Search Results for "data visualization" - Page 2
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Showing 238 open source projects for "data visualization"
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dtreeviz
Python library for decision tree visualization & model interpretation
A python library for decision tree visualization and model interpretation. Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. The visualizations are inspired by an educational animation by R2D3; A visual introduction to machine learning. ... -
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ReinforcementLearning.jl
A reinforcement learning package for Julia
A collection of tools for doing reinforcement learning research in Julia. Provide elaborately designed components and interfaces to help users implement new algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate and diagnose agents. Facilitate reproducibility from traditional tabular methods to modern deep reinforcement learning algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and... -
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tidytext
Text mining using tidy tools
tidytext brings tidy data principles to text mining by converting text into a tidy data frame format. It provides tools for tokenization, sentiment analysis, n‑gram creation, and term‑document matrices, enabling interoperability with dplyr, ggplot2, and other tidyverse workflows. -
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Eidos
An extensible framework for Personal Data Management
Eidos is an extensible personal data management platform designed to help users organize and interact with their information using a local-first architecture. The system transforms SQLite into a flexible personal database that can store structured and unstructured information such as notes, documents, datasets, and knowledge resources. Its interface is inspired by tools like Notion, allowing users to create documents, databases, and custom views to organize personal information. Unlike... -
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MCP Grafana
MCP server for Grafana
The Grafana MCP Server is a Model Context Protocol (MCP) server designed to provide access to Grafana instances and their surrounding ecosystems. It enables seamless integration with Grafana's visualization and monitoring capabilities. -
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MNE-Python
Magnetoencephalography (MEG) and Electroencephalography EEG in Python
Open-source Python package for exploring, visualizing, and analyzing human neurophysiological data. MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more. -
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FEniCS.jl
A scientific machine learning (SciML) wrapper for the FEniCS
FEniCS.jl is a wrapper for the FEniCS library for finite element discretizations of PDEs. This wrapper includes three parts. Installation and direct access to FEniCS via a Conda installation. Alternatively one may use their current FEniCS installation. A low-level development API and provides some functionality to make directly dealing with the library a little bit easier, but still requires knowledge of FEniCS itself. Interfaces have been provided for the main functions and their... -
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DeepAnalyze
Autonomous LLM agent for end-to-end data science workflows
DeepAnalyze is an open source project that introduces an agentic large language model designed to perform autonomous data science tasks from start to finish. It is built to handle the entire data science pipeline, including data preparation, analysis, modeling, visualization, and report generation without requiring continuous human guidance. DeepAnalyze is capable of conducting open-ended data research across multiple data formats such as structured tables, semi-structured files, and unstructured text, enabling flexible and comprehensive analysis workflows. ... -
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Learning Interpretability Tool
Interactively analyze ML models to understand their behavior
The Learning Interpretability Tool (LIT, formerly known as the Language Interpretability Tool) is a visual, interactive ML model-understanding tool that supports text, image, and tabular data. It can be run as a standalone server, or inside of notebook environments such as Colab, Jupyter, and Google Cloud Vertex AI notebooks. -
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Python Programming Hub
Learn Python and Machine Learning from scratch
...The repository emphasizes hands-on learning by demonstrating real programming tasks such as data manipulation, statistical analysis, visualization, and automation. It also includes examples of commonly used libraries such as NumPy, Pandas, and other tools used in data science workflows. -
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Potpie
Create custom engineering agents for your codebase
Potpie is an AI-powered data analysis tool that automates the exploration and visualization of datasets, assisting users in uncovering insights without extensive coding. -
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PandasAI
PandasAI is a Python library that integrates generative AI
PandasAI is a Python library that adds Generative AI capabilities to pandas, the popular data analysis and manipulation tool. It is designed to be used in conjunction with pandas, and is not a replacement for it. PandasAI makes pandas (and all the most used data analyst libraries) conversational, allowing you to ask questions to your data in natural language. For example, you can ask PandasAI to find all the rows in a DataFrame where the value of a column is greater than 5, and it will... -
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airda
airda(Air Data Agent
airda(Air Data Agent) is a multi-smart body for data analysis, capable of understanding data development and data analysis needs, understanding data, generating data-oriented queries, data visualization, machine learning and other tasks of SQL and Python codes. -
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MLDatasets.jl
Utility package for accessing common Machine Learning datasets
This package represents a community effort to provide a common interface for accessing common Machine Learning (ML) datasets. In contrast to other data-related Julia packages, the focus of MLDatasets.jl is specifically on downloading, unpacking, and accessing benchmark datasets. Functionality for the purpose of data processing or visualization is only provided to a degree that is special to some datasets. -
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MCP Server Chart
A visualization mcp contains 25+ visual charts
