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Tech-Query-Recommendation-System

Introduction

  • A recommendation system is a subclass of Information filtering Systems that seeks to predict the rating or the preference a user might give to an item. In simple words, it is an algorithm that suggests relevant items to users.
  • The recommendation system deals with a large volume of information present by filtering the most important information based on the data provided by a user and other factors that take care of the user’s preference and interest. It finds out the match between user and item and imputes the similarities between users and items for recommendation.
  • During the last few decades, with the rise of Youtube, Amazon, Netflix and many other such web services, recommendation systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys.
  • Recommendation systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors.
  • The main aim is to build a project which can possibly provide answers for the technical queries and also suggests the user a few similar questions

Motivation

  • Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models. Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
  • Everyone is curious about what ,why and how? So this project is built to solve these queries related to technology which doesn't only provides the answers but also suggests similar types.Based on this we are going to build a technical query recommendation system.
  • This project is built using python language and it provides users the answers whenever they put a query related to technical questions.

Proposed Method

The proposed method for this project is to extract data by scraping from different sources and then pre-processing the extracted data so that it can be further worked upon.We then analyze and visualize the clean data to gain meaningful insights from it. We further use the insights gained to apply the relevant Machine Learning model to transform the dataset to give appropriate recommendations.

Methology

  • Data Collection: Using BeautifulSoup4 library we will scrape off the data from different sources.
  • Data Cleaning: Using pandas we will clean the extracted dataset before working upon it.
  • Data Analysis: Using pandas, Matplotlib and seaborn we perform data analysis and visualization
  • Model Building: Create a model using K-means clustering algorithm to recommend similar questions to the user
  • User-Interface: The Last step is to create a User-Interface to our model so that the users find it attractive and easy to use.

Plan of Work

  • Data Extraction: In this phase we would collect and extract data from the web resouces.
  • Data Pre-Processing: Here we will preprocess the data and clen for further analysis and model building.
  • Data Analysis: This phase includes analyzing the data using various methods to make it simpler and gain insights.
  • Data Visualization: In this the visualization of data would place using graphs and other techniques used for data visualization.
  • Applying Machine Learning Models: Here the models will be trained using training dataset to provide recommendations to the user.
  • Building a Dashboard : Here comes the phase where the web user interface will ask for the user ask query.
  • Testing : Finally the built model will be tested using test dataset.

