Feature Engineering: What Powers Machine Learning

This one line of code gives us over 200 features for each label in cutoff_times. Each feature is a combination of feature primitives and is built with only data from before the associated cutoff time. The features built by Featuretools are explainable in natural language because they are built up from basic operations. For example, we see the feature AVG_TIME_BETWEEN(transactions.transaction_date). This represents the average time between transactions for each customer. When we plot this colored by the label we see that customers who churned appear to have a slightly longer average time between transactions. In addition to getting hundreds of valid, relevant features, developing an automated feature engineering pipeline in Featuretools means we can use the same code for different prediction problems with our dataset. We just need to pass in the correct label times to the cutoff_times parameter and we'll be able to build features for a different prediction problem.