I spent last weekend staring at a dataset. It was a sprawling collection of customer information—transaction histories, sign-up dates, support tickets—spread across multiple, interconnected tables. My task was to predict which customers were likely to stop using the service. The raw data was just a pile of timestamps, IDs, and numbers. The real challenge, the part that consumes most of a data scientist’s time, was feature engineering: transforming that raw data into meaningful signals a model could use. It’s tedious, repetitive, and crucial. But what if we could automate that creative, time-consuming process? That question led me straight to a powerful Python library called Featuretools and its core algorithm, Deep Feature Synthesis.
The traditional approach to feature engineering is manual. We use domain knowledge to create features like “days since last purchase” or “average order value.” It works, but it’s slow and doesn’t scale. You might create a few dozen hand-crafted features. What if you could generate hundreds, or even thousands, of potentially useful features systematically? This is where automated feature engineering changes the game. It allows us to frame the problem differently. Instead of asking, “What features should I build?” we ask, “What are the entities and relationships in my data?” The library then handles the heavy lifting.
Think about a typical e-commerce database. You have a customers table, an orders table linked to customers, and an order_items table linked to orders. This structure is a goldmine for features. A human might think to calculate a customer’s total spend. Featuretools can do that, but it can also calculate the standard deviation of their order amounts, the day of the week they most frequently purchase, and the total weight of products they’ve ever bought—all by understanding how these tables connect.
Let’s see how this works in practice. First, we set up our data frames. Here’s a snippet creating a simple, synthetic dataset to illustrate the point.
import pandas as pd
import featuretools as ft
import numpy as np
# Create sample data
customers_df = pd.DataFrame({
'customer_id': [1, 2, 3, 4],
'join_date': pd.to_datetime(['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-20']),
'country': ['USA', 'UK', 'USA', 'Canada']
})
orders_df = pd.DataFrame({
'order_id': [101, 102, 103, 104, 105],
'customer_id': [1, 1, 2, 3, 4],
'amount': [54.99, 120.50, 29.99, 75.00, 200.25],
'timestamp': pd.to_datetime(['2023-03-01', '2023-03-15', '2023-03-10', '2023-04-01', '2023-04-05'])
})We have two tables: customers and their orders. The link is the customer_id. In Featuretools, we must first define these entities and their relationship. This step is like giving the library a map of your data universe.
# Initialize an EntitySet, which is a container for our data
es = ft.EntitySet(id="customer_data")
# Add the customers dataframe as an entity
es = es.add_dataframe(
dataframe_name="customers",
dataframe=customers_df,
index="customer_id", # The unique key for this table
time_index="join_date"
)
# Add the orders dataframe
es = es.add_dataframe(
dataframe_name="orders",
dataframe=orders_df,
index="order_id",
time_index="timestamp"
)
# Define the relationship: A customer can have many orders
es = es.add_relationship(ft.Relationship(
es["customers"]["customer_id"],
es["orders"]["customer_id"]
))Now for the magic. We use a function called dfs (Deep Feature Synthesis). It traverses the relationships we defined and applies operations called “primitives”—like sum, mean, count, or trend—to build features.
# Generate features automatically
feature_matrix, feature_defs = ft.dfs(
entityset=es,
target_dataframe_name="customers", # We want features for each customer
max_depth=2, # How many relationships to traverse
verbose=True
)
print(feature_matrix.head())What happens next is fascinating. The algorithm will create features like SUM(orders.amount) (total lifetime value), COUNT(orders) (number of orders), MEAN(orders.amount) (average order value), and LAST(orders.timestamp) (most recent order date). It builds these by looking “through” the customer to their related orders. The max_depth parameter controls complexity; increasing it can create features from deeper relationships. Have you ever considered how the time between a customer’s first and last order might predict their future behavior? The algorithm can.
But is more always better? Generating thousands of features can lead to overfitting and slow training. This brings us to a critical part of the workflow: feature selection. After generation, we need to filter. We might use correlation analysis with our target variable, or employ techniques like recursive feature elimination. The goal is to keep the signal and discard the noise.
# Example: Simple correlation filter (after encoding categoricals)
# Assuming 'target' is our label for churn
encoded_matrix = pd.get_dummies(feature_matrix)
correlations = encoded_matrix.corrwith(target).abs()
strong_features = correlations[correlations > 0.05].index.tolist()
filtered_matrix = encoded_matrix[strong_features]The real power emerges when you integrate this into a full machine learning pipeline. You can use Featuretools alongside Scikit-learn, creating a DataFrameMapper or a custom transformer to automate the entire process from raw data to model input. This makes your workflow reproducible and robust. Imagine starting with a fresh database snapshot and, with one script, generating a rich feature matrix ready for model training. How much time could that save your team?
It’s important to remember what this tool is not. It’s not a replacement for domain expertise. The initial setup—defining entities and relationships—requires human understanding of the data. It also works best on structured, relational data. It won’t automatically engineer features from images or raw text. Its strength is in turning transactional and time-series data into predictive gold.
So, why did this topic come to my mind? Because after that weekend of manual data wrestling, I realized we often get stuck in a cycle of incremental, manual improvement. Automated feature engineering is a force multiplier. It doesn’t think for you, but it exhaustively executes the logical building blocks of feature creation, freeing you to focus on strategy, interpretation, and model refinement. It’s about working smarter, not just harder.
I encourage you to try it on a familiar dataset. Start with a simple parent-child relationship, like customers and orders. Run dfs with max_depth=1 and see what it creates. You might be surprised by a useful feature you hadn’t considered. This approach has fundamentally changed how I approach new forecasting problems.
If you found this exploration helpful, please share it with a colleague who might be buried in manual feature creation. Have you used automated feature engineering in your projects? What was your experience? Let me know in the comments below—I’d love to hear about it and continue the conversation
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