Minor Presentation

SRS

Synposis

Models

Extracted Dataset

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

1 star

Watchers

1 watching

Forks

1 fork
Report repository

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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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Tech-Query-Recommendation-System
Introduction
  • A recommendation system is a subclass of Information filtering Systems that seeks to predict the rating or the preference a user might give to an item. In simple words, it is an algorithm that suggests relevant items to users.
  • The recommendation system deals with a large volume of information present by filtering the most important information based on the data provided by a user and other factors that take care of the user’s preference and interest. It finds out the match between user and item and imputes the similarities between users and items for recommendation.
  • During the last few decades, with the rise of Youtube, Amazon, Netflix and many other such web services, recommendation systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys.
  • Recommendation systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors.
  • The main aim is to build a project which can possibly provide answers for the technical queries and also suggests the user a few similar questions
Motivation
  • Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
  • Everyone is curious about what ,why and how? So this project is built to solve these queries related to technology which doesn't only provides the answers but also suggests similar types.Based on this we are going to build a technical query recommendation system.
  • This project is built using python language and it provides users the answers whenever they put a query related to technical questions.
Proposed Method
The proposed method for this project is to extract data by scraping from different sources and then pre-processing the extracted data so that it can be further worked upon.We then analyze and visualize the clean data to gain meaningful insights from it. We further use the insights gained to apply the relevant Machine Learning model to transform the dataset to give appropriate recommendations.
Methology
  • Data Collection: Using BeautifulSoup4 library we will scrape off the data from different sources.
  • Data Cleaning: Using pandas we will clean the extracted dataset before working upon it.
  • Data Analysis: Using pandas, Matplotlib and seaborn we perform data analysis and visualization
  • Model Building: Create a model using K-means clustering algorithm to recommend similar questions to the user
  • User-Interface: The Last step is to create a User-Interface to our model so that the users find it attractive and easy to use.
Plan of Work
  • Data Extraction: In this phase we would collect and extract data from the web resouces.
  • Data Pre-Processing: Here we will preprocess the data and clen for further analysis and model building.
  • Data Analysis: This phase includes analyzing the data using various methods to make it simpler and gain insights.
  • Data Visualization: In this the visualization of data would place using graphs and other techniques used for data visualization.
  • Applying Machine Learning Models: Here the models will be trained using training dataset to provide recommendations to the user.
  • Building a Dashboard : Here comes the phase where the web user interface will ask for the user ask query.
  • Testing : Finally the built model will be tested using test dataset.
Minor Presentation
SRS
Synposis
Models
Extracted Dataset
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
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1 fork
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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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BranchesTags
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Tech-Query-Recommendation-System
Introduction
  • A recommendation system is a subclass of Information filtering Systems that seeks to predict the rating or the preference a user might give to an item. In simple words, it is an algorithm that suggests relevant items to users.
  • The recommendation system deals with a large volume of information present by filtering the most important information based on the data provided by a user and other factors that take care of the user’s preference and interest. It finds out the match between user and item and imputes the similarities between users and items for recommendation.
  • During the last few decades, with the rise of Youtube, Amazon, Netflix and many other such web services, recommendation systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys.
  • Recommendation systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors.
  • The main aim is to build a project which can possibly provide answers for the technical queries and also suggests the user a few similar questions
Motivation
  • Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
  • Everyone is curious about what ,why and how? So this project is built to solve these queries related to technology which doesn't only provides the answers but also suggests similar types.Based on this we are going to build a technical query recommendation system.
  • This project is built using python language and it provides users the answers whenever they put a query related to technical questions.
Proposed Method
The proposed method for this project is to extract data by scraping from different sources and then pre-processing the extracted data so that it can be further worked upon.We then analyze and visualize the clean data to gain meaningful insights from it. We further use the insights gained to apply the relevant Machine Learning model to transform the dataset to give appropriate recommendations.
Methology
  • Data Collection: Using BeautifulSoup4 library we will scrape off the data from different sources.
  • Data Cleaning: Using pandas we will clean the extracted dataset before working upon it.
  • Data Analysis: Using pandas, Matplotlib and seaborn we perform data analysis and visualization
  • Model Building: Create a model using K-means clustering algorithm to recommend similar questions to the user
  • User-Interface: The Last step is to create a User-Interface to our model so that the users find it attractive and easy to use.
Plan of Work
  • Data Extraction: In this phase we would collect and extract data from the web resouces.
  • Data Pre-Processing: Here we will preprocess the data and clen for further analysis and model building.
  • Data Analysis: This phase includes analyzing the data using various methods to make it simpler and gain insights.
  • Data Visualization: In this the visualization of data would place using graphs and other techniques used for data visualization.
  • Applying Machine Learning Models: Here the models will be trained using training dataset to provide recommendations to the user.
  • Building a Dashboard : Here comes the phase where the web user interface will ask for the user ask query.
  • Testing : Finally the built model will be tested using test dataset.
Minor Presentation
SRS
Synposis
Models
Extracted Dataset
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
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('^' + ".*" + '

