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Data Science Portfolio

Data Science portfolio of ipython notebooks implementing several Machine Learning algorithms following a structured, well-organized methodology to face each challenge:

[Data acquisition -> Data cleaning -> Data analysis -> Algorithm implementation -> Algorithm applied to dataset -> further optimization and advanced topics]

The notebooks cover a variety of topics and algorithms:

AlgorithmModelTopic
RecommenderMatrix Factorization - ALSLastFM music-user-artist data
RegressionRandom ForestsAirplane Delay
SimulationMonteCarlo in TimeSeriesFinantial Risk
ClusteringKMeansNetwork Traffic and Anomaly Detection
ClusteringKMeans in TimeSeriesTimeseries of NeuroImages

The last couple of notebooks belong to a Challenge by SAFRAN, two three-hour sessions that were part of their recruitment process. They served as the ultimate test to everything learnt beforehand, since no work was allowed out of the sessions.

Details

  • Language: Python over Jupyter Notebooks.
  • Execution: set over a remote Spark cluster in EURECOM, managed by Zoe
  • Libraries: numpy, pandas, matplotlib, pyspark, thunder

Authors

  • Ole Andreas Hansen @oleaha
  • Alberto Ibarrondo Luis @ibarrond

Sources and acknowledgments

The rough sketches of all the notebooks are the main focus of the course Algorithmic Machine Learning in EURECOM, and in particular Pietro Michiardi

The majority of the Notebooks are based on use cases illustrated in the book Advanced Analytics with Spark, by Sandy Ryza, Uri Laserson, Sean Owen & Josh Wills.

The Notebooks are based on publicly available data.

License

MIT Free software

About

Data Science portfolio of ipython Notebooks with several Machine Learning algorithms. They follow a structured data science methodology to face various challenges

Topics

data-sciencekmeans-clusteringmachine-learning-algorithmsmonte-carlo-simulationsrandom-forestrecommender-systemssafran

Resources

Readme
MIT license
Activity

Stars

8 stars

Watchers

2 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, '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" + '

