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Feature Engineering

Feature engineering is the process of creating, selecting, and transforming input features to improve the performance of machine learning models. It includes techniques such as feature extraction, feature selection, encoding categorical variables, scaling numerical features, and generating new features from existing data. Effective feature engineering helps models capture meaningful patterns, improve predictive accuracy, and generalize better to unseen data.

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nni

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

  • Updated Jul 3, 2024
  • Python

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.

  • Updated Aug 10, 2026
  • Jupyter Notebook

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

  • Updated Aug 27, 2022
  • Jupyter Notebook

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

  • Updated Jun 2, 2026
  • Scala