple projects on github. We choose random projects based on their stars and pull requests. 1000 project's meta data for five languages where fetched and organized in a dataset file. This dataset file is freely available for research purpose here -> http://junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Then the dataset was used to create and test on mltiple machine learning algorithms. The best model with best recal and accuracy was selected and feed to the Github Success Predictor Project. This project uses the WEKA library and the model generated by the dataset we coleected to create a prediction model of github. This is able to take your repository meta deta and classify that into either successfull or un-successfull project. All these classification are ML Based and represent the latest trends.
Future Update:
1- Instead of filling in all the parameters, The software takes a link to your repository and fetches your projects meta deta and uses that to classify. Restriction (Will require internet connection)
About
This repository consist of two main projects. 1- GithupAPi Fetcher 2- Github Success Predictor.
junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Resources
Readme
GPL-3.0 license
Activity
Stars
2 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
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Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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GithubSuccessPredictor
This repository consist of two main projects.
1- GithupAPi Fetcher
2- Github Success Predictor.
The Fetcher was constructed to use the api to gather data about multiple projects on github. We choose random projects based on their stars and pull requests. 1000 project's meta data for five languages where fetched and organized in a dataset file. This dataset file is freely available for research purpose here -> http://junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Then the dataset was used to create and test on mltiple machine learning algorithms. The best model with best recal and accuracy was selected and feed to the Github Success Predictor Project. This project uses the WEKA library and the model generated by the dataset we coleected to create a prediction model of github. This is able to take your repository meta deta and classify that into either successfull or un-successfull project. All these classification are ML Based and represent the latest trends.
Future Update:
1- Instead of filling in all the parameters, The software takes a link to your repository and fetches your projects meta deta and uses that to classify. Restriction (Will require internet connection)
About
This repository consist of two main projects. 1- GithupAPi Fetcher 2- Github Success Predictor.
junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Resources
Readme
GPL-3.0 license
Activity
Stars
2 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
GithubSuccessPredictor
This repository consist of two main projects.
1- GithupAPi Fetcher
2- Github Success Predictor.
The Fetcher was constructed to use the api to gather data about multiple projects on github. We choose random projects based on their stars and pull requests. 1000 project's meta data for five languages where fetched and organized in a dataset file. This dataset file is freely available for research purpose here -> http://junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Then the dataset was used to create and test on mltiple machine learning algorithms. The best model with best recal and accuracy was selected and feed to the Github Success Predictor Project. This project uses the WEKA library and the model generated by the dataset we coleected to create a prediction model of github. This is able to take your repository meta deta and classify that into either successfull or un-successfull project. All these classification are ML Based and represent the latest trends.
Future Update:
1- Instead of filling in all the parameters, The software takes a link to your repository and fetches your projects meta deta and uses that to classify. Restriction (Will require internet connection)
About
This repository consist of two main projects. 1- GithupAPi Fetcher 2- Github Success Predictor.
junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Resources
Readme
GPL-3.0 license
Activity
Stars
2 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
GithubSuccessPredictor
This repository consist of two main projects.
1- GithupAPi Fetcher
2- Github Success Predictor.
The Fetcher was constructed to use the api to gather data about multiple projects on github. We choose random projects based on their stars and pull requests. 1000 project's meta data for five languages where fetched and organized in a dataset file. This dataset file is freely available for research purpose here -> http://junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Then the dataset was used to create and test on mltiple machine learning algorithms. The best model with best recal and accuracy was selected and feed to the Github Success Predictor Project. This project uses the WEKA library and the model generated by the dataset we coleected to create a prediction model of github. This is able to take your repository meta deta and classify that into either successfull or un-successfull project. All these classification are ML Based and represent the latest trends.
Future Update:
1- Instead of filling in all the parameters, The software takes a link to your repository and fetches your projects meta deta and uses that to classify. Restriction (Will require internet connection)
About
This repository consist of two main projects. 1- GithupAPi Fetcher 2- Github Success Predictor.
junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Resources
Readme
GPL-3.0 license
Activity
Stars
2 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
GithubSuccessPredictor
This repository consist of two main projects.
1- GithupAPi Fetcher
2- Github Success Predictor.
The Fetcher was constructed to use the api to gather data about multiple projects on github. We choose random projects based on their stars and pull requests. 1000 project's meta data for five languages where fetched and organized in a dataset file. This dataset file is freely available for research purpose here -> http://junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Then the dataset was used to create and test on mltiple machine learning algorithms. The best model with best recal and accuracy was selected and feed to the Github Success Predictor Project. This project uses the WEKA library and the model generated by the dataset we coleected to create a prediction model of github. This is able to take your repository meta deta and classify that into either successfull or un-successfull project. All these classification are ML Based and represent the latest trends.
Future Update:
1- Instead of filling in all the parameters, The software takes a link to your repository and fetches your projects meta deta and uses that to classify. Restriction (Will require internet connection)
About
This repository consist of two main projects. 1- GithupAPi Fetcher 2- Github Success Predictor.
junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Resources
Readme
GPL-3.0 license
Activity
Stars
2 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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BranchesTags
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GithubSuccessPredictor
This repository consist of two main projects.
1- GithupAPi Fetcher
2- Github Success Predictor.
The Fetcher was constructed to use the api to gather data about multiple projects on github. We choose random projects based on their stars and pull requests. 1000 project's meta data for five languages where fetched and organized in a dataset file. This dataset file is freely available for research purpose here -> http://junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Then the dataset was used to create and test on mltiple machine learning algorithms. The best model with best recal and accuracy was selected and feed to the Github Success Predictor Project. This project uses the WEKA library and the model generated by the dataset we coleected to create a prediction model of github. This is able to take your repository meta deta and classify that into either successfull or un-successfull project. All these classification are ML Based and represent the latest trends.
Future Update:
1- Instead of filling in all the parameters, The software takes a link to your repository and fetches your projects meta deta and uses that to classify. Restriction (Will require internet connection)
About
This repository consist of two main projects. 1- GithupAPi Fetcher 2- Github Success Predictor.
junaidmaqsood.com/success-or-failure-identification-for-githubs-open-source-projects
Resources
Readme
GPL-3.0 license
Activity
Stars
2 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages