5.5.1 Feature Engineering Roadmap: Make Data Easier to Learn

Feature engineering is the work of making inputs useful, stable, and safe for models. Many model problems are actually feature problems.

Look at the Feature Flow First

understand columns→preprocess→construct→select→package as Pipeline

Step	First action
understand	list numeric, categorical, text, date, target columns
preprocess	scale, encode, fill missing values
construct	create ratios, counts, dates, interactions
select	remove useless or leaking features
pipeline	make preprocessing reproducible

Run One Pipeline

Create feature_first_loop.py and run it after installing pandas and scikit-learn.

import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler

X = pd.DataFrame(
    {
        "age": [22, 35, 47, 52, 28, 41],
        "city": ["A", "B", "A", "C", "B", "C"],
        "visits": [2, 6, 5, 9, 3, 7],
    }
)
y = [0, 1, 1, 1, 0, 1]

preprocess = ColumnTransformer(
    transformers=[
        ("num", StandardScaler(), ["age", "visits"]),
        ("cat", OneHotEncoder(handle_unknown="ignore"), ["city"]),
    ]
)

pipe = Pipeline([("preprocess", preprocess), ("model", LogisticRegression())])
pipe.fit(X, y)

print("pipeline_steps:", list(pipe.named_steps))
print("training_accuracy:", round(pipe.score(X, y), 3))

Expected output:

pipeline_steps: ['preprocess', 'model']
training_accuracy: 1.0

This tiny dataset is too small for real evaluation. The point is the workflow: preprocessing and model travel together.

Learn in This Order

Order	Read	What to practice
1	5.5.2 Feature Understanding	feature types, target, leakage risk
2	5.5.3 Data Preprocessing	scaling, encoding, missing values
3	5.5.4 Feature Construction	ratios, bins, dates, interactions
4	5.5.5 Feature Selection	remove noise, redundancy, leakage
5	5.5.6 Pipeline	reproducible preprocessing and training

Evidence to Keep

Keep this page’s proof of learning as a small evidence card:

Feature State

raw columns, types, missing values, scale, and target relationship

Transformation

preprocessing, construction, selection, or pipeline step

Output

transformed feature table, pipeline object, score change, or selected features

Failure Check

leakage, inconsistent train/test transform, high-cardinality trap, or meaningless feature

Expected Output

feature pipeline evidence with before/after and metric impact

Pass Check

You pass this roadmap when you can list feature types, build one preprocessing Pipeline, and explain why preprocessing outside the train/test workflow can cause leakage.

Check reasoning and explanation

1. Start by listing feature types, missing values, scale differences, categorical cardinality, and possible target leakage.
2. Preprocessing should live inside a Pipeline or ColumnTransformer so train and test data receive the same learned transformation without leaking information.
3. A useful feature change includes before/after evidence: transformed columns, score change, error sample change, or a reason to reject the feature.