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Tech-Query-Recommendation-System
Introduction
  • A recommendation system is a subclass of Information filtering Systems that seeks to predict the rating or the preference a user might give to an item. In simple words, it is an algorithm that suggests relevant items to users.
  • The recommendation system deals with a large volume of information present by filtering the most important information based on the data provided by a user and other factors that take care of the user’s preference and interest. It finds out the match between user and item and imputes the similarities between users and items for recommendation.
  • During the last few decades, with the rise of Youtube, Amazon, Netflix and many other such web services, recommendation systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys.
  • Recommendation systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors.
  • The main aim is to build a project which can possibly provide answers for the technical queries and also suggests the user a few similar questions
Motivation
  • Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
  • Everyone is curious about what ,why and how? So this project is built to solve these queries related to technology which doesn't only provides the answers but also suggests similar types.Based on this we are going to build a technical query recommendation system.
  • This project is built using python language and it provides users the answers whenever they put a query related to technical questions.
Proposed Method
The proposed method for this project is to extract data by scraping from different sources and then pre-processing the extracted data so that it can be further worked upon.We then analyze and visualize the clean data to gain meaningful insights from it. We further use the insights gained to apply the relevant Machine Learning model to transform the dataset to give appropriate recommendations.
Methology
  • Data Collection: Using BeautifulSoup4 library we will scrape off the data from different sources.
  • Data Cleaning: Using pandas we will clean the extracted dataset before working upon it.
  • Data Analysis: Using pandas, Matplotlib and seaborn we perform data analysis and visualization
  • Model Building: Create a model using K-means clustering algorithm to recommend similar questions to the user
  • User-Interface: The Last step is to create a User-Interface to our model so that the users find it attractive and easy to use.
Plan of Work
  • Data Extraction: In this phase we would collect and extract data from the web resouces.
  • Data Pre-Processing: Here we will preprocess the data and clen for further analysis and model building.
  • Data Analysis: This phase includes analyzing the data using various methods to make it simpler and gain insights.
  • Data Visualization: In this the visualization of data would place using graphs and other techniques used for data visualization.
  • Applying Machine Learning Models: Here the models will be trained using training dataset to provide recommendations to the user.
  • Building a Dashboard : Here comes the phase where the web user interface will ask for the user ask query.
  • Testing : Finally the built model will be tested using test dataset.
Minor Presentation
SRS
Synposis
Models
Extracted Dataset
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
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, '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" + '

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Tech-Query-Recommendation-System
Introduction
  • A recommendation system is a subclass of Information filtering Systems that seeks to predict the rating or the preference a user might give to an item. In simple words, it is an algorithm that suggests relevant items to users.
  • The recommendation system deals with a large volume of information present by filtering the most important information based on the data provided by a user and other factors that take care of the user’s preference and interest. It finds out the match between user and item and imputes the similarities between users and items for recommendation.
  • During the last few decades, with the rise of Youtube, Amazon, Netflix and many other such web services, recommendation systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys.
  • Recommendation systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors.
  • The main aim is to build a project which can possibly provide answers for the technical queries and also suggests the user a few similar questions
Motivation
  • Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
  • Everyone is curious about what ,why and how? So this project is built to solve these queries related to technology which doesn't only provides the answers but also suggests similar types.Based on this we are going to build a technical query recommendation system.
  • This project is built using python language and it provides users the answers whenever they put a query related to technical questions.
Proposed Method
The proposed method for this project is to extract data by scraping from different sources and then pre-processing the extracted data so that it can be further worked upon.We then analyze and visualize the clean data to gain meaningful insights from it. We further use the insights gained to apply the relevant Machine Learning model to transform the dataset to give appropriate recommendations.
Methology
  • Data Collection: Using BeautifulSoup4 library we will scrape off the data from different sources.
  • Data Cleaning: Using pandas we will clean the extracted dataset before working upon it.
  • Data Analysis: Using pandas, Matplotlib and seaborn we perform data analysis and visualization
  • Model Building: Create a model using K-means clustering algorithm to recommend similar questions to the user
  • User-Interface: The Last step is to create a User-Interface to our model so that the users find it attractive and easy to use.
Plan of Work
  • Data Extraction: In this phase we would collect and extract data from the web resouces.
  • Data Pre-Processing: Here we will preprocess the data and clen for further analysis and model building.
  • Data Analysis: This phase includes analyzing the data using various methods to make it simpler and gain insights.
  • Data Visualization: In this the visualization of data would place using graphs and other techniques used for data visualization.
  • Applying Machine Learning Models: Here the models will be trained using training dataset to provide recommendations to the user.
  • Building a Dashboard : Here comes the phase where the web user interface will ask for the user ask query.
  • Testing : Finally the built model will be tested using test dataset.
Minor Presentation
SRS
Synposis
Models
Extracted Dataset
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
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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('^' + ".*" + '

