Awesome GitHub RepositoriesFeature Engineering Tools

Scikit-learn is a machine learning library for predictive data analysis that provides a collection of algorithms for supervised and unsupervised learning. It functions as a comprehensive toolkit for data preprocessing, dimensionality reduction, and model selection, allowing users to classify data objects, predict continuous values, and cluster similar items based on historical patterns. The project is defined by a unified interface design where objects either learn from data, transform data, or chain these operations into sequential workflows. To ensure performance on large or high-dimensiona

This project is a Python-based framework that functions as a generative AI agent for programmatic data analysis. It enables users to interact with structured data sources through natural language prompts, translating these requests into executable code to perform analysis, data cleaning, and visualization. By maintaining conversational context across multi-turn interactions, the system allows for iterative exploration and the building of complex data narratives. The framework distinguishes itself through a robust semantic layer and secure execution model. It maps raw datasets to descriptive m

h2oGPT is a self-hosted platform designed for running large language models and executing retrieval-augmented generation workflows locally. It provides a comprehensive web interface that allows users to index private document collections into searchable databases, enabling context-aware question answering and summarization without exposing sensitive data to external services. The platform distinguishes itself by offering a modular architecture that supports both local model execution and connections to external inference servers. It facilitates the development of autonomous agents capable of

LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters

Vaex is a high-performance Apache Arrow DataFrame library and out-of-core data processing engine designed to handle billion-row tabular datasets in Python. It functions as a lazy evaluation framework that defers computations and transformations until results are required, enabling the processing of datasets that exceed available system RAM by mapping files directly from disk. The project distinguishes itself as a tool for big data visualization and exploration, specifically integrated for use within interactive notebooks. It provides specialized capabilities for machine learning feature engin

Aerosolve est un framework de machine learning conçu pour l'entraînement et le déploiement de modèles interprétables. Il fonctionne comme un outil d'ingénierie des caractéristiques (feature engineering) et un entraîneur de modèles utilisant la modélisation de caractéristiques creuses (sparse feature modeling) pour simplifier le débogage des poids et accélérer l'itération sur les données. Le système inclut un langage de transformation spécifique au domaine pour convertir des données brutes en représentations prêtes pour le modèle. Il offre également des capacités d'analyse de contenu visuel en mappant les images dans des espaces vectoriels denses de haute dimension pour classer et organiser les données par style ou par contenu. Le framework permet un entraînement centré sur l'humain en injectant des croyances a priori et des poids spécifiques dans le processus d'apprentissage. Pour le déploiement, il utilise un runtime d'inférence minimal pour exécuter des prédictions légères et un mécanisme de scoring à contexte partagé pour traiter plusieurs éléments en une seule opération.