Skip to content
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Data Science Portfolio
Data Science portfolio of ipython notebooks implementing several Machine Learning algorithms following a structured, well-organized methodology to face each challenge:
[Data acquisition -> Data cleaning -> Data analysis -> Algorithm implementation -> Algorithm applied to dataset -> further optimization and advanced topics]
The notebooks cover a variety of topics and algorithms:
AlgorithmModelTopic
RecommenderMatrix Factorization - ALSLastFM music-user-artist data
RegressionRandom ForestsAirplane Delay
SimulationMonteCarlo in TimeSeriesFinantial Risk
ClusteringKMeansNetwork Traffic and Anomaly Detection
ClusteringKMeans in TimeSeriesTimeseries of NeuroImages
The last couple of notebooks belong to a Challenge by SAFRAN, two three-hour sessions that were part of their recruitment process. They served as the ultimate test to everything learnt beforehand, since no work was allowed out of the sessions.
Details
  • Language: Python over Jupyter Notebooks.
  • Execution: set over a remote Spark cluster in EURECOM, managed by Zoe
  • Libraries: numpy, pandas, matplotlib, pyspark, thunder
Authors
  • Ole Andreas Hansen @oleaha
  • Alberto Ibarrondo Luis @ibarrond
Sources and acknowledgments
The rough sketches of all the notebooks are the main focus of the course Algorithmic Machine Learning in EURECOM, and in particular Pietro Michiardi
The majority of the Notebooks are based on use cases illustrated in the book Advanced Analytics with Spark, by Sandy Ryza, Uri Laserson, Sean Owen & Josh Wills.
The Notebooks are based on publicly available data.
License
MIT Free software
About
Data Science portfolio of ipython Notebooks with several Machine Learning algorithms. They follow a structured data science methodology to face various challenges
Topics
data-sciencekmeans-clusteringmachine-learning-algorithmsmonte-carlo-simulationsrandom-forestrecommender-systemssafran
Resources
Readme
MIT license
Activity
Stars
8 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Data Science Portfolio
Data Science portfolio of ipython notebooks implementing several Machine Learning algorithms following a structured, well-organized methodology to face each challenge:
[Data acquisition -> Data cleaning -> Data analysis -> Algorithm implementation -> Algorithm applied to dataset -> further optimization and advanced topics]
The notebooks cover a variety of topics and algorithms:
AlgorithmModelTopic
RecommenderMatrix Factorization - ALSLastFM music-user-artist data
RegressionRandom ForestsAirplane Delay
SimulationMonteCarlo in TimeSeriesFinantial Risk
ClusteringKMeansNetwork Traffic and Anomaly Detection
ClusteringKMeans in TimeSeriesTimeseries of NeuroImages
The last couple of notebooks belong to a Challenge by SAFRAN, two three-hour sessions that were part of their recruitment process. They served as the ultimate test to everything learnt beforehand, since no work was allowed out of the sessions.
Details
  • Language: Python over Jupyter Notebooks.
  • Execution: set over a remote Spark cluster in EURECOM, managed by Zoe
  • Libraries: numpy, pandas, matplotlib, pyspark, thunder
Authors
  • Ole Andreas Hansen @oleaha
  • Alberto Ibarrondo Luis @ibarrond
Sources and acknowledgments
The rough sketches of all the notebooks are the main focus of the course Algorithmic Machine Learning in EURECOM, and in particular Pietro Michiardi
The majority of the Notebooks are based on use cases illustrated in the book Advanced Analytics with Spark, by Sandy Ryza, Uri Laserson, Sean Owen & Josh Wills.
The Notebooks are based on publicly available data.
License
MIT Free software
About
Data Science portfolio of ipython Notebooks with several Machine Learning algorithms. They follow a structured data science methodology to face various challenges
Topics
data-sciencekmeans-clusteringmachine-learning-algorithmsmonte-carlo-simulationsrandom-forestrecommender-systemssafran
Resources
Readme
MIT license
Activity
Stars
8 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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
Repository files navigation
Data Science Portfolio
Data Science portfolio of ipython notebooks implementing several Machine Learning algorithms following a structured, well-organized methodology to face each challenge:
[Data acquisition -> Data cleaning -> Data analysis -> Algorithm implementation -> Algorithm applied to dataset -> further optimization and advanced topics]
The notebooks cover a variety of topics and algorithms:
AlgorithmModelTopic
RecommenderMatrix Factorization - ALSLastFM music-user-artist data
RegressionRandom ForestsAirplane Delay
SimulationMonteCarlo in TimeSeriesFinantial Risk
