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Vigneshpk6/README.md

Hi 👋, I'm Vignesh P

Entry-level Data Scientist | Python | Power BI | ML Enthusiast

🚀 About Me

  • ⚡ Python Backend Developer (FastAPI + Django)

  • 🧩 Experience building REST APIs using FastAPI

  • 💡 Passionate about Data Science, Machine Learning, and AI

  • 📊 Experienced with Power BI dashboards (Amazon Prime Analysis, IPL Analysis, Bike Sales, Budget Analysis)

  • 🤖 Built AI Chatbots using Ollama + Qdrant and experimented with Claude (Anthropic)

  • 🧠 Worked on Deep Learning project for Enhanced Speech Clarity

  • 🌐 Developed Django Restaurant Reviews Web App

  • 🐳 Familiar with Docker for containerising apps and reproducible environments

  • 🤗 Worked with Hugging Face models & transformers for NLP tasks

  • 🎓 Published a paper at ICIRIAC 2024

🛠️ Skills & Tools

Tools & Technologies

Languages: Python, SQL Libraries: Pandas, NumPy, Matplotlib, Scikit-Learn, Transformers Tools: Power BI, Git, VS Code, Jupyter Notebook, Docker AI Platforms: Hugging Face, Ollama, Claude (Anthropic) Frameworks: Django, Machine Learning Other: Data Cleaning, EDA, Model Building & Deployment

🔭 Current Focus

  • Containerising ML pipelines and dashboards using Docker
  • Fine-tuning and deploying transformer models on Hugging Face
  • Prototyping assistant/chatbot features with Claude and Ollama + Qdrant

📌 Featured Projects

Amazon Prime Data Analysis
IPL Cricket Analytics
Bike Showroom Sales Dashboard
Budget Analysis Dashboard
Speech Enhancement Deep Learning
Django Restaurant Review System

- Amazon Prime Data Analysis — EDA, visualizations, insights - IPL Cricket Analytics — match-level feature engineering & models - Bike Showroom Sales Dashboard — Power BI interactive reports - Budget Analysis Dashboard — financial insights and KPIs - Enhanced Speech Clarity using Deep Learning — preprocessing, model training - Django Restaurant Review System — full-stack web app

Each project contains a detailed README with dataset, approach, and results.

⚙️ Example: Docker + Hugging Face (short snippet)

# build and run a simple container for model inference
docker build -t vignesh-model .
docker run -p 5000:5000 vignesh-model
# minimal Hugging Face inference example
from transformers import pipeline
nlp = pipeline('sentiment-analysis')
print(nlp('I love working on ML projects!'))

📫 Contact

📊 GitHub Stats

- **Email:** your-email-here - **LinkedIn:** your-linkedin-here

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