data-analysis

Automated system for LLM evaluation via agents. Doc as below:

Loop until it's better — drop-in agentic loops (autoresearch, scientific writing, data analysis, code/SQL/prompt optimization, red-teaming) as open-standard Agent Skills. Verification-gated; native on Claude Code, portable across Codex, Cursor & other Skills hosts.

The GenAI-powered toolkit for automated data intelligence.

Python + Markdown framework for building internal apps.

Multi-Class Text Classification for products based on their description with Machine Learning algorithms and Neural Networks (MLP, CNN, Distilbert).

Rank images using TrueSkill by comparing them against each other in the browser. 🖼📊

This repository contains various examples of how to use LangChain, a way to use natural language to interact with LLM, a large language model from Azure OpenAI Service.

ExcelChat - Chat w/ your excel file

Code Repository for Machine Learning, Data Science and Generative AI with Python, Published by Packt

Methodology + templates for a trustworthy, traceable, self-growing AI data-analyst agent: metrics-first, independent read-only validation, and a self-growing rule library.

Rental app with in-depth data analysis, interactive data visualization, predictive analytics, and chatbot using Dash. Selected as Plotly-Dash’s Top GenAI Project

The AI-Powered Healthcare Intelligence Network is an AI-driven system offering disease prediction, drug recommendations, heart disease risk assessment, and an AI medical chatbot. Using ML, NLP, and LLMs, it provides accurate diagnoses, insights, and recommendations, enhancing healthcare accessibility, efficiency, and decision-making .

💸 A comprehensive AI-powered data explorer that combines FRED economic data & insights with vector search, regression analysis, and interactive RAG chatbot via Pinecone Vector DB, OpenAI, Claude, and Gemini. Built with TypeScript, React, and Express for seamless full-stack performance.

H.E.I.M.D.A.L.L looks at fleet telemetry and gives you natural-language insights. GPU data loading (cuDF), local LLM inference (Gemma 2), and production NIM on GKE. Open the notebooks, run cells, get answers! Quick start should not take longer than 10 minutes and the T4 path is completely free!

Having fun with ML