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 ist ein Machine-Learning-Framework für das Training und Deployment interpretierbarer Modelle. Es dient als Tool für Feature Engineering und Modelltraining, das auf Sparse-Feature-Modellierung setzt, um das Debugging von Gewichten zu vereinfachen und die Dateniteration zu beschleunigen. Das System enthält eine domänenspezifische Transformationssprache, um Rohdaten in modellfertige Repräsentationen zu überführen. Zudem bietet es Funktionen zur visuellen Inhaltsanalyse, indem Bilder in dichte, hochdimensionale Vektorräume abgebildet werden, um Daten nach Stil oder Inhalt zu ordnen und zu klassifizieren. Das Framework ermöglicht ein menschenzentriertes Training, bei dem Vorwissen und spezifische Gewichte direkt in den Lernprozess einfließen können. Für das Deployment nutzt es eine minimale Inference-Runtime für leichtgewichtige Vorhersagen sowie einen Shared-Context-Scoring-Mechanismus zur Verarbeitung mehrerer Elemente in einem einzigen Vorgang.