Data Science
Showing 36 of 252 projects
A Python library for exploratory analysis, diagnostics, and visualization of Bayesian models.
A curated collection of academic papers on data mining and machine learning techniques for fraud detection across various domains.
Create blogs and websites with R Markdown, integrating dynamic R code, graphics, and technical writing elements.
A meta-package for installing and loading core R packages for data science that share common design principles.
A collection of R packages for data science that share common design principles and work together seamlessly.
An open-source Python toolkit providing a comprehensive collection of algorithms for interpreting and explaining machine learning models and datasets.
A lightweight MongoDB schema analyzer that reveals document structure, field frequencies, and data outliers.
A comprehensive R package that embeds Python within R sessions, enabling seamless interoperability between the two languages.
A native R kernel for Jupyter notebooks, enabling R programming within the Jupyter ecosystem.
Python code and examples for Bayesian statistics from the book 'Think Bayes: Bayesian Statistics Made Simple'.
A unified interface and infrastructure for machine learning in R, supporting classification, regression, clustering, and survival analysis.
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
Hyperopt-sklearn automates hyperparameter optimization and model selection for scikit-learn machine learning pipelines.
Python implementation of the Boruta all-relevant feature selection method with scikit-learn compatibility.
A satirical programming language designed to mock enterprise software development culture with intentionally cumbersome syntax and corporate jargon.
A Go machine learning library with online learning capabilities and a variety of implemented models.
A curated reading list and syllabus for a Stanford discussion class on applied data science topics.
A curated collection of 60 ChatGPT prompts for data science tasks, from model building to code explanation.
A JupyterLab extension for version control using Git, enabling Git operations directly within the JupyterLab interface.
A Python package for concise, transparent, and accurate predictive modeling with sklearn-compatible interpretable models.
A deprecated repository for community-contributed Keras extensions like layers, activations, and loss functions.
An open-source Python repository providing around 40 feature selection algorithms for machine learning applications.
A Python library that automatically extracts schema, statistics, and sensitive entities (PII/NPI) from datasets.
A Python framework for building real-time data pipelines and event-driven microservices on Apache Kafka using a Streaming DataFrame API.
Interactive maps in Jupyter notebooks using Leaflet.js with Python bindings.
A Python library for agile data preparation workflows that works with Pandas, Dask, cuDF, Dask-cuDF, Vaex, and PySpark.
A curated, categorized directory of packages, libraries, and resources for the Julia programming language.
A comprehensive Python library for generating and analyzing multi-class confusion matrices with extensive statistical metrics.
Python library providing clean, chainable functions for data cleaning and manipulation with pandas DataFrames.
A Python library for time series forecasting using scikit-learn compatible machine learning models.
A Python library for time series forecasting using scikit-learn compatible machine learning models.
A suite of high-performance command line tools for filtering, summarizing, joining, and manipulating large tabular data files.
A model-agnostic toolkit for exploring and explaining the behavior of complex machine learning models in R and Python.
A Python interface for the igraph library, enabling fast creation, manipulation, and analysis of large graphs and networks.
An R package that converts R functions into web APIs using special code annotations.
A deep learning framework for Julia with GPU support and automatic differentiation using dynamic computational graphs.
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