Feature Engineering Techniques: A Beginner’s Guide to Build Better ML Features

Updated on Oct 26, 2025

11 min read

Feature engineering is the critical process of transforming raw data into meaningful inputs (features) that significantly enhance machine learning (ML) models’ performance. By converting data, such as logs or text, into useful features like columns in a table or embedded vectors, you improve how effectively models learn patterns. This guide is designed for beginners eager to enhance their understanding of feature engineering and its impact on model success.

In this guide, you’ll explore:

The importance of feature engineering for ML success.
Different feature types and handling methods.
Beginner-friendly techniques, including imputation, encoding, scaling, extraction, and selection.
A structured workflow using tools such as pandas and scikit-learn.
Two detailed walkthroughs: predicting house prices and classifying customer churn.

Along the way, you’ll encounter short code snippets, comparison tables for categorical encodings, and essential links to authoritative resources for digital experimentation.

Why Feature Engineering Matters

Models derive their learning from patterns embedded in features. Transforming raw signals into informative features that reveal predictive structures often leads to better performance than merely switching to a more complex algorithm.
For example, a timestamp column may offer limited value compared to derived features like hour-of-day or whether a date falls on a holiday. These modifications can uncover patterns typically hidden from the model.
Feature engineering aids interpretability; domain-specific features (like customer tenure or purchase frequency) facilitate stakeholder comprehension of model outputs.
Moreover, engineered features clarify deployment needs, defining exactly what calculations are necessary in production.
However, deep learning models on extensive raw data (images, text, audio) can automatically learn features. Nonetheless, careful preprocessing and the choice of good features (like augmentations and embeddings) remain crucial.

Feature Types and Their Handling

Different feature types necessitate distinct treatments before they can be input to models.

Numerical Features

Continuous vs discrete: Scale continuous values (age, salary) as needed, while discrete counts (number of visits) require alternative handling.
Common issues include different scales (meters vs dollars), outliers, and missing values.

Categorical Features

Nominal (unordered) vs ordinal (ordered): Apply ordinal encoding when order matters (e.g., education level) and nominal encoding otherwise.
Consider cardinality: low-cardinality (few unique values) vs high-cardinality (many unique values like user IDs); high-cardinality features may require techniques like target encoding or hashing.

Datetime Features

Decompose timestamps into year, month, day, hour, weekday, and seasonal indicators.
For cyclical features, use sine/cosine transformations:

import numpy as np
hour = df['timestamp'].dt.hour
df['hour_sin'] = np.sin(2 * np.pi * hour / 24)
df['hour_cos'] = np.cos(2 * np.pi * hour / 24)

Text Features

Utilize bag-of-words, TF-IDF, or n-grams for basic techniques. Preprocess with tokenization, lowercasing, and possibly stopword removal or lemmatization.
For more advanced techniques, consider pretrained embeddings like word2vec or BERT. Beginners interested in embeddings should check this guide on working with language models.

Image and Sensor Features

Differentiate between pixel-level features (raw images) and learned features (CNN embeddings). Refer to this resource on camera and sensor technology for insight into feature selection influencing factors (noise, dynamic range).
Employ transfer learning (using pretrained CNN backbones) to extract embeddings rather than manually crafting pixel features when feasible.

Interaction and Aggregated Features

Feature crossings (for example, city × device type) and group aggregates (e.g., customer mean purchase value) often reveal hidden patterns absent in raw data.
Aggregations are vital for transactional/time-series data, converting event-level information into entity-level features like for customers or items.

Common Feature Engineering Techniques

Below are practical techniques with tips and examples. I link to scikit-learn’s preprocessing documentation for further information: Scikit-learn Preprocessing Docs.

1) Missing Value Handling (Imputation)

Basic: numeric → mean/median; categorical → mode or a new category labeled “missing”.
Advanced: KNN imputation and IterativeImputer (model-based). It’s advisable to add a missing indicator column to identify rows with imputed values.
Understand why data is missing (MCAR, MAR, MNAR) to avoid leaks of target information.

Example (scikit-learn pipeline):

from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer

num_imputer = SimpleImputer(strategy='median')
cat_imputer = SimpleImputer(strategy='constant', fill_value='missing')

preprocessor = ColumnTransformer([
    ('num', num_imputer, numeric_cols),
    ('cat', cat_imputer, categorical_cols)
])

2) Scaling and Normalization

Use StandardScaler (zero mean, unit variance), MinMaxScaler (0 to 1), or RobustScaler (handles outliers).
Scaling is important for distance-based models (KNN), regularized linear models, and gradient-based optimizers.

3) Encoding Categorical Variables

One-hot encoding suits low-cardinality nominal features.
Use ordinal encoding for ordered categories and target encoding (mean) for high-cardinality categories, ensuring to manage the risk of leakage through cross-validated smoothing.
For extremely high-cardinality features, consider the hashing trick for memory efficiency.

