#Feature-engineering
Showing 60 of 129 repositories tagged #feature-engineering, ranked by stars
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
An open source python library for automated feature engineering
Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Book_6_《数据有道》 | 鸢尾花书：从加减乘除到机器学习；欢迎大家批评指正！纠错多的同学会得到赠书感谢！
:book: [译] 面向机器学习的特征工程
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning
Feature engineering and selection open-source Python library compatible with sklearn.
The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
Feathr – A scalable, unified data and AI engineering platform for enterprise
Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering
Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.
OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.
[UNMAINTAINED] Automated machine learning for analytics & production
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
Unified multimodal backend for AI data apps
Automated Time Series Forecasting
Hopsworks - Data-Intensive AI platform with a Feature Store
Automated Deep Learning without ANY human intervention. 1'st Solution for AutoDL challenge@NeurIPS.
NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
An intuitive library to extract features from time series.
Welcome to the Data Science EBooks repository! This collection offers a variety of high-quality ebooks on Data Science, Machine Learning, and AI. Perfect for both beginners and advanced learners, explore these resources to deepen your knowledge and skills.
LAMA - automatic model creation framework
A scalable general purpose micro-framework for defining dataflows. THIS REPOSITORY HAS BEEN MOVED TO www.github.com/dagworks-inc/hamilton
EvalML is an AutoML library written in python.
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
Feature exploration for supervised learning
Code for Kaggle Data Science Competitions
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
BharatMLStack is an open-source, end-to-end machine learning infrastructure stack built at Meesho to support real-time and batch ML workloads at Bharat scale
Serverless Machine Learning Course for building AI-enabled Prediction Services from models and features
Features selector based on the self selected-algorithm, loss function and validation method
Mobile app for planning tasks for the day with multimodule architecture, MVI, Compose, Room, Voyager, AlarmManager, Notification, Charts
Complete-Life-Cycle-of-a-Data-Science-Project
Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning
A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning
We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.
DeltaPy - Tabular Data Augmentation (by @firmai)
A unified interface for optimization algorithms and experiments
Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
A collection of demos showcasing automated feature engineering and machine learning in diverse use cases
Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.
Open solution to the Home Credit Default Risk challenge :house_with_garden:
Flexible time series feature extraction & processing
Code repository for the online course Feature Engineering for Machine Learning
本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs
FeatHub - A stream-batch unified feature store for real-time machine learning
Data Science Feature Engineering and Selection Tutorials
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Fast, high-quality forecasts on relational and multivariate time-series data powered by new feature learning algorithms and automated ML.
Feature Engineering and Feature Importance in Machine Learning for Financial Markets
A detailed summary of "Designing Machine Learning Systems" by Chip Huyen. This book gives you and end-to-end view of all the steps required to build AND OPERATE ML products in production. It is a must-read for ML practitioners and Software Engineers Transitioning into ML.
Semi-automatic feature engineering process using Language Models and your dataset descriptions. Based on the paper "LLMs for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering" by Hollmann, Müller, and Hutter (2023).
AI-powered NBA game outcome predictor that uses advanced team stats and trend-based features to forecast winners and track model performance