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Tech-Query-Recommendation-System
Introduction
  • A recommendation system is a subclass of Information filtering Systems that seeks to predict the rating or the preference a user might give to an item. In simple words, it is an algorithm that suggests relevant items to users.
  • The recommendation system deals with a large volume of information present by filtering the most important information based on the data provided by a user and other factors that take care of the user’s preference and interest. It finds out the match between user and item and imputes the similarities between users and items for recommendation.
  • During the last few decades, with the rise of Youtube, Amazon, Netflix and many other such web services, recommendation systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys.
  • Recommendation systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors.
  • The main aim is to build a project which can possibly provide answers for the technical queries and also suggests the user a few similar questions
Motivation
  • Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
  • Everyone is curious about what ,why and how? So this project is built to solve these queries related to technology which doesn't only provides the answers but also suggests similar types.Based on this we are going to build a technical query recommendation system.
  • This project is built using python language and it provides users the answers whenever they put a query related to technical questions.
Proposed Method
The proposed method for this project is to extract data by scraping from different sources and then pre-processing the extracted data so that it can be further worked upon.We then analyze and visualize the clean data to gain meaningful insights from it. We further use the insights gained to apply the relevant Machine Learning model to transform the dataset to give appropriate recommendations.
Methology
  • Data Collection: Using BeautifulSoup4 library we will scrape off the data from different sources.
  • Data Cleaning: Using pandas we will clean the extracted dataset before working upon it.
  • Data Analysis: Using pandas, Matplotlib and seaborn we perform data analysis and visualization
  • Model Building: Create a model using K-means clustering algorithm to recommend similar questions to the user
  • User-Interface: The Last step is to create a User-Interface to our model so that the users find it attractive and easy to use.
Plan of Work
  • Data Extraction: In this phase we would collect and extract data from the web resouces.
  • Data Pre-Processing: Here we will preprocess the data and clen for further analysis and model building.
  • Data Analysis: This phase includes analyzing the data using various methods to make it simpler and gain insights.
  • Data Visualization: In this the visualization of data would place using graphs and other techniques used for data visualization.
  • Applying Machine Learning Models: Here the models will be trained using training dataset to provide recommendations to the user.
  • Building a Dashboard : Here comes the phase where the web user interface will ask for the user ask query.
  • Testing : Finally the built model will be tested using test dataset.
Minor Presentation
SRS
Synposis
Models
Extracted Dataset
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
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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('^' + ".*" + '

