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HelloShibani/README.md

👋 Hey! Nice to see you.

I'm [Shibani Roychoudhury] 😄

👩‍💻 Data Scientist specializing in NLP, Generative AI, and decision-support systems. 🧠 Passionate about building explainable, modular AI pipelines using LangChain, vector databases, and LLMs. 🚀 Former software engineer (15+ yrs) turned AI system designer—focusing on real-world ML applications in insurance, HR, and e-commerce. 📦 Projects include multi-agent RAG assistants, recommender systems with fallback logic, and Dockerized AI pipelines. 🔍 Always exploring the bridge between research and usable AI.

Currently looking for a internship / job 🔎 Email me

🔧 Tools: Python, SQL, LangChain, Hugging Face, ChromaDB, FastAPI
🧠 Focus: NLP, Generative AI, Recommender Systems, Explainable ML
🚀 What I build: Modular AI pipelines, document-grounded assistants, Dockerized ML systems

⚙️ Languages & Tools I Work With

📫 How to reach me:

Pinned Loading

  1. multi-strategy-recommendation-pipelinemulti-strategy-recommendation-pipelinePublic

    A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world …

    Jupyter Notebook 1 2

  2. Sentiment-Based-Product-Recommendation-Analysis-RevisionSentiment-Based-Product-Recommendation-Analysis-RevisionPublic

    Revised sentiment-based product recommendation analysis with improved features & model tuning.

    Jupyter Notebook 1

  3. HelpMate_AIHelpMate_AIPublic

    Retrieval-Augmented Question Answering system for complex insurance documents using Ollama, LangChain, and ChromaDB. Designed for scalable, intuitive document navigation and decision support.

    Jupyter Notebook

  4. Sentiment-Based-Product-Recommendation-AnalysisSentiment-Based-Product-Recommendation-AnalysisPublic

    Sentiment-based recommendation system leveraging NLP for personalized product suggestions.

    Jupyter Notebook

  5. Lead_Scoring_Case_StudyLead_Scoring_Case_StudyPublic

    Lead Scoring Case Study: Analyzing and prioritizing leads using data-driven techniques to enhance sales efficiency

    Jupyter Notebook

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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})();
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
HelloShibani (Shibani RoyChoudhury) · GitHub
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HelloShibani/README.md
👋 Hey! Nice to see you.
I'm [Shibani Roychoudhury] 😄
👩‍💻 Data Scientist specializing in NLP, Generative AI, and decision-support systems.
🧠 Passionate about building explainable, modular AI pipelines using LangChain, vector databases, and LLMs.
🚀 Former software engineer (15+ yrs) turned AI system designer—focusing on real-world ML applications in insurance, HR, and e-commerce.
📦 Projects include multi-agent RAG assistants, recommender systems with fallback logic, and Dockerized AI pipelines.
🔍 Always exploring the bridge between research and usable AI.
Currently looking for a internship / job 🔎 Email me
🔧 Tools: Python, SQL, LangChain, Hugging Face, ChromaDB, FastAPI

🧠 Focus: NLP, Generative AI, Recommender Systems, Explainable ML

🚀 What I build: Modular AI pipelines, document-grounded assistants, Dockerized ML systems
⚙️ Languages & Tools I Work With
📫 How to reach me:

Pinned
Loading
  1. multi-strategy-recommendation-pipelinemulti-strategy-recommendation-pipelinePublic
    
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world …

    Jupyter Notebook
1

2

  2. Sentiment-Based-Product-Recommendation-Analysis-RevisionSentiment-Based-Product-Recommendation-Analysis-RevisionPublic
    
Revised sentiment-based product recommendation analysis with improved features & model tuning.

    Jupyter Notebook
1

  3. HelpMate_AIHelpMate_AIPublic
    
Retrieval-Augmented Question Answering system for complex insurance documents using Ollama, LangChain, and ChromaDB. Designed for scalable, intuitive document navigation and decision support.

    Jupyter Notebook
  4. Sentiment-Based-Product-Recommendation-AnalysisSentiment-Based-Product-Recommendation-AnalysisPublic
    
Sentiment-based recommendation system leveraging NLP for personalized product suggestions.