ClusteringKMeansNetwork Traffic and Anomaly Detection
ClusteringKMeans in TimeSeriesTimeseries of NeuroImages
The last couple of notebooks belong to a Challenge by SAFRAN, two three-hour sessions that were part of their recruitment process. They served as the ultimate test to everything learnt beforehand, since no work was allowed out of the sessions.
Details
  • Language: Python over Jupyter Notebooks.
  • Execution: set over a remote Spark cluster in EURECOM, managed by Zoe
  • Libraries: numpy, pandas, matplotlib, pyspark, thunder
Authors
  • Ole Andreas Hansen @oleaha
  • Alberto Ibarrondo Luis @ibarrond
Sources and acknowledgments
The rough sketches of all the notebooks are the main focus of the course Algorithmic Machine Learning in EURECOM, and in particular Pietro Michiardi
The majority of the Notebooks are based on use cases illustrated in the book Advanced Analytics with Spark, by Sandy Ryza, Uri Laserson, Sean Owen & Josh Wills.
The Notebooks are based on publicly available data.
License
MIT Free software
About
Data Science portfolio of ipython Notebooks with several Machine Learning algorithms. They follow a structured data science methodology to face various challenges
Topics
data-sciencekmeans-clusteringmachine-learning-algorithmsmonte-carlo-simulationsrandom-forestrecommender-systemssafran
Resources
Readme
MIT license
Activity
Stars
8 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
Data Science Portfolio
Data Science portfolio of ipython notebooks implementing several Machine Learning algorithms following a structured, well-organized methodology to face each challenge:
[Data acquisition -> Data cleaning -> Data analysis -> Algorithm implementation -> Algorithm applied to dataset -> further optimization and advanced topics]
The notebooks cover a variety of topics and algorithms:
AlgorithmModelTopic
RecommenderMatrix Factorization - ALSLastFM music-user-artist data
RegressionRandom ForestsAirplane Delay
SimulationMonteCarlo in TimeSeriesFinantial Risk
ClusteringKMeansNetwork Traffic and Anomaly Detection
ClusteringKMeans in TimeSeriesTimeseries of NeuroImages
The last couple of notebooks belong to a Challenge by SAFRAN, two three-hour sessions that were part of their recruitment process. They served as the ultimate test to everything learnt beforehand, since no work was allowed out of the sessions.
Details
  • Language: Python over Jupyter Notebooks.
  • Execution: set over a remote Spark cluster in EURECOM, managed by Zoe
  • Libraries: numpy, pandas, matplotlib, pyspark, thunder
Authors
  • Ole Andreas Hansen @oleaha
  • Alberto Ibarrondo Luis @ibarrond
Sources and acknowledgments
The rough sketches of all the notebooks are the main focus of the course Algorithmic Machine Learning in EURECOM, and in particular Pietro Michiardi
The majority of the Notebooks are based on use cases illustrated in the book Advanced Analytics with Spark, by Sandy Ryza, Uri Laserson, Sean Owen & Josh Wills.
The Notebooks are based on publicly available data.
License
MIT Free software
About
Data Science portfolio of ipython Notebooks with several Machine Learning algorithms. They follow a structured data science methodology to face various challenges
Topics
data-sciencekmeans-clusteringmachine-learning-algorithmsmonte-carlo-simulationsrandom-forestrecommender-systemssafran
Resources
Readme
MIT license
Activity
Stars
8 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
Data Science Portfolio
Data Science portfolio of ipython notebooks implementing several Machine Learning algorithms following a structured, well-organized methodology to face each challenge:
[Data acquisition -> Data cleaning -> Data analysis -> Algorithm implementation -> Algorithm applied to dataset -> further optimization and advanced topics]
The notebooks cover a variety of topics and algorithms:
AlgorithmModelTopic
RecommenderMatrix Factorization - ALSLastFM music-user-artist data
RegressionRandom ForestsAirplane Delay
SimulationMonteCarlo in TimeSeriesFinantial Risk
ClusteringKMeansNetwork Traffic and Anomaly Detection
ClusteringKMeans in TimeSeriesTimeseries of NeuroImages
The last couple of notebooks belong to a Challenge by SAFRAN, two three-hour sessions that were part of their recruitment process. They served as the ultimate test to everything learnt beforehand, since no work was allowed out of the sessions.
Details
  • Language: Python over Jupyter Notebooks.
  • Execution: set over a remote Spark cluster in EURECOM, managed by Zoe
  • Libraries: numpy, pandas, matplotlib, pyspark, thunder
Authors
  • Ole Andreas Hansen @oleaha
  • Alberto Ibarrondo Luis @ibarrond
Sources and acknowledgments
The rough sketches of all the notebooks are the main focus of the course Algorithmic Machine Learning in EURECOM, and in particular Pietro Michiardi
