Open To: Data Analyst • Data Scientist • Machine Learning Intern roles, freelance analytics projects, and collaboration on open-source data projects.
I'm a data-driven problem solver with a strong foundation in Python, SQL, and data analysis, focused on turning messy, real-world data into decisions people can act on.
- 🔍 Hands-on experience with EDA, dashboards, and data visualization
- 🤖 Practical exposure to Machine Learning — Regression, Classification, Clustering
- 🧠 Foundational knowledge of Deep Learning — Neural Networks, CNNs
- 📊 Comfortable building end-to-end analysis, from raw data to a shareable dashboard
- 🎯 Actively preparing for Data Analyst / Data Scientist roles
Languages
Machine Learning Libraries
Deep Learning Libraries
Data Analysis & Manipulation
Databases
Data Visualization & BI
Tools & Platforms
Cloud & MLOps
| Domain | Proficiency | Details |
|---|---|---|
| Machine Learning | 🟣🟣🟣🟣🟣 | Regression, Classification, Clustering, Feature Engineering, Model Evaluation |
| Deep Learning | 🟣🟣🟣🟣⚪ | Neural Networks, CNN fundamentals |
| Exploratory Data Analysis | 🟣🟣🟣🟣🟣 | Data cleaning, feature analysis, visual storytelling |
| SQL & Data Querying | 🟣🟣🟣🟣🟣 | Advanced querying, joins, aggregation |
| Natural Language Processing | 🟣🟣🟣🟣⚪ | Text pipelines, embeddings, semantic understanding |
| RAG (Retrieval-Augmented Generation) | 🟣🟣🟣🟣⚪ | Document embeddings, semantic search, context retrieval |
| LangChain | 🟣🟣🟣⚪⚪ | Building LLM-powered applications & pipelines |
| LangGraph | 🟣🟣🟣⚪⚪ | Multi-step agentic workflow design |
🔹 Streamlit Dashboard
📊 Interactive dashboard built using Python & Streamlit for exploring and visualizing data in real time.
| Stack | Highlights |
|---|---|
| Python, Streamlit, Pandas | Data cleaning & preprocessing pipeline |
| Matplotlib / Seaborn | User-friendly, interactive UI for insights |
| — | Designed for fast, at-a-glance data exploration |
🔗 Repository: streamlit-dashboard
🔹 Smartphone Data Analysis
📱 Exploratory Data Analysis (EDA) project uncovering patterns and pricing insights across smartphone specifications.
| Stack | Highlights |
|---|---|
| Python, Pandas, NumPy | Data cleaning & feature analysis |
| Matplotlib, Seaborn | Visual insights and correlation analysis |
| — | End-to-end EDA workflow, from raw CSV to insight |
🔗 Repository: smartphone-data-analysis
🔹 Personal Portfolio Website
🌐 A 5-page personal portfolio built to present role-tailored resumes, technical writing, and project work to recruiters across Data Scientist, Data Analyst, ML Engineer, and GenAI Engineer roles.
| Stack | Highlights |
|---|---|
| React, Design System (Poppins/Inter) | Home, Resume, Blogs, Projects, Contact pages on one consistent design system |
| Role-tailored Resume tabs | 4 pill-tab resume versions (Data Scientist / Data Analyst / ML Engineer / GenAI Engineer) with dynamic summary & skills |
| Formspree (no-backend contact form) | WhatsApp, email, and social quick-links; responsive across mobile/tablet/desktop |
🔗 Repository: add repo link here • 🔗 Live Site: add deployed URL here
🔹 RAG Customer Support Assistant
🤖 A Retrieval-Augmented Generation assistant combining document embeddings and semantic search to answer customer queries accurately.
| Stack | Highlights |
|---|---|
| LangChain, Python | RAG pipeline with document embeddings & semantic search |
| Vector Search | Retrieves relevant context before generation to reduce hallucination |
| — | Built as part of GenAI internship workflows |
🔗 Repository: rag-customer-support-assistant • 🎥 Demo Video: Watch here
🔹 Meta Ads Performance Dashboard
📈 Interactive Power BI dashboard tracking Facebook/Instagram ad performance — impressions, clicks, CTR, conversions, and spend.
| Stack | Highlights |
|---|---|
| Power BI | KPI tracking across impressions, clicks, CTR, conversions |
| Data Cleaning | Structured raw ad-platform exports into a clean reporting model |
| — | Translates raw ad spend data into actionable performance insight |
🔗 Repository: Meta-Ad-Performance-Dashboard • 📊 Live Dashboard: View on Power BI
🔹 IPL Analytics Web App
🏏 Web application analyzing IPL cricket data with interactive visualizations and statistics.
| Stack | Highlights |
|---|---|
| Python, Streamlit/Dash | Interactive visualizations over historical IPL data |
| Data Visualization | Player & team-level stats explorable in real time |
| — | Deployed as a live, publicly accessible web app |
🔗 Repository: IPL-Analytics-Web-App • 🚀 Live Demo: Open App
Learning:
- GenAI & Tools- Build strong, portfolio-ready data projects
- Sharpen analytical & problem-solving skills
- Secure a role as a Data Analyst / Data Scientist
To activate the snake animation, add the snk GitHub Action workflow to this repo — it generates the SVG above automatically on a schedule.
"Turning data into decisions, one project at a time."