    Jupyter Notebook
  5. Lead_Scoring_Case_StudyLead_Scoring_Case_StudyPublic
    
Lead Scoring Case Study: Analyzing and prioritizing leads using data-driven techniques to enhance sales efficiency

    Jupyter Notebook

, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
HelloShibani (Shibani RoyChoudhury) · GitHub
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1
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 · 1
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  • India
  • 
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Achievements
Achievement: Pull SharkAchievement: Quickdraw
Achievements
Achievement: Pull SharkAchievement: Quickdraw

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HelloShibani/README.md
👋 Hey! Nice to see you.
I'm [Shibani Roychoudhury] 😄
👩‍💻 Data Scientist specializing in NLP, Generative AI, and decision-support systems.
🧠 Passionate about building explainable, modular AI pipelines using LangChain, vector databases, and LLMs.
🚀 Former software engineer (15+ yrs) turned AI system designer—focusing on real-world ML applications in insurance, HR, and e-commerce.
📦 Projects include multi-agent RAG assistants, recommender systems with fallback logic, and Dockerized AI pipelines.
🔍 Always exploring the bridge between research and usable AI.
Currently looking for a internship / job 🔎 Email me
🔧 Tools: Python, SQL, LangChain, Hugging Face, ChromaDB, FastAPI

🧠 Focus: NLP, Generative AI, Recommender Systems, Explainable ML

🚀 What I build: Modular AI pipelines, document-grounded assistants, Dockerized ML systems
⚙️ Languages & Tools I Work With
📫 How to reach me:

Pinned
Loading
  1. multi-strategy-recommendation-pipelinemulti-strategy-recommendation-pipelinePublic
    
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world …

    Jupyter Notebook
1

2

  2. Sentiment-Based-Product-Recommendation-Analysis-RevisionSentiment-Based-Product-Recommendation-Analysis-RevisionPublic
    
Revised sentiment-based product recommendation analysis with improved features & model tuning.

    Jupyter Notebook
1

  3. HelpMate_AIHelpMate_AIPublic
    
Retrieval-Augmented Question Answering system for complex insurance documents using Ollama, LangChain, and ChromaDB. Designed for scalable, intuitive document navigation and decision support.

    Jupyter Notebook
  4. Sentiment-Based-Product-Recommendation-AnalysisSentiment-Based-Product-Recommendation-AnalysisPublic
    
Sentiment-based recommendation system leveraging NLP for personalized product suggestions.

    Jupyter Notebook
  5. Lead_Scoring_Case_StudyLead_Scoring_Case_StudyPublic
    
Lead Scoring Case Study: Analyzing and prioritizing leads using data-driven techniques to enhance sales efficiency

    Jupyter Notebook

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
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var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
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style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
HelloShibani (Shibani RoyChoudhury) · GitHub
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View HelloShibani's full-sized avatar
1
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 · 1
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  • India
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17:12
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Achievements
Achievement: Pull SharkAchievement: Quickdraw
Achievements
Achievement: Pull SharkAchievement: Quickdraw

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HelloShibani/README.md
👋 Hey! Nice to see you.
I'm [Shibani Roychoudhury] 😄
👩‍💻 Data Scientist specializing in NLP, Generative AI, and decision-support systems.
🧠 Passionate about building explainable, modular AI pipelines using LangChain, vector databases, and LLMs.
🚀 Former software engineer (15+ yrs) turned AI system designer—focusing on real-world ML applications in insurance, HR, and e-commerce.
📦 Projects include multi-agent RAG assistants, recommender systems with fallback logic, and Dockerized AI pipelines.
🔍 Always exploring the bridge between research and usable AI.
Currently looking for a internship / job 🔎 Email me
🔧 Tools: Python, SQL, LangChain, Hugging Face, ChromaDB, FastAPI

🧠 Focus: NLP, Generative AI, Recommender Systems, Explainable ML

🚀 What I build: Modular AI pipelines, document-grounded assistants, Dockerized ML systems
⚙️ Languages & Tools I Work With
📫 How to reach me:

Pinned
Loading
  1. multi-strategy-recommendation-pipelinemulti-strategy-recommendation-pipelinePublic
    
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world …

    Jupyter Notebook
1

2

  2. Sentiment-Based-Product-Recommendation-Analysis-RevisionSentiment-Based-Product-Recommendation-Analysis-RevisionPublic
    
Revised sentiment-based product recommendation analysis with improved features & model tuning.

    Jupyter Notebook
1

  3. HelpMate_AIHelpMate_AIPublic
    
Retrieval-Augmented Question Answering system for complex insurance documents using Ollama, LangChain, and ChromaDB. Designed for scalable, intuitive document navigation and decision support.

    Jupyter Notebook
  4. Sentiment-Based-Product-Recommendation-AnalysisSentiment-Based-Product-Recommendation-AnalysisPublic
    
Sentiment-based recommendation system leveraging NLP for personalized product suggestions.

