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| # Projects — Kuldeep Kumar Mishra | |
| ## 1. Agentic Hybrid RAG Chatbot (Flagship Project) | |
| Type: AI Engineering / Multi-Agent System | |
| Status: Deployed on Hugging Face | |
| Description: | |
| A sophisticated multi-agent chatbot system that combines Retrieval-Augmented Generation (RAG) with intelligent agent routing. The system uses multiple specialized agents coordinated through an intelligent orchestration layer to handle diverse user queries with high accuracy. | |
| Tech Stack: | |
| - Language: Python | |
| - Framework: Flask, LangChain, LangGraph | |
| - LLM: LLaMA 3.1 8B via Groq API | |
| - Vector DB: ChromaDB | |
| - Keyword Search: BM25 (Okapi BM25) | |
| - Memory: SQLite (persistent conversation memory) | |
| - Retrieval: Reciprocal Rank Fusion (RRF) — hybrid of vector + keyword search | |
| Agent Architecture: | |
| - RAG Agent: Answers questions from indexed documents using hybrid retrieval | |
| - Web Search Agent: Searches the internet for real-time information | |
| - Math Agent: Solves mathematical problems and equations | |
| - Memory Agent: Retrieves and uses past conversation context | |
| - General Agent: Handles general knowledge questions | |
| - Router Agent: Intelligently routes each query to the correct agent | |
| Key Features: | |
| - Hybrid retrieval combining semantic vector search + BM25 keyword search via RRF | |
| - Persistent conversation memory using SQLite | |
| - PDF document indexing and retrieval | |
| - Streaming responses for real-time interaction | |
| - Multi-agent coordination via LangGraph | |
| Deployment: Hugging Face Spaces (publicly accessible) | |
| GitHub: Available on kuldeepmishra92 | |
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| ## 2. Skin Cancer Classification System | |
| Type: Computer Vision / Medical AI | |
| Status: Deployed on Hugging Face | |
| Description: | |
| A medical image classification system that uses deep learning to classify skin lesions into seven diagnostic categories. The system assists in early detection of skin cancer from dermoscopy images. | |
| Tech Stack: | |
| - Architecture: Google Vision Transformer Large (ViT-Large) | |
| - Dataset: HAM10000 dermoscopy dataset (10,000+ images) | |
| - Framework: PyTorch, Hugging Face Transformers | |
| - Deployment: Hugging Face Spaces + Model Hub | |
| 7 Classification Categories: | |
| 1. Melanocytic Nevi (nv) | |
| 2. Melanoma (mel) | |
| 3. Benign Keratosis-like Lesions (bkl) | |
| 4. Basal Cell Carcinoma (bcc) | |
| 5. Actinic Keratoses (akiec) | |
| 6. Vascular Lesions (vasc) | |
| 7. Dermatofibroma (df) | |
| Performance: | |
| - Classification Accuracy: Over 92% | |
| - Model: vit-large-skin-cancer-ham10000 (published on Hugging Face Model Hub) | |
| Deployment: Hugging Face Spaces and Model Hub (publicly available) | |
| --- | |
| ## 3. Airline Passenger Satisfaction Prediction System | |
| Type: Machine Learning / Tabular Data | |
| Status: Deployed as Flask Web Application | |
| Description: | |
| A machine learning system that predicts whether airline passengers are satisfied or dissatisfied with their flight experience based on various features including service quality, flight details, and passenger demographics. | |
| Tech Stack: | |
| - Language: Python | |
| - ML Framework: LightGBM | |
| - Hyperparameter Tuning: Optuna | |
| - Deployment: Flask web application | |
| - Other Libraries: Scikit-learn, Pandas, NumPy | |
| Approach: | |
| - Evaluated multiple algorithms: Logistic Regression, Random Forest, XGBoost, LightGBM | |
| - Selected LightGBM as best performing model | |
| - Applied Optuna for automated hyperparameter optimization | |
| Performance: | |
| - Final Model Accuracy: Over 96% | |
| Key Features: | |
| - Interactive web interface for real-time prediction | |
| - Feature importance visualization | |
| - Model explainability | |
| --- | |
| ## 4. World University Rankings Platform | |
| Type: Data Science / Analytics | |
| Status: Published on Hugging Face | |
| Description: | |
| A data analytics and visualization platform for exploring and analyzing world university rankings data. Enables users to filter, compare, and understand ranking trends across institutions and countries. | |
| Deployment: Hugging Face Spaces | |
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| ## Published Hugging Face Projects | |
| 1. Skin Cancer Classifier (Application) | |
| 2. Multi-Agent RAG Chatbot (Application) | |
| 3. World University Rankings Platform (Application) | |
| 4. vit-large-skin-cancer-ham10000 (Model) | |
| All projects available at: huggingface.co/Kuldeepmishra3 | |