Feature Engineering Toolkit: How feature_engine Supercharges Your Python Code

Level up your Machine Learning skills with these 10 essential feature_engine functions you need to know.

Feature Engineering | Image generated by AI. Google Gemini, 2025.

Feature engineering can make or break your machine learning model. You’ve probably heard the saying: “garbage in, garbage out.”

Well, there is a library called feature_engine that helps clean things up before your data ever hits a model. And it gets better: this package is scikit-learn–compatible and comes loaded with tools to transform, encode, and handle missing values in a simple, reproducible way.

In this post, we’ll walk through 10 of the best methods from Feature Engine that you can start using today.

Dataset

To apply this knowledge, we can load the dataset Attention, from Seaborn, which is open source under license BDS 3.

import pandas as pd
import seaborn as sns

# Load data
df = sns.load_dataset('attention', index_col=[0])
df.head(3)

And don’t forget to install Feature Engine.

pip install feature_engine --quiet

1. MeanEncoder (Target Encoding)