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PAVAN2005-LAB/readme.md

Pavan Kumar Yadav

Software Engineer | Full Stack & Data Science Practitioner CS @ Government Engineering College, Dahod & BS @ Indian Institute of Technology Madras πŸ“ Jamnagar, Gujarat, India

Portfolio β€’ LinkedIn β€’ LeetCode β€’ Codeforces β€’ MS Learn β€’ Blog

Portfolio LinkedIn LeetCode Codeforces Microsoft Learn


πŸ›οΈ Executive Summary

Software Engineer and Machine Learning practitioner specializing in backend engineering, LLM agent systems, native desktop applications, and data analytics pipelines. Final year CS undergraduate at Government Engineering College, Dahod (CGPA: 8.52) and pursuing a BS in Data Science & Applications from IIT Madras (CGPA: 7.04, 100% Python Score).

Proven track record building end-to-end production systems with verified impact, including a Global Rank #740 in TCS CodeVita Season 13 and predictive ML models achieving 98.9%+ accuracy.


πŸ“Š Highlights & Metrics

Benchmark Value / Metric Details
TCS CodeVita Season 13 Global Rank #740 Verified global competitive programming rank
TCS CodeVita Season 12 Global Rank #4918 Global competitive programming season rank
GEC Dahod Engineering 8.52 CGPA B.Tech Computer Science & Engineering (Final Year)
IIT Madras Data Science 7.04 CGPA (100% Python) BS in Data Science & Applications
Problem Solving 200+ Solved LeetCode (OC6WwPL7IG), Codeforces Max Pupil (1189)
Industry Internships Azure / IBM SkillsBuild CloudThat (Azure) & IBM SkillsBuild x CSRBOX Intern
Location Jamnagar, Gujarat, India Open for remote & on-site opportunities

πŸ’» Core Competencies & Technical Matrix

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ Category                  β”‚ Stack / Tooling                                                        β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ Languages                 β”‚ Python, Go (Golang), JavaScript / TypeScript, Java, C, SQL, HTML5/CSS3  β”‚
β”‚ Backend & Web             β”‚ Node.js, Express.js, Django, Django REST Framework, FastAPI, Wails v2  β”‚
β”‚ AI / ML & Data Science    β”‚ LangGraph, Gemini API, XGBoost, Scikit-Learn, Pandas, NumPy, Power BI  β”‚
β”‚ Cloud & Infrastructure    β”‚ Microsoft Azure, Docker, PostgreSQL, MongoDB, MySQL, Git, GCP          β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

πŸš€ Key Engineering Projects

  • Architecture: Go 1.22+ β€’ Wails v2 β€’ Gemini API β€’ OpenAI API β€’ Local Ollama Engine
  • Impact: Lightweight native desktop app (<12 MB binary) that categorizes messy folder structures into structured directories. Supports 100% offline local Ollama models for private, zero-latency organization.
  • Architecture: Node.js β€’ Express.js β€’ PostgreSQL β€’ Razorpay API β€’ Nodemailer
  • Impact: Event management engine featuring real-time registrations, Razorpay payment processing, automated transactional email dispatch, and live production APIs (Live Demo).
  • Architecture: Python β€’ Django REST Framework β€’ Docker β€’ Waitress β€’ JWT Auth
  • Impact: Production Library Management System actively deployed on local college network. Built with JWT authentication, bulk CSV data ingestion, and containerized WSGI server deployment.
  • Architecture: Python β€’ LangGraph β€’ LangChain β€’ Playwright β€’ Gemini API β€’ FastAPI
  • Impact: Autonomous AI agent runner capable of multi-step reasoning, web scraping, dynamic code execution, and automated evaluation submissions.
  • Architecture: Python β€’ XGBoost β€’ Scikit-Learn β€’ Pandas β€’ Matplotlib β€’ Seaborn
  • Impact: End-to-end machine learning pipeline analyzing IEA building-level electricity datasets, achieving 98.9% classification accuracy for demand forecasting.

πŸ“ˆ GitHub Engineering Activity

GitHub Stats Top Languages

πŸ“¬ Direct Contact & Credentials

Β© Pavan Kumar Yadav. All rights reserved.

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  1. Energy_Consumption_Analysis Energy_Consumption_Analysis Public

    Jupyter Notebook

  2. GED_Dahod_library GED_Dahod_library Public

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  3. -LLM-App-Builder-Deployer -LLM-App-Builder-Deployer Public

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  4. TDS_P2_SEP_2025 TDS_P2_SEP_2025 Public

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  5. Techfest2k26 Techfest2k26 Public

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  6. intership_tracker intership_tracker Public

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