pandas

MaverickMCP - Personal Stock Analysis MCP Server

Excel-to-Neo4j knowledge graph examples: legacy py2neo v3 plus modern Neo4j GraphRAG/vector search.

🐍 Hand-picked awesome Python libraries and frameworks, organised by category

Flexible time series feature extraction & processing

A beginner's roadmap to self studying Machine Learning and Artificial Intelligence

Technical and sentiment analysis to predict the stock market with machine learning models based on historical time series data and news article sentiment collected using APIs and web scraping.

Tutorials for PandasAI

A curated collection of AI learning materials

Python package implementing ML feature engineering and pre-processing for polars or pandas dataframes.

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

Umbrella package for SciTeX — reproducible science from raw data to manuscript

ExcelChat - Chat w/ your excel file

Synthetic test data that hits the numbers you declare, exactly. Multi-table with verified foreign-key integrity, deterministic, no model in the data path. Python + MCP server.

Python Streamlit web app allowing users to interact with their data from a CSV or XLSX file, utilizing OpenAI API and LangChain. It imports necessary libraries, handles API key loading, displays a user-friendly interface for file upload and data preview, creates a Pandas DF agent with OpenAI, and executes user queries.

Power up your data science workflow with ChatGPT.

7 Day AI ML Fundamentals Workshop The purpose of this FREE workshop is 1. To give you a boost of getting started with AI. 2. A life-long community with a similar mindset. 3. strong grip on fundamentals that the advanced concepts will be easy to understand.

Python temellerinden Reinforcement Learning'e kadar uzanan, 22 modüllük uçtan uca Veri Bilimi & Makine Öğrenmesi yol haritası. Her konuda çalıştırılmış notebook'lar ve PDF tekrar soruları.

The Financial Research Analyst Agent is a hierarchical multi-agent system that provides comprehensive stock analysis by coordinating 11 specialized AI agents, 20+ analysis tools, a RAG knowledge pipeline, and a multi-provider data layer — all accessible through a Streamlit web app, REST API, and CLI.

The Practical Machine Learning Course focuses on equipping learners with the essential skills and tools needed to apply machine learning techniques in real-world scenarios.

Enhance your knowledge in medical research with the help of LLM and RAG.