Search Results for "data modeling"
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Showing 104 open source projects for "data modeling"
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NYC Taxi Data
Import public NYC taxi and for-hire vehicle (Uber, Lyft)
The nyc-taxi-data repository is a rich dataset and exploratory project around New York City taxi trip records. It collects and preprocesses large-scale trip datasets (fares, pickup/dropoff, timestamps, locations, passenger counts) to enable data analysis, modeling, and visualization efforts. The project includes scripts and notebooks for cleaning and filtering the raw data, memory-efficient processing for large CSV/Parquet files, and aggregation workflows (e.g. trips per hour, heatmaps of pickups/dropoffs). ... -
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Mongoose
Elegant mongodb object modeling for node.js
Mongoose is a MongoDB object modeling tool that was built to answer the need for better ways to model your application data. It's designed to work in an asynchronous environment, providing a simple, straightforward approach to object modeling that skips out on the tedious tasks of writing MongoDB validation, casting and business logic boilerplate. Mongoose offers an uncomplicated schema-based solution, and comes with nifty features like type casting, validation, query building, and business logic hooks right out of the box. ... -
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Meridian
Meridian is an MMM framework
...The framework provides a robust foundation for constructing in-house MMM pipelines capable of handling both national and geo-level data, with built-in support for calibration using experimental data or prior knowledge. Meridian uses the No-U-Turn Sampler (NUTS) for Markov Chain Monte Carlo (MCMC) sampling to produce statistically rigorous results, and it includes GPU acceleration to significantly reduce computation time. -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. -
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GeoStats.jl
An extensible framework for geospatial data science
GeoStats.jl is a Julia framework for geospatial data science and geostatistical modeling. It’s fully implemented in Julia and designed to provide an extensible, high-performance stack that handles spatial domains, interpolation, simulation, learning, and visualization. The package is modular: it breaks out geometry, spatial domains, transforms, variograms, covariance models, and modeling into subpackages (e.g., GeoStatsBase, GeoStatsModels, GeoStatsTransforms). ... -
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Frappe
Low code web framework for real world applications
Frappe is a full-stack, low-code web framework written in Python and JavaScript, used to build scalable and modular enterprise applications. It powers ERPNext and includes tools for REST APIs, user management, document modeling, workflows, and real-time updates. Frappe uses a "model-view-controller" approach with its own ORM and frontend system, enabling rapid development without sacrificing control or performance. -
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EpicReact.Dev
Build a ReactJS App workshop
EpicReact.Dev is the codebase used in the “Build an Epic React App” workshop, where participants build a complete React application from scratch. The project demonstrates how to structure a modern React app, including data modeling, authentication, routing, testing, and interaction with a backend. It uses a realistic “bookshelf” domain where users can manage lists of books, track reading status, and record notes, which provides a concrete context for learning. The repository includes setup scripts and system requirements checks for Git, Node, and npm, plus Docker and Codespaces options for people who struggle with local environments. ... -
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RStudio Cheatsheets
Curated collection of official cheat sheets for data science tools
...It covers topics such as data wrangling, data import, modeling, visualization, RStudio IDE shortcuts, Shiny development, and the tidyverse suite (dplyr, ggplot2, tidyr, purrr). These cheat sheets are widely used by R learners, educators, and practitioners as quick reference tools, and they often ship with RStudio by default or are linked from RStudio’s help/documentation pages. -
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Copulas
A library to model multivariate data using copulas
Copulas is a Python library for modeling multivariate distributions and sampling from them using copula functions. Given a table of numerical data, use Copulas to learn the distribution and generate new synthetic data following the same statistical properties. Choose from a variety of univariate distributions and copulas – including Archimedian Copulas, Gaussian Copulas and Vine Copulas. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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CTGAN
Conditional GAN for generating synthetic tabular data
CTGAN is a collection of Deep Learning based synthetic data generators for single table data, which are able to learn from real data and generate synthetic data with high fidelity. If you're just getting started with synthetic data, we recommend installing the SDV library which provides user-friendly APIs for accessing CTGAN. The SDV library provides wrappers for preprocessing your data as well as additional usability features like constraints. When using the CTGAN library directly, you may... -
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Mangle
Go library for Datalog-style logical reasoning and domain modeling
Mangle is a programming language developed by Google for deductive database programming, serving as an advanced extension of Datalog. It is designed to unify and query data from multiple sources in a structured, declarative way while allowing developers to model complex relationships and domain knowledge beyond binary predicates. Mangle enhances traditional Datalog by introducing features such as aggregation, function calls, and optional type-checking, which make it more practical for modern... -
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Apache InLong
Apache InLong - a one-stop integration framework for massive data
Apache InLong is a one-stop integration framework for massive data that provides automatic, secure and reliable data transmission capabilities. InLong supports both batch and stream data processing at the same time, which offers great power to build data analysis, modeling and other real-time applications based on streaming data. InLong (应龙) is a divine beast in Chinese mythology who guides the river into the sea, and it is regarded as a metaphor of the InLong system for reporting data streams. ... -
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Easy3D
Efficient library for processing 3D data