The majority of the Notebooks are based on use cases illustrated in the book Advanced Analytics with Spark, by Sandy Ryza, Uri Laserson, Sean Owen & Josh Wills.
The Notebooks are based on publicly available data.
License
MIT Free software
About
Data Science portfolio of ipython Notebooks with several Machine Learning algorithms. They follow a structured data science methodology to face various challenges
Topics
data-sciencekmeans-clusteringmachine-learning-algorithmsmonte-carlo-simulationsrandom-forestrecommender-systemssafran
Resources
Readme
MIT license
Activity
Stars
8 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
Data Science Portfolio
Data Science portfolio of ipython notebooks implementing several Machine Learning algorithms following a structured, well-organized methodology to face each challenge:
[Data acquisition -> Data cleaning -> Data analysis -> Algorithm implementation -> Algorithm applied to dataset -> further optimization and advanced topics]
The notebooks cover a variety of topics and algorithms:
AlgorithmModelTopic
RecommenderMatrix Factorization - ALSLastFM music-user-artist data
RegressionRandom ForestsAirplane Delay
SimulationMonteCarlo in TimeSeriesFinantial Risk
ClusteringKMeansNetwork Traffic and Anomaly Detection
ClusteringKMeans in TimeSeriesTimeseries of NeuroImages
The last couple of notebooks belong to a Challenge by SAFRAN, two three-hour sessions that were part of their recruitment process. They served as the ultimate test to everything learnt beforehand, since no work was allowed out of the sessions.
Details
  • Language: Python over Jupyter Notebooks.
  • Execution: set over a remote Spark cluster in EURECOM, managed by Zoe
  • Libraries: numpy, pandas, matplotlib, pyspark, thunder
Authors
  • Ole Andreas Hansen @oleaha
  • Alberto Ibarrondo Luis @ibarrond
Sources and acknowledgments
The rough sketches of all the notebooks are the main focus of the course Algorithmic Machine Learning in EURECOM, and in particular Pietro Michiardi
The majority of the Notebooks are based on use cases illustrated in the book Advanced Analytics with Spark, by Sandy Ryza, Uri Laserson, Sean Owen & Josh Wills.
The Notebooks are based on publicly available data.
License
MIT Free software
About
Data Science portfolio of ipython Notebooks with several Machine Learning algorithms. They follow a structured data science methodology to face various challenges
Topics
data-sciencekmeans-clusteringmachine-learning-algorithmsmonte-carlo-simulationsrandom-forestrecommender-systemssafran
Resources
Readme
MIT license
Activity
Stars
8 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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Data Science Portfolio
Data Science portfolio of ipython notebooks implementing several Machine Learning algorithms following a structured, well-organized methodology to face each challenge:
[Data acquisition -> Data cleaning -> Data analysis -> Algorithm implementation -> Algorithm applied to dataset -> further optimization and advanced topics]
The notebooks cover a variety of topics and algorithms:
AlgorithmModelTopic
RecommenderMatrix Factorization - ALSLastFM music-user-artist data
RegressionRandom ForestsAirplane Delay
SimulationMonteCarlo in TimeSeriesFinantial Risk
ClusteringKMeansNetwork Traffic and Anomaly Detection
ClusteringKMeans in TimeSeriesTimeseries of NeuroImages
The last couple of notebooks belong to a Challenge by SAFRAN, two three-hour sessions that were part of their recruitment process. They served as the ultimate test to everything learnt beforehand, since no work was allowed out of the sessions.
Details
  • Language: Python over Jupyter Notebooks.
  • Execution: set over a remote Spark cluster in EURECOM, managed by Zoe
  • Libraries: numpy, pandas, matplotlib, pyspark, thunder
Authors
  • Ole Andreas Hansen @oleaha
  • Alberto Ibarrondo Luis @ibarrond
Sources and acknowledgments
The rough sketches of all the notebooks are the main focus of the course Algorithmic Machine Learning in EURECOM, and in particular Pietro Michiardi
The majority of the Notebooks are based on use cases illustrated in the book Advanced Analytics with Spark, by Sandy Ryza, Uri Laserson, Sean Owen & Josh Wills.
The Notebooks are based on publicly available data.
License
MIT Free software
About
Data Science portfolio of ipython Notebooks with several Machine Learning algorithms. They follow a structured data science methodology to face various challenges
Topics
data-sciencekmeans-clusteringmachine-learning-algorithmsmonte-carlo-simulationsrandom-forestrecommender-systemssafran
Resources
Readme
MIT license
Activity
Stars
8 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages