Featuretools automatically creates features from
temporal and relational datasets
Deep Feature Synthesis
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.
Precise Handling of Time
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.
Reusable Feature Primitives
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.
Why use Featuretools?
Improve your existing workflow
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.
Accesible Python API
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.
Tweets from @featuretools_py
Featuretools v0.3.0 is out! We're particularly excited about this release because feature calculations now run 2x faster on average and over 10x faster in some cases. See all the changes in our documentation https://t.co/KhuRJ0Tvx4 pic.twitter.com/0rhFo4RBjd— Featuretools (@featuretools_py) August 28, 2018
Better features means better models. Check out this tutorial by @koehrsen_will on how Featuretools can help you automatically generate hundreds of potentially predictive features for your data. More demos here: https://t.co/htmqGFDnok https://t.co/2ni4M7eMSB— Featuretools (@featuretools_py) June 12, 2018