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Learn AI with Python · Lesson

Automated Feature Engineering with Featuretools

EntitySet, DFS (Deep Feature Synthesis), feature primitives, stacking aggregations.

Automated Feature Engineering with Featuretools is a free Learn AI with Python lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Learn AI with Python learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Automating Feature Creation

Manual feature engineering is slow and easy to miss combinations. Featuretools automatically builds many candidate features from relational data using a method called Deep Feature Synthesis.

Relational Data and Entities

Real data often spans multiple related tables: customers, orders, products. Featuretools models these relationships and generates features that aggregate across the links automatically.

Creating an EntitySet

An EntitySet is the container that holds your dataframes and how they relate. Start by creating an empty one.

import featuretools as ft

es = ft.EntitySet(id="customers_data")

Adding DataFrames

es.add_dataframe registers a table. You specify its index (unique key) and optionally a time_index for temporal data.

import featuretools as ft

es = es.add_dataframe(
    dataframe_name="customers",
    dataframe=customers_df,
    index="customer_id",
)
es = es.add_dataframe(
    dataframe_name="orders",
    dataframe=orders_df,
    index="order_id",
    time_index="order_date",
)

Defining Relationships

Tell Featuretools how tables connect with add_relationship: a parent key links to a child foreign key (one customer has many orders).

es = es.add_relationship(
    "customers", "customer_id",
    "orders", "customer_id",
)

Deep Feature Synthesis

DFS (ft.dfs) is the engine. It stacks operations across relationships to create features like "average order value per customer" without you coding each one.

import featuretools as ft

feature_matrix, feature_defs = ft.dfs(
    entityset=es,
    target_dataframe_name="customers",
)

Aggregation Primitives

Aggregation primitives summarize child rows up to the parent: SUM, MEAN, COUNT, MAX, STD. They turn many orders into per-customer statistics.

feature_matrix, defs = ft.dfs(
    entityset=es,
    target_dataframe_name="customers",
    agg_primitives=["mean", "sum", "count", "max"],
)

Transform Primitives

Transform primitives operate within a single row or table: extracting month from a date, computing time since, or taking absolute values.

feature_matrix, defs = ft.dfs(
    entityset=es,
    target_dataframe_name="customers",
    trans_primitives=["month", "weekday", "time_since_previous"],
)

Controlling Depth with max_depth

max_depth limits how many primitives stack. Depth 1 is direct aggregations; depth 2 applies a transform then an aggregation (e.g. mean of the weekday of orders). Higher depth = more, but riskier, features.

feature_matrix, defs = ft.dfs(
    entityset=es,
    target_dataframe_name="customers",
    max_depth=2,
)

Using the Feature Matrix

The resulting feature_matrix is a clean dataframe indexed by the target entity, ready to feed into a model. feature_defs documents each generated feature.

print(feature_matrix.shape)
print(feature_matrix.head())

X = feature_matrix.fillna(0)
# now train any sklearn model on X

Strengths and Cautions

Featuretools rapidly produces many features and uncovers relational signals. But it can generate hundreds of columns, so pair it with feature selection. Watch for leakage by setting a proper cutoff_time with temporal data.

Quick Check

Test your Featuretools knowledge.

Recap

Recap: Featuretools automates feature engineering. Build an EntitySet, register tables with es.add_dataframe, define relationships, then run ft.dfs with max_depth. Aggregation primitives roll child rows up to parents; transform primitives work within a row. The feature_matrix is model-ready, pair it with feature selection.

Frequently asked questions

Is the “Automated Feature Engineering with Featuretools” lesson free?

Yes — the full text of “Automated Feature Engineering with Featuretools” is free to read here on the web, and the Learn AI with Python course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Learn AI with Python course, upgrade to CoddyKit PRO.

What will I learn in “Automated Feature Engineering with Featuretools”?

EntitySet, DFS (Deep Feature Synthesis), feature primitives, stacking aggregations. You practise Learn AI with Python with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Learn AI with Python?

No prior experience is required. Learn AI with Python on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Automated Feature Engineering with Featuretools” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Learn AI with Python lesson?

Yes. Every Learn AI with Python lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Feature Selection Methods
  2. Creating Interaction and Polynomial Features
  3. Target Encoding and Advanced Categorical Handling
  4. Automated Feature Engineering with Featuretools
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