    Jupyter Notebook
  5. Lead_Scoring_Case_StudyLead_Scoring_Case_StudyPublic
    
Lead Scoring Case Study: Analyzing and prioritizing leads using data-driven techniques to enhance sales efficiency

    Jupyter Notebook

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

  • India
  • 
17:12
(UTC -12:00)
Achievements
Achievement: Pull SharkAchievement: Quickdraw
Achievements
Achievement: Pull SharkAchievement: Quickdraw

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HelloShibani/README.md
👋 Hey! Nice to see you.
I'm [Shibani Roychoudhury] 😄
👩‍💻 Data Scientist specializing in NLP, Generative AI, and decision-support systems.
🧠 Passionate about building explainable, modular AI pipelines using LangChain, vector databases, and LLMs.
🚀 Former software engineer (15+ yrs) turned AI system designer—focusing on real-world ML applications in insurance, HR, and e-commerce.
📦 Projects include multi-agent RAG assistants, recommender systems with fallback logic, and Dockerized AI pipelines.
🔍 Always exploring the bridge between research and usable AI.
Currently looking for a internship / job 🔎 Email me
🔧 Tools: Python, SQL, LangChain, Hugging Face, ChromaDB, FastAPI

🧠 Focus: NLP, Generative AI, Recommender Systems, Explainable ML

🚀 What I build: Modular AI pipelines, document-grounded assistants, Dockerized ML systems
⚙️ Languages & Tools I Work With
📫 How to reach me:

Pinned
Loading
  1. multi-strategy-recommendation-pipelinemulti-strategy-recommendation-pipelinePublic
    
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world …

    Jupyter Notebook
1

2

  2. Sentiment-Based-Product-Recommendation-Analysis-RevisionSentiment-Based-Product-Recommendation-Analysis-RevisionPublic
    
Revised sentiment-based product recommendation analysis with improved features & model tuning.

    Jupyter Notebook
1

  3. HelpMate_AIHelpMate_AIPublic
    
Retrieval-Augmented Question Answering system for complex insurance documents using Ollama, LangChain, and ChromaDB. Designed for scalable, intuitive document navigation and decision support.

    Jupyter Notebook
  4. Sentiment-Based-Product-Recommendation-AnalysisSentiment-Based-Product-Recommendation-AnalysisPublic
    
Sentiment-based recommendation system leveraging NLP for personalized product suggestions.

    Jupyter Notebook
  5. Lead_Scoring_Case_StudyLead_Scoring_Case_StudyPublic
    
Lead Scoring Case Study: Analyzing and prioritizing leads using data-driven techniques to enhance sales efficiency

    Jupyter Notebook

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
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var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
HelloShibani (Shibani RoyChoudhury) · GitHub
Skip to content
View HelloShibani's full-sized avatar
1
follower
 · 1
following

  • India
  • 
17:12
(UTC -12:00)
Achievements
Achievement: Pull SharkAchievement: Quickdraw
Achievements
Achievement: Pull SharkAchievement: Quickdraw

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HelloShibani/README.md
👋 Hey! Nice to see you.
I'm [Shibani Roychoudhury] 😄
👩‍💻 Data Scientist specializing in NLP, Generative AI, and decision-support systems.
🧠 Passionate about building explainable, modular AI pipelines using LangChain, vector databases, and LLMs.
🚀 Former software engineer (15+ yrs) turned AI system designer—focusing on real-world ML applications in insurance, HR, and e-commerce.
📦 Projects include multi-agent RAG assistants, recommender systems with fallback logic, and Dockerized AI pipelines.
🔍 Always exploring the bridge between research and usable AI.
Currently looking for a internship / job 🔎 Email me
🔧 Tools: Python, SQL, LangChain, Hugging Face, ChromaDB, FastAPI

🧠 Focus: NLP, Generative AI, Recommender Systems, Explainable ML

🚀 What I build: Modular AI pipelines, document-grounded assistants, Dockerized ML systems
⚙️ Languages & Tools I Work With
📫 How to reach me:

Pinned
Loading
  1. multi-strategy-recommendation-pipelinemulti-strategy-recommendation-pipelinePublic
    
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world …

    Jupyter Notebook
1

2

  2. Sentiment-Based-Product-Recommendation-Analysis-RevisionSentiment-Based-Product-Recommendation-Analysis-RevisionPublic
    
Revised sentiment-based product recommendation analysis with improved features & model tuning.

    Jupyter Notebook
1

  3. HelpMate_AIHelpMate_AIPublic
    
Retrieval-Augmented Question Answering system for complex insurance documents using Ollama, LangChain, and ChromaDB. Designed for scalable, intuitive document navigation and decision support.

    Jupyter Notebook
  4. Sentiment-Based-Product-Recommendation-AnalysisSentiment-Based-Product-Recommendation-AnalysisPublic
    
Sentiment-based recommendation system leveraging NLP for personalized product suggestions.

