Featuretools uses DFS for automated feature engineering. You can combine your raw data with what you know about your data to build meaningful features for machine learning and predictive modeling.
Featuretools provides APIs to ensure only valid data is used for calculations, keeping your feature vectors safe from common label leakage problems. You can specify prediction times row-by-row.
Featuretools comes with a library of low-level functions which can be stacked to create features. You can build and share your own custom primitives to be reused on any dataset.
Featuretools works alongside tools you already use to build machine learning pipelines. You can load in pandas dataframes and automatically create meaningful features in a fraction of the time it would take to do manually.
With several demo applications, extensive documentation and community support on Stack Overflow, getting started with Featuretools is easier than ever. Take a look at the Demos page to get started.
Wow, it's been 1 year since we open sourced @featuretools. I'm so proud of the @feature_labs team and the Featuretools community who have helped it mature into the most popular library for automated feature engineering. From one year ago: https://t.co/KHVL6rIICg.
Get the latest tutorials, releases, and demos!
Featuretools is an open source framework for automated feature engineering created by Feature Labs. If building meaningful predictive models is something you care about, please get in touch.