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dinesh9997/README.md

Hi 👋, I'm GUJJU DINESH

Building intelligent AI systems, scalable full-stack applications, and agentic workflows.

⚡ About Me

  • 🎓 B.Tech in Artificial Intelligence
  • 🤖 Focus: Generative AI • RAG • Agentic AI • NLP
  • 💻 Full Stack Developer (React • FastAPI • Node.js)
  • 🧠 1000+ DSA problems solved
  • ☁️ 25+ Google Cloud Skill Badges
  • 🔬 Interested in AI research & real-world ML systems
  • 📍 Andhra Pradesh, India

💻 Tech Stack

🏆 Competitive Programming

                    

- 🟨 LeetCode Rating: 1570 - 🟫 CodeChef Rating: 1512 - 🔥 1000+ Problems Solved - 🧠 Strong DSA & problem-solving skills

📈 GitHub Stats

📫 Connect With Me

🕹️ Pacman Contribution Graph

pacman contribution graph

⭐ “Building intelligent systems that solve real-world problems”

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  1. WEEK-1 WEEK-1 Public

    A web application that uses machine learning to predict water pollution levels based on the year and station number. It automatically checks the predicted chemical levels against safety guidelines …

    Jupyter Notebook

  2. TEAM-05 TEAM-05 Public

    Hackathon for JNTU Vijayanagaram - December 13, 2025

  3. Customer-Churn-Prediction Customer-Churn-Prediction Public

    Built a machine learning web application that predicts customer churn using Logistic Regression, Decision Tree, Random Forest, and SVM models. Developed with Flask, HTML, CSS, and JavaScript to pro…

    HTML

  4. web-development-projects web-development-projects Public

    A modern web development portfolio featuring a dynamic full-stack Spotify clone (curated with nostalgic childhood soundtracks) and a responsive Netflix UI clone.

    HTML

  5. OpenDeepResearcher-Agentic-LLM-Research-Framework OpenDeepResearcher-Agentic-LLM-Research-Framework Public

    Forked from stkwer/OpenDeepResearcher-Agentic-LLM-Research-Framework

    Agentic RAG Research System implementation using LM Studio and Streamlit .

    Python

  6. employee-salary-prediction employee-salary-prediction Public

    💼 End-to-end Machine Learning web application predicting employee salary class (≤50K or >50K) using optimized Gradient Boosting. Developed under AICTE – Edunet Foundation Internship.

    Jupyter Notebook