{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/13"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"}],"enrichment":{"capability":"Featuretools automates feature engineering for machine learning by synthesizing new features from multi-table datasets using Deep Feature Synthesis (DFS), eliminating manual feature creation.","skillfed_tags":["feature-engineering","machine-learning","automated-ml"],"use_cases":["Predict customer behavior (e.g., next purchase) by automatically generating aggregated features from transaction history and customer metadata.","Build time-series features from event logs by automatically computing rolling statistics and temporal aggregations across related tables.","Rapidly prototype machine learning pipelines on new datasets by generating a baseline feature set without manual feature engineering.","Scale feature engineering to large datasets using Dask integration to parallelize DFS computation across multiple jobs.","Define domain-specific features via custom primitives when built-in aggregations don't capture the business logic you need."],"what_it_does":"Featuretools is a Python library that automates feature engineering\u2014the traditionally manual process of creating predictive features from raw data. It implements Deep Feature Synthesis (DFS), an algorithm that automatically generates features by aggregating and transforming values across related tables in a multi-table dataset. You define the structure of your data (entities and relationships), point DFS at a target table, and it produces a feature matrix ready for machine learning, handling temporal aggregations, counts, statistics, and custom transformations.\n\nThe library comes with built-in primitives for common feature types (sum, mean, mode, date extraction, etc.) and allows you to define custom primitives for domain-specific transformations. It integrates with standard data science tools\u2014pandas for data handling, numpy and scipy for computation, and optional Dask support for parallel execution. The package is actively maintained, production-stable, and widely used in data science workflows where multi-table relational data needs rapid feature exploration.","worth_installing":"Yes. Featuretools is worth installing if you work with multi-table relational data and want to automate feature engineering. It has low install friction, active maintenance, no known vulnerabilities, permissive licensing, and a mature API. Install it if you're prototyping ML pipelines or need to scale feature generation; skip it if your data is already a single flat table or if you prefer manual feature control."},"id":"featuretools","links":{"html":"https://skillfed.io/packages/featuretools","md":"https://skillfed.io/packages/featuretools.md","pypi":"https://pypi.org/project/featuretools/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2024-05-14","license_spdx":null,"license_treatment":"permissive","name":"featuretools","python_support":"supports_current","summary":"a framework for automated feature engineering"},"popularity":{"monthly_downloads":206281,"position":9575,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.31.0"}