Automate the Feature Engineering Pipeline for Your Relational Dataset

Essential guide to an open-source Python framework for automated feature engineering

Image by mohamed Hassan from Pixabay

Feature engineering is an important and time-consuming component of the data science model development pipeline. The feature engineering pipeline decides the robustness and performance of the model.

There are various automated feature engineering packages that process and create features for a single dataset. But these packages fail for the use-cases that involve the usage of multiple relational datasets. Merging multiple relational datasets and computing features from the same is a tedious and time-consuming task. In this article, we will discuss an open-source package Featuretools that automatically create features from temporal and relational datasets in a few lines of Python code.

Featuretools:

Featuretools is an open-source python framework to automate the feature engineering pipeline for the predictive modeling use-cases with temporal and relational datasets. Some of the key features of the Featuretools library are:

• Deep Feature Synthesis: Featuretools package offers DFS to automatically build meaningful features from a relational dataset.