Encoding	Use When	Pros	Cons
One-hot	Low cardinality nominal	Simple, interpretable	Dimensionality explosion
Ordinal	Ordered categories	Preserves order	Assumes numeric spacing
Target Encoding	High-cardinality categories	Compact, predictive	Risk of target leakage
Hashing	Very high cardinality	Memory efficient	Collisions and less interpretable

Example of one-hot with scikit-learn:

from sklearn.preprocessing import OneHotEncoder
onehot = OneHotEncoder(handle_unknown='ignore', sparse=False)

4) Binning and Discretization

Convert continuous variables into bins (equal-width or quantiles) to reduce sensitivity to outliers and enhance model robustness.

Example (pandas):

df['age_bin'] = pd.qcut(df['age'], q=4, labels=False)  # Quartiles

5) Polynomial and Interaction Features

Use PolynomialFeatures or create pairwise interactions to allow linear models to capture non-linear relationships, watching for combinatorial explosions in dimensionality.

from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=2, interaction_only=True, include_bias=False)

6) Feature Extraction and Dimensionality Reduction

Consider PCA for numerical data, TruncatedSVD for sparse textual data, and autoencoders for non-linear compression.
Methods like t-SNE and UMAP are excellent for visualization but typically aren’t directly used as inputs for supervised tasks.

7) Text Feature Engineering

Basic techniques like Bag-of-Words and TF-IDF serve classic ML models; n-grams and character n-grams are useful for handling small texts.
For semantic comprehension, use pretrained embeddings. Beginners can enhance their skills in feature engineering through Kaggle’s course on Feature Engineering.

Example: TF-IDF with scikit-learn:

from sklearn.feature_extraction.text import TfidfVectorizer
vec = TfidfVectorizer(ngram_range=(1,2), max_features=5000)
X_text = vec.fit_transform(df['text'])

8) Time-Series and Lag Features

Create lag features (previous values), rolling aggregates, differences, and seasonal indicators, being cautious about data alignment to avoid leakage.

Simple lag creation with pandas:

df = df.sort_values(['customer_id', 'date'])
df['purchase_lag_1'] = df.groupby('customer_id')['purchase_amount'].shift(1)

9) Automated Feature Engineering

Tools like Featuretools leverage Deep Feature Synthesis to automate the generation of aggregates and transformation features. For an accessible introduction to DFS, check this overview on Deep Feature Synthesis.

10) Feature Selection Methods

Explore filter methods (correlation thresholding, variance threshold), wrapper methods (Recursive Feature Elimination), and embedded methods (L1 regularization, tree-based importances).
Conduct feature selection within cross-validation folds to prevent selection bias.

Practical Workflow & Tools

A structured feature engineering workflow promotes reproducibility and minimizes leakage.

Typical pipeline includes:

Exploratory Data Analysis (EDA): Examine distributions, missing data, and correlations.
Cleaning: Adjust data types, manage missing values, and eliminate duplicates.
Transformation: Apply scaling, encoding, and create new features.
Selection: Reduce feature dimensions and eliminate noise.
Modeling & Validation: Implement cross-validation and fine-tune hyperparameters.
Monitoring: Assess feature drift and model performance in production.

Utilize scikit-learn’s Pipelines and ColumnTransformer to encapsulate preprocessing and modeling, ensuring no training information is leaked into validation:

from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestRegressor

pipeline = Pipeline([
    ('pre', preprocessor),
    ('model', RandomForestRegressor())
])

Key tools for beginners:

pandas for data manipulation.
scikit-learn for preprocessing and modeling (check the preprocessing guide).
Featuretools for automated feature synthesis (see previous overview).
TSFresh for automatic extraction of time-series features.
spaCy and Hugging Face Transformers for text embeddings and NLP tasks (see the internal guide on embeddings).

Achieving reproducibility and a quality environment:

Track datasets and transformations via DVC or MLflow, or even version scripts.
If you’re setting experiments locally, consult the hardware guidelines here: Building Home Lab.
For Windows users wishing to utilize Linux tools, consider installing WSL and configuring it for your development: WSL Configuration Guide.

Simple Walkthroughs (2 mini-cases)

These mini-cases provide practical decisions along with code snippets to aid understanding.

Case A: House Price Prediction (Regression)

Goal: Predict house sale prices using tabular data (both numerical and categorical).

Steps:

EDA: Analyze missingness and skewness (e.g., sale price is frequently skewed).
Imputation:
- Numeric: use median imputation.
- Categorical: introduce a ‘missing’ category or use the mode based on semantics.
Transformations:
- Log-transform the target variable if skewed: y = np.log1p(y).
- Apply log transformations to skewed numeric features (e.g., living area).
Encoding:
- One-hot encode low-cardinality features such as ‘roof_style’.
- Utilize target encoding on high-cardinality features like ‘neighbourhood’, using K-fold smoothing.
Interaction Features:
- For example, compute living_area * num_rooms or age_of_house = year_sold - year_built.
Selection & Model:
- Train tree-based models (like RandomForest/GradientBoosting) and inspect feature importances.
- Prune features that show little importance.

Pseudocode pipeline:

# Simple sketch
preprocessor = ColumnTransformer([...])
pipeline = Pipeline([('pre', preprocessor), ('model', GradientBoostingRegressor())])

Quick wins in house price predictions can include log transformations, median imputations, and selecting a few interaction terms.

Case B: Customer Churn (Classification)

Goal: Predict whether customers will churn using transactional logs and account metadata.