Easy3D is a lightweight, easy-to-use, and efficient library for processing and rendering 3D data, implemented in C++ with Python bindings. It is designed for tasks such as 3D modeling, geometry processing, and rendering, emphasizing simplicity and efficiency. Easy3D serves as a valuable tool for research, education, and the development of sophisticated 3D applications, providing a solid foundation for handling 3D data. -
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XState
State machines and statecharts for the modern web
JavaScript and TypeScript finite state machines and statecharts for the modern web. Statecharts are a formalism for modeling stateful, reactive systems. This is useful for declaratively describing the behavior of your application, from the individual components to the overall application logic. XState is a library for creating, interpreting, and executing finite state machines and statecharts, as well as managing invocations of those machines as actors. The following fundamental computer... -
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Ash
A declarative, extensible framework for building Elixir applications
Ash is a declarative framework for building resource-oriented apps in Elixir. It emphasizes composability, DSL-driven definitions of resources/actions/relationships, and extensibility through plugins for API, database, and UI layers. -
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AtomicServer
An open source headless CMS / real-time database
Create, share, fetch, and model Atomic Data! AtomicServer is a lightweight, yet powerful CMS / Graph Database. Atomic Data is a modular specification for sharing, modifying, and modeling graph data. It combines the ease of use of JSON, the connectivity of RDF (linked data), and the reliability of type-safety. Atomic Data uses links to connect pieces of data, and therefore makes it easier to connect datasets to each other - even when these datasets exist on separate machines. -
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Gen.jl
A general-purpose probabilistic programming system
...Neural network inference is fast, but can be inaccurate on out-of-distribution data, and requires expensive training. -
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Cesium
An open-source JavaScript library for world-class 3D globes and maps
CesiumJS is an open source JavaScript library for creating world-class 3D globes and maps with the best possible performance, precision, visual quality, and ease of use. Developers across industries, from aerospace to smart cities to drones, use CesiumJS to create interactive web apps for sharing dynamic geospatial data. Built on open formats, CesiumJS is designed for robust interoperability and scaling for massive datasets. CesiumJS is released under the Apache 2.0 license and is free for... -
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Remult
Full-stack CRUD, simplified, with SSOT TypeScript entities
Remult is a full-stack CRUD framework for building type-safe web applications using a single shared TypeScript model. It automatically exposes backend APIs based on your entities and provides real-time synchronization, role-based access control, and deep integration with front-end frameworks like React, Angular, and Vue. Remult simplifies full-stack development by unifying API and model definitions. -
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Glamorous Toolkit
Glamorous Toolkit is the Moldable Development environment
Programming, exploring data, browsing APIs, knowledge management, log investigations, domain modeling are all part of the same continuum. They require dedicated tools, but those tools can come to you in an integrated experience that is specific to your context. This is the essence of Moldable Development. And this is what Glamorous Toolkit makes practical. Glamorous Toolkit is the Moldable Development environment. -
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Stock prediction deep neural learning
Predicting stock prices using a TensorFlow LSTM
Predicting stock prices can be a challenging task as it often does not follow any specific pattern. However, deep neural learning can be used to identify patterns through machine learning. One of the most effective techniques for series forecasting is using LSTM (long short-term memory) networks, which are a type of recurrent neural network (RNN) capable of remembering information over a long period of time. This makes them extremely useful for predicting stock prices. Predicting stock... -
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statsmodels
Statsmodels, statistical modeling and econometrics in Python
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. Generalized linear models with support for all... -
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Complete Node Bootcamp
Starter files, final projects and FAQ for my Complete Node.js Bootcamp
...It contains starter files, finished project files, and course support material for building backend applications with JavaScript. The repository is centered on practical server-side development, including Node.js fundamentals, Express APIs, MongoDB data modeling, authentication, security, payments, deployment, and real-world backend architecture. Learners can use the starter files to follow the lessons and compare their code with the final versions when something breaks. It also includes slides and FAQ-style guidance to make the course easier to navigate. The project is best understood as a hands-on educational workspace for learning production-minded Node.js development. -
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RAG Anything
RAG-Anything: All-in-One RAG Framework
RAG-Anything is an open-source unified framework that extends the Retrieval-Augmented Generation (RAG) paradigm to fully multimodal document and knowledge retrieval, enabling systems to ingest, parse, represent, and query rich content that includes text, images, tables, formulas, and other structured or visual elements. Traditional RAG systems are typically limited to text and cannot effectively work across heterogeneous document layouts, but RAG-Anything addresses this by modeling multimodal content in ways that preserve cross-modal relationships and semantic context, often treating content elements as interconnected knowledge entities rather than separate data silos. The system uses a multi-stage pipeline (e.g., document parsing, content analysis, knowledge graph construction, intelligent retrieval) so queries can navigate across modalities with deeper understanding and relevance. -
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GLM.jl
Generalized linear models in Julia
GLM.jl is a Julia package for fitting linear and generalized linear models (GLMs) with a syntax and functionality familiar to users of R or other statistical environments. It is part of the JuliaStats ecosystem and is tightly integrated with StatsModels.jl for formula handling, and Distributions.jl for specifying error families. The package supports modeling through both formula-based (e.g. @formula) and matrix-based interfaces, allowing both high-level convenience and low-level control....