mcp-server-chart is a TypeScript Model Context Protocol (MCP) server that turns AntV’s visualization stack into agent-callable tools for automatic chart generation and lightweight data analysis. Out of the box it exposes more than 20–25 chart generators—covering staples like bar, line, area, histogram and pie, plus advanced visuals such as dual-axes, heatmaps, radar, flow and fishbone diagrams—so an AI client can request a chart and receive an image URL in return. -
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Apache Hamilton
Helps data scientists define testable self-documenting dataflows
Apache Hamilton is an open-source Python framework designed to simplify the creation and management of dataflows used in analytics, machine learning pipelines, and data engineering workflows. The framework enables developers to define data transformations as simple Python functions, where each function represents a node in a dataflow graph and its parameters define dependencies on other nodes. Hamilton automatically analyzes these functions and constructs a directed acyclic graph... -
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Aix-DB
Based on the LangChain/LangGraph framework
Aix-DB is an open-source intelligent data analysis platform that combines large language models with database technologies to enable conversational data exploration. The system is designed as a ChatBI solution that allows users to query datasets using natural language and receive structured insights, charts, and visualizations automatically. Built on frameworks such as LangChain and LangGraph, Aix-DB integrates retrieval-augmented generation and Text-to-SQL capabilities to convert user... -
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PaddleX
PaddlePaddle End-to-End Development Toolkit
...Users only need to put pictures belonging to the same category in the same folder. When the model is trained, we need to divide the training set, the validation set and the test set. Therefore, we need to divide the above data. Using the paddlex command, the data set can be randomly divided into 70% training set, 20% validation set and 10% test set. If you use the PaddleX visualization client for model training, the data set division function is integrated in the client, and you do not need to use command division by yourself. -
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MEDIUM_NoteBook
Repository containing notebooks of my posts on Medium
MEDIUM_NoteBook is an open-source repository that contains a collection of Jupyter notebooks and code examples originally developed to accompany technical articles published on Medium. The project provides practical demonstrations of machine learning algorithms, data analysis workflows, and visualization techniques. Each notebook typically focuses on explaining a specific concept through step-by-step examples that combine explanatory text, code, and visual outputs. The repository covers a wide variety of data science topics such as predictive modeling, data preprocessing, statistical analysis, and feature engineering. ... -
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Quantitative Trading System
A comprehensive quantitative trading system with AI-powered analysis
Quantitative Trading System is a comprehensive quantitative trading platform that integrates artificial intelligence, financial data analysis, and automated strategy execution within a unified software system. The project is designed to provide an end-to-end infrastructure for building and operating algorithmic trading strategies in financial markets. It includes tools for collecting and processing market data from multiple sources, performing statistical and machine learning analysis, and... -
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Vector Admin
The universal tool suite for vector database management
Vector Admin is a full-stack management platform designed to simplify the handling, visualization, and control of vector databases used in modern AI and machine learning applications. It acts as a universal interface for interacting with multiple vector database providers such as Pinecone, Chroma, Weaviate, and Qdrant, enabling users to manage embeddings without relying solely on APIs or backend tooling. The platform provides a user-friendly graphical interface that allows developers and non-technical users alike to inspect, edit, and organize vectorized data in a way that is typically opaque in standard workflows. ... -
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Preswald
Python tool for browser-based interactive data apps in one file
Preswald is an open source Python-based framework and static-site generator designed for building interactive data applications that run entirely in the browser. It packages application logic, data processing, and user interface components into a single self-contained output, enabling easy sharing and deployment without requiring local dependencies. Preswald leverages a WebAssembly runtime along with technologies like Pyodide and DuckDB to execute Python code directly in the browser... -
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PaperBanana
Extension of Google Research’s PaperBanana
PaperBanana is an open-source agentic framework designed to automatically generate publication-quality academic diagrams and statistical plots directly from text descriptions. The project focuses on helping researchers, educators, and data scientists transform conceptual descriptions of figures into structured visual outputs suitable for research papers, presentations, and technical reports. Instead of manually designing charts or diagrams using traditional visualization tools, users can describe the desired figure in natural language and allow the system to generate the visual representation automatically. ... -
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Mito
AI-powered Jupyter spreadsheet that converts workflows into Python
Mito is an open source set of Jupyter extensions designed to speed up Python workflows and data analysis. It combines a spreadsheet-style interface with AI-assisted coding, allowing users to explore, clean, and transform data without switching tools. Mito includes a context-aware AI assistant that helps generate code, debug errors, and guide workflows directly inside Jupyter. Its spreadsheet layer supports familiar functions such as filters, pivot tables, and formulas, while automatically... -
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database.build
In-browser Postgres sandbox with AI assistance
database.build is an innovative in-browser development environment that allows users to instantly create and interact with fully functional PostgreSQL databases directly within their browser, powered by WebAssembly technology. It eliminates the need for traditional database setup by running a lightweight version of PostgreSQL locally using PGlite, enabling developers to experiment, prototype, and analyze data without relying on external servers. Each database instance is paired with a large...