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Tech-Query-Recommendation-System
Introduction
  • A recommendation system is a subclass of Information filtering Systems that seeks to predict the rating or the preference a user might give to an item. In simple words, it is an algorithm that suggests relevant items to users.
  • The recommendation system deals with a large volume of information present by filtering the most important information based on the data provided by a user and other factors that take care of the user’s preference and interest. It finds out the match between user and item and imputes the similarities between users and items for recommendation.
  • During the last few decades, with the rise of Youtube, Amazon, Netflix and many other such web services, recommendation systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys.
  • Recommendation systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors.
  • The main aim is to build a project which can possibly provide answers for the technical queries and also suggests the user a few similar questions
Motivation
  • Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
  • Everyone is curious about what ,why and how? So this project is built to solve these queries related to technology which doesn't only provides the answers but also suggests similar types.Based on this we are going to build a technical query recommendation system.
  • This project is built using python language and it provides users the answers whenever they put a query related to technical questions.
Proposed Method
The proposed method for this project is to extract data by scraping from different sources and then pre-processing the extracted data so that it can be further worked upon.We then analyze and visualize the clean data to gain meaningful insights from it. We further use the insights gained to apply the relevant Machine Learning model to transform the dataset to give appropriate recommendations.
Methology
  • Data Collection: Using BeautifulSoup4 library we will scrape off the data from different sources.
  • Data Cleaning: Using pandas we will clean the extracted dataset before working upon it.
  • Data Analysis: Using pandas, Matplotlib and seaborn we perform data analysis and visualization
  • Model Building: Create a model using K-means clustering algorithm to recommend similar questions to the user
  • User-Interface: The Last step is to create a User-Interface to our model so that the users find it attractive and easy to use.
Plan of Work
  • Data Extraction: In this phase we would collect and extract data from the web resouces.
  • Data Pre-Processing: Here we will preprocess the data and clen for further analysis and model building.
  • Data Analysis: This phase includes analyzing the data using various methods to make it simpler and gain insights.
  • Data Visualization: In this the visualization of data would place using graphs and other techniques used for data visualization.
  • Applying Machine Learning Models: Here the models will be trained using training dataset to provide recommendations to the user.
  • Building a Dashboard : Here comes the phase where the web user interface will ask for the user ask query.
  • Testing : Finally the built model will be tested using test dataset.
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Models
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About
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, '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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Tech-Query-Recommendation-System
Introduction
  • A recommendation system is a subclass of Information filtering Systems that seeks to predict the rating or the preference a user might give to an item. In simple words, it is an algorithm that suggests relevant items to users.
  • The recommendation system deals with a large volume of information present by filtering the most important information based on the data provided by a user and other factors that take care of the user’s preference and interest. It finds out the match between user and item and imputes the similarities between users and items for recommendation.
  • During the last few decades, with the rise of Youtube, Amazon, Netflix and many other such web services, recommendation systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys.
  • Recommendation systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors.
  • The main aim is to build a project which can possibly provide answers for the technical queries and also suggests the user a few similar questions
Motivation
  • Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
  • Everyone is curious about what ,why and how? So this project is built to solve these queries related to technology which doesn't only provides the answers but also suggests similar types.Based on this we are going to build a technical query recommendation system.
  • This project is built using python language and it provides users the answers whenever they put a query related to technical questions.
Proposed Method
The proposed method for this project is to extract data by scraping from different sources and then pre-processing the extracted data so that it can be further worked upon.We then analyze and visualize the clean data to gain meaningful insights from it. We further use the insights gained to apply the relevant Machine Learning model to transform the dataset to give appropriate recommendations.
Methology
  • Data Collection: Using BeautifulSoup4 library we will scrape off the data from different sources.
  • Data Cleaning: Using pandas we will clean the extracted dataset before working upon it.
  • Data Analysis: Using pandas, Matplotlib and seaborn we perform data analysis and visualization
  • Model Building: Create a model using K-means clustering algorithm to recommend similar questions to the user
  • User-Interface: The Last step is to create a User-Interface to our model so that the users find it attractive and easy to use.
Plan of Work
  • Data Extraction: In this phase we would collect and extract data from the web resouces.
  • Data Pre-Processing: Here we will preprocess the data and clen for further analysis and model building.
  • Data Analysis: This phase includes analyzing the data using various methods to make it simpler and gain insights.
  • Data Visualization: In this the visualization of data would place using graphs and other techniques used for data visualization.
  • Applying Machine Learning Models: Here the models will be trained using training dataset to provide recommendations to the user.
  • Building a Dashboard : Here comes the phase where the web user interface will ask for the user ask query.
  • Testing : Finally the built model will be tested using test dataset.
Minor Presentation
SRS
Synposis
Models
Extracted Dataset
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages