What Is Feature Engineering? A Beginner’s Guide With Examples

Imagine sifting through a mountain of clues at a crime scene. Scattered notes, fingerprints, CCTV footage, victim statements, etc., but all of this is messy and unorganized. In the machine learning world, raw data is messy and uninformative. For the data to be useful, it should be transformed into something meaningful. 

In this example, we are taking a retail company that is analyzing customer purchases. The item codes and timestamps of purchases alone won’t give us anything useful. When you have engineering features such as “average spend per category” or “days since last purchase,” that is when the data can give you predictive insights. Feature engineering acts as the link between raw data and predictive models. In this article, 

What is Feature Engineering?

It’s the science of creating, transforming, and selecting features that improve the performance of predictive models. The algorithm doesn’t determine the success, but the quality of the data. More than 60 to 80 percent of a data scientist’s time is spent on data preparation, including feature engineering (Source: McKinsey). 

The core philosophy of feature engineering can be covered in the phrase,” Garbage in, garbage out.” When the input features are poorly structured or irrelevant, even advanced algorithms will find it difficult to find patterns or predict correctly. 

You can simplify the data journey in the following way:

Raw Data→ Data Preprocessing→ Feature Engineering→ ML Model.

Feature engineering makes the data speak clearly to the model, making it more effective. It will also be able to make better decisions.

Data Preprocessing

Before embarking on feature engineering, we must learn about its precursor: Data preprocessing. It is a set of operations performed on raw data to make it suitable for machine learning algorithms. It ensures that the data is clean and consistent, and in a format that can be used. 

Steps in Data Preprocessing:

Data Cleaning: 

The process handles missing values (eg, imputation, deletion), correcting inconsistencies (eg, standardizing formats, resolving contradictions), and removing duplicates. 

Example: Let’s say a dataset doesn’t have proper customer IDs or contains inconsistent product names. Data cleaning ensures the integrity of the data, thereby preventing errors. 

Data Normalization/Scaling: 

Algorithms perform better when numerical features are on a similar scale. Normalization (scaling to a range, eg, 0 to 1) and standardization (scaling to zero mean and unit variance) are commonly used techniques. It prevents features with larger values from dominating the learning process. 

Data Encoding: 

The categorical variables must be converted into a numerical representation understood by machine learning models. The popular techniques used are one-hot encoding or label encoding. 

Data Reduction: 

It is better to reduce the volume of data that is being handled. Do so while preserving as much information as possible. 

It can involve:

1. Feature selection: It chooses the features that are relevant to the analysis.
2. Dimensionality reduction: It reduces the number of features with a variety of techniques (Example: Principal Component Analysis).
3. Sampling: Only a representative subset of your data is considered. 

Data Validation: 

The final step involves validating your preprocessed data to meet the requirements for your analysis. 

It involves

• Checking data types
• Verifying value ranges
• Ensuring all the necessary features are present
• Checking for any inconsistencies or any remaining missing values

The above steps represent the core of the data preprocessing pipeline. The techniques and the order in which they are done might vary. It depends on the nature of the data and the requirements of the data science project. 

Data Preprocessing in Data Mining:

Here, the focus is on cleaning and transforming unstructured datasets (often large), to improve the quality of discovered patterns. It handles missing values, removes noise, reduces dimensionality, and integrates heterogenous data sources . This ensures that the mined results are accurate and meaningful.

Data Preprocessing in Machine Learning:

In this case, the preprocessing is model-centric, where it prepares data for training by formatting features, normalizing or scaling values, encoding categorical variables, and splitting datasets for evaluation. 

Even though data preprocessing in data mining and data preprocessing in machine learning are to improve the data quality, the former is driven by better insights and pattern discovery. On the other hand, machine learning preprocessing improves model performance and generalization. 

