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nileshkumbhare4520-commits/README.md

Hi👋, I'm Nilesh Kumbhare

About Me

I am a Python and AI enthusiast focused on learning and applying Machine Learning, Deep Learning, and Large Language Models (LLMs). I work on data-driven projects and explore intelligent and agentic AI systems, with an interest in model deployment and real-world applications.

Currently, I am strengthening my understanding of ML/DL fundamentals while building practical projects involving Generative AI,Agentic AI, NLP, and predictive modeling.

Technical Skills Programming & Libraries

Python

NumPy, Pandas

Matplotlib, Seaborn

Scikit-learn

Machine Learning & AI

Machine Learning

Deep Learning

NLP and Large Language Models (LLMs)

Generative AI

Agentic AI (learning and experimentation)

Deployment & Tools

Flask

HTML

Docker

Git and GitHub

Projects

Completed Projects

Twitter Sentiment Analysis using LSTM, GRU, and BERT

Fake Job Detection using NLP and Machine Learning

Hotel Management Data Analysis for insights and prediction

Ongoing Projects

Agentic AI–based applications

LLM-powered RAG systems

Interests

Data visualization and predictive modeling

Intelligent systems and applied AI

Generative and agentic AI research

Contact

Location: India

Email: nileshkumbhare4520@gmail.com

Popular repositories Loading

  1. Hotel-Management Hotel-Management Public

    “Analyzed hotel pricing data to find which discounts increase bookings and built models to optimize discount strategy.”

    Jupyter Notebook

  2. fake-job-detection fake-job-detection Public

    Fake Job Posting Detection using ML & NLP

    Jupyter Notebook

  3. twitter-sentiment-analysis twitter-sentiment-analysis Public

    End-to-end Twitter Sentiment Analysis using LSTM, GRU and BERT with Flask API

    Jupyter Notebook

  4. nileshkumbhare4520-commits nileshkumbhare4520-commits Public

  5. rag-wiki-assistant rag-wiki-assistant Public

    A simple Retrieval-Augmented Generation (RAG) chatbot built with LangChain, ChromaDB, Sentence Transformers, and Ollama. The system answers questions using a Wikipedia-based AI knowledge base.

    Python