Data Science
Showing 36 of 506 projects
A web interface and REST API for classification and regression using Support Vector Machine (SVM) and Support Vector Regression (SVR) algorithms.
An end-to-end Python outlier detection system with database support, automated machine learning, and unified APIs for statistical, ML, and deep learning models.
Define, run, and deploy big data applications on AWS, OpenStack, and local machines using Docker.
R client for the Elasticsearch HTTP API, enabling data indexing, search, and analysis from R.
An F# type provider that enables seamless interoperability with R packages, offering type-safe access to R functions from .NET.
Course materials for GWU's Data Mining and Machine Learning classes covering preprocessing, modeling, and practical Kaggle applications.
An R driver for Neo4j that enables reading and writing graph data directly from the R environment.
An idiomatic Clojure machine learning library providing a unified interface for classification, regression, and unsupervised models.
An R driver for Neo4j that enables reading and writing graph data directly from R.
A fast feature selection algorithm for tree-based models like XGBoost, designed to outperform Boruta in speed and performance.
A curated collection of learning resources, R packages, and practical examples for understanding and applying topic modeling techniques.
A PyTorch-based Python package for deep and machine learning analysis of microscopy data, designed for domain scientists.
R bindings to the libgit2 library, providing programmatic access to Git repositories from R.
A Darcula theme for JupyterLab, modeled after the classic IntelliJ theme, with dark scrollbar support.
An R package for flexibly rearranging, reshaping, and aggregating data, now superseded by tidyr.
A charting library designed for interactive data visualization in F# scripting environments.
An R package for creating and exporting statistical animations to HTML, GIF, video, and PDF formats.
An R interface to Google's V8 JavaScript and WebAssembly engine for executing JavaScript code within R.
A comprehensive family of R packages for analyzing spatial point pattern data and other spatial data types.
A Python toolkit for text-focused data science on medium-sized datasets, bridging memory and cluster-scale processing.
A Python library for building lazy data processing and machine learning workflows that handle datasets larger than memory.
A Python package for automated univariate and bivariate data analysis and visualization to streamline machine learning workflows.
A Python package for exploring and analyzing data from your Home Assistant database.
A JupyterHub extension for publishing notebooks and apps as secure, interactive dashboards for non-technical audiences.
Archived R package for accessing open data from various government and scientific sources.
A Python library for logging ML metrics, parameters, and models in simple file formats, compatible with DVC and Git.
A command-line tool to view Jupyter notebooks directly in the terminal with customizable display options.
A Clojure library providing data-frames and arrays through Python's pandas and numpy.
A tool for data visualization and statistical analysis of threat intelligence indicator feeds to measure their quality and effectiveness.
A Python machine learning and informatics suite for analyzing, mining, and modeling chemical and materials data.
A Scala/Spark library for measuring fairness and mitigating bias in large-scale machine learning workflows.
A collection of examples demonstrating how to use Comet.ml for machine learning experiment tracking across various Python frameworks.
Analysis of High Frequency Trading patterns and strategies on Bitcoin exchanges using Jupyter notebooks.
A curated collection of academic papers, articles, and resources on credit scoring and credit risk modeling techniques.
A Python probabilistic programming framework for objective model selection in time-varying parameter time series models.
A practical guide to exploratory data analytics using Hadoop with Pig and Ruby for terabyte-scale data processing.
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