    Jupyter Notebook
  5. Lead_Scoring_Case_StudyLead_Scoring_Case_StudyPublic
    
Lead Scoring Case Study: Analyzing and prioritizing leads using data-driven techniques to enhance sales efficiency

    Jupyter Notebook

, 'i');
if (__m === '*' || __re.test(location.href)) {
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document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
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observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
HelloShibani (Shibani RoyChoudhury) · GitHub
Skip to content
View HelloShibani's full-sized avatar
1
follower
 · 1
following

  • India
  • 
17:12
(UTC -12:00)
Achievements
Achievement: Pull SharkAchievement: Quickdraw
Achievements
Achievement: Pull SharkAchievement: Quickdraw

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HelloShibani/README.md
👋 Hey! Nice to see you.
I'm [Shibani Roychoudhury] 😄
👩‍💻 Data Scientist specializing in NLP, Generative AI, and decision-support systems.
🧠 Passionate about building explainable, modular AI pipelines using LangChain, vector databases, and LLMs.
🚀 Former software engineer (15+ yrs) turned AI system designer—focusing on real-world ML applications in insurance, HR, and e-commerce.
📦 Projects include multi-agent RAG assistants, recommender systems with fallback logic, and Dockerized AI pipelines.
🔍 Always exploring the bridge between research and usable AI.
Currently looking for a internship / job 🔎 Email me
🔧 Tools: Python, SQL, LangChain, Hugging Face, ChromaDB, FastAPI

🧠 Focus: NLP, Generative AI, Recommender Systems, Explainable ML

🚀 What I build: Modular AI pipelines, document-grounded assistants, Dockerized ML systems
⚙️ Languages & Tools I Work With
📫 How to reach me:

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  1. multi-strategy-recommendation-pipelinemulti-strategy-recommendation-pipelinePublic
    
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world …

    Jupyter Notebook
1

2

  2. Sentiment-Based-Product-Recommendation-Analysis-RevisionSentiment-Based-Product-Recommendation-Analysis-RevisionPublic
    
Revised sentiment-based product recommendation analysis with improved features & model tuning.

    Jupyter Notebook
1

  3. HelpMate_AIHelpMate_AIPublic
    
Retrieval-Augmented Question Answering system for complex insurance documents using Ollama, LangChain, and ChromaDB. Designed for scalable, intuitive document navigation and decision support.

    Jupyter Notebook
  4. Sentiment-Based-Product-Recommendation-AnalysisSentiment-Based-Product-Recommendation-AnalysisPublic
    
Sentiment-based recommendation system leveraging NLP for personalized product suggestions.

    Jupyter Notebook
  5. Lead_Scoring_Case_StudyLead_Scoring_Case_StudyPublic
    
Lead Scoring Case Study: Analyzing and prioritizing leads using data-driven techniques to enhance sales efficiency

    Jupyter Notebook

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filter: none !important;
}
/* Fix common UI elements */
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filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
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::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
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HelloShibani (Shibani RoyChoudhury) · GitHub
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HelloShibani/README.md
👋 Hey! Nice to see you.
I'm [Shibani Roychoudhury] 😄
👩‍💻 Data Scientist specializing in NLP, Generative AI, and decision-support systems.
🧠 Passionate about building explainable, modular AI pipelines using LangChain, vector databases, and LLMs.
🚀 Former software engineer (15+ yrs) turned AI system designer—focusing on real-world ML applications in insurance, HR, and e-commerce.
📦 Projects include multi-agent RAG assistants, recommender systems with fallback logic, and Dockerized AI pipelines.
🔍 Always exploring the bridge between research and usable AI.
Currently looking for a internship / job 🔎 Email me
🔧 Tools: Python, SQL, LangChain, Hugging Face, ChromaDB, FastAPI

🧠 Focus: NLP, Generative AI, Recommender Systems, Explainable ML

🚀 What I build: Modular AI pipelines, document-grounded assistants, Dockerized ML systems
⚙️ Languages & Tools I Work With
📫 How to reach me:

Pinned
Loading
  1. multi-strategy-recommendation-pipelinemulti-strategy-recommendation-pipelinePublic
    
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world …

    Jupyter Notebook
1

2

  2. Sentiment-Based-Product-Recommendation-Analysis-RevisionSentiment-Based-Product-Recommendation-Analysis-RevisionPublic
    
Revised sentiment-based product recommendation analysis with improved features & model tuning.

    Jupyter Notebook
1

  3. HelpMate_AIHelpMate_AIPublic
    
Retrieval-Augmented Question Answering system for complex insurance documents using Ollama, LangChain, and ChromaDB. Designed for scalable, intuitive document navigation and decision support.

    Jupyter Notebook
  4. Sentiment-Based-Product-Recommendation-AnalysisSentiment-Based-Product-Recommendation-AnalysisPublic
    
Sentiment-based recommendation system leveraging NLP for personalized product suggestions.

    Jupyter Notebook
  5. Lead_Scoring_Case_StudyLead_Scoring_Case_StudyPublic
    
Lead Scoring Case Study: Analyzing and prioritizing leads using data-driven techniques to enhance sales efficiency

    Jupyter Notebook