Preprocessing vs. Feature Engineering

Importance of preprocessing for high-quality features

• Improves model accuracy: Working with clean and well-structured data allows algorithms to learn patterns more effectively. This will result in better predictions and outcomes
• Reduces inconsistencies: It removes irrelevant or erroneous data, which helps in preventing misleading insights and model confusion
• Handles missing data: Techniques such as imputation or deletion ensure that gaps in data don’t hamper model performance
• Improves model convergence: Data that is normalized and scaled helps models train faster and more reliably. 
• Maximizes predictive power: When preprocessing removes inconsistencies or scales data, feature engineering gets to focus on extracting better and valuable insights instead of compensating for data flaws
  

Feature Engineering: The Art of Data Transformation

While preprocessing cleans your data, feature engineering takes it a step further. It creates new variables or modifies existing ones, uncovering hidden patterns and relationships that machine learning models can learn from. KDnuggets describes feature engineering as “the art and science of creating new variables or transforming existing ones from raw data to improve the predictive power.” 

It includes the following key techniques:

Feature Creation: 

It derives new variables from the existing ones and requires a deep understanding of the problem domain. For example, using a customer’s date of birth, an “age” feature can be created. You can arrive at BMI using weight and height. 

Feature Transformation:

Here, mathematical functions are applied to existing features to change their distribution or scale in a way that makes it more suitable for modeling. 

Here are a few common transformations: 

• Categorization: It converts numerical features into dummy or ordinal variables
• Log Transform: It applies a logarithmic function to highly skewed data. The objective is to make it more normalized
• Binning/Discretization: Converts continuous numerical features into categories to simplify relationships (For example, age can be binned into 0-18, 18-29, 30-50, etc.) 

Feature Extraction:

It creates new features by combining or projections of the original features to simplify data. It does this while retaining most of the important information. Feature extraction is data-driven. It reduces complexity, improves performance, and prevents overfitting. 

Feature Selection: 

It prepares data for machine learning and the main objective is to identify and keep only the input features that contribute the most to accurate predictions. Feature selection focuses only on the most relevant variables. It helps build models that are simpler, less prone to overfitting, and easier to interpret. 

Feature Scaling: 

It transforms the numerical features in a dataset to a common scale or range. In machine learning, different features of a dataset may have different ranges or units. When there is no scaling, the differences can lead to poor performance. Feature scaling ensures that all the features contribute equally to the model as it brings them to a similar scale. 

Proper Feature Engineering leads to:

• Better accuracy: The models can discover complex patterns with ease
• Reduced overfitting: It provides relevant features and removes irrelevant noise. Therefore, the model is less likely to memorize the training data
• Faster training: If there are fewer and more meaningful features, it means only less computational power is needed
• Enhanced interpretability: Features like “price_per_sqft” are easier for humans to understand than a model’s reliance on a complex interaction between raw “price” and “sqft”
  

Real-World Examples of Feature Engineering

To understand how advantageous feature engineering is, you must know its real-world applications. 

Tools for Feature Engineering

Scikit-Learn:

Scikit-Learn is an open-source machine learning library that offers several modules for feature engineering. It contains methods like OneHotEncoder and LabelEncoder that convert categorical variables to numerical variables. They also offer feature scaling methods such as StandardScaler and MinMaxScaler. 

Tsfresh:

A Python package, it automatically calculates a large number of time series characteristics, or the so-called features. Also, the package contains methods to evaluate the power of such characteristics for regression or classification tasks. 

Autofeat: 

It automates feature selection, creation, and transformation to improve linear model accuracy. 

Its two main tasks are:

• Feature Generation: It automatically creates non-linear features from the original data
• Feature Selection: Chooses the most relevant features using L1-regularized linear models. They make the process more efficient by selecting features contributing to predictive performance

Autofeat simplifies the feature engineering process as it makes it more accessible to practitioners without extensive domain expertise. 

Featuretools:

One of the most popular libraries for automated feature engineering, Featuretools supports a lot of functionalities, which include feature selection, feature construction, and using relational databases to create new features. It uses the deep feature synthesis (DFS) algorithm to build new features based on transformation