Feature Engineering
Showing 27 of 27 projects
Automatically extracts and selects relevant features from time series data for machine learning tasks.
An open-source Python library for automated feature engineering using Deep Feature Synthesis.
An open-source Python library for automated feature engineering using Deep Feature Synthesis.
A complete AI-driven process using GANs with LSTM and CNN to predict stock price movements, incorporating diverse data sources and hyperparameter optimization.
A machine learning library designed for human interpretability, featuring debuggable models and a feature transform language.
An Automated Machine Learning Python package for tabular data with feature engineering, hyperparameter tuning, explanations, and automatic documentation.
A lightweight Python library for creating portable, expressive, and testable data transformation DAGs with built-in lineage and metadata.
A Python library for defining portable, modular, and testable data transformation DAGs with built-in lineage and metadata.
A machine learning framework for developing high-frequency trading strategies using full orderbook tick data.
A Python library for feature engineering and selection with scikit-learn compatible transformers.
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
A software implementation of factorization machines for estimating interactions between categorical variables in large datasets.
A real-time AI lakehouse platform with a Python-centric feature store and comprehensive MLOps capabilities.
A Python framework for scalable time series forecasting using machine learning models, designed for production environments.
An open dataset and toolkit for training static PE malware machine learning models, featuring millions of labeled Windows executable samples.
A Ruby library for building and serving predictive models with support for PMML and integration with Python and R models.
A Python library that automates the tedious parts of exploratory data analysis with cleaning, feature engineering, visualization, and versioning.
An AutoML implementation and tutorial for automating machine learning pipelines on both static datasets and dynamic data streams, with a focus on IoT anomaly detection.
A Python library for generating high-quality synthetic tabular data using GANs, diffusion models, and large language models.
An R package that automates exploratory data analysis and data treatment with one-line reports and visualizations.
An open-source machine learning solution for the Home Credit Default Risk Kaggle competition, providing reproducible code and experiments.
An intelligent data search and enrichment library for machine learning that automatically finds and adds relevant external features to ML pipelines.
A Clojure library for machine learning and statistical inference designed for production deployment and composable algorithms.
A fast, sklearn-like feature processing library for Go that generates optimized transformers from struct tags.
A benchmark dataset with 3.2 million malicious and benign files across 6 file types for evaluating malware classifiers.
A Python feature engineering engine that internally manages past dependent values for continuous calculation of time-based features.
Feature generation code for the Kaggle Acquire Valued Shoppers Challenge, focusing on customer behavior prediction.
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