AI/ML | Deep Learning | NLP | Generative AI | Backend | Frontend
Email: prathamrajput@gmail.com
GitHub: Prathamesh603 | LinkedIn: /prathamesh-rajput-5364802b6

I am a third-year Computer Engineering student passionate about building intelligent systems using AI, Machine Learning, Deep Learning, NLP, and Generative AI.
I have hands-on experience creating real-time AI applications, production-level ML pipelines, and backend systems.
Strong in Python, model development, problem-solving, and Data Structures & Algorithms.

Python | C++ | C | HTML | CSS | JavaScript

Scikit-Learn | TensorFlow | PyTorch | NumPy | Pandas | LangChain Hugging Face / Transformers

Skills include: Machine Learning, Deep Learning, NLP, Transformers, Generative AI, Agentic AI, RAG, CNN, LSTM, GRU, MobileNetV2, OpenCV, MLOps, LangChain, FAISS.

FastAPI | Docker | MySQL | Git

• Developed a deep learning model to classify 8 Surya Namaskar poses.
• Used MobileNetV2 with fine-tuning for improved accuracy and stability.
• Enabled real-time classification using OpenCV and MediaPipe.
• Supports image, video, and camera feed.

Tech Stack: TensorFlow, Keras, OpenCV, MediaPipe, Python

• Built a PDF-based question-answer chatbot using LangChain + FAISS.
• Integrated Groq (LLaMA models) for fast and context-aware retrieval.
• Achieved 85%+ accuracy in user queries.
• Reduced response time by 40% through optimized chunking and retrieval.

Tech Stack: Python, LangChain, FAISS, Groq (LLaMA), Streamlit
Live Demo: https://language-agnostic-chatbot-fe4dh4iuvs8tufejn5eket.streamlit.app/

• Designed an automated ML pipeline for phishing URL detection.
• Included data ingestion, preprocessing, model training, and deployment.
• Used MLflow for experiment tracking and Dockerized FastAPI for live inference.

Tech Stack: Python, Scikit-Learn, FastAPI, Docker, MLflow, MongoDB

• Built an LSTM-based neural network for predicting the next word in a text sequence.
• Trained on a custom dataset to learn grammar, sentence structure, and semantics.
• Achieved stable predictions using sequence modeling and tokenization.

Tech Stack: Python, TensorFlow/Keras, NLP, LSTM

• Developed a predictive ML solution improving baseline accuracy by 12%.
• Worked collaboratively in a competitive 4-member team.

• Built an Exoplanet Classification ML model using NASA’s open datasets.
• Developed preprocessing pipelines and ML models within 48 hours.
  Project Link: https://nasa-frontend-qisq.onrender.com/index.html

GitHub: https://github.com/Prathamesh603
LinkedIn: https://www.linkedin.com/in/prathamesh-rajput-5364802b6/
RAG Chatbot: https://language-agnostic-chatbot-fe4dh4iuvs8tufejn5eket.streamlit.app/
NASA Project: https://nasa-frontend-qisq.onrender.com/index.html
NLP Certificate: https://www.udemy.com/certificate/UC-1cc681e8-629e-47bd-959d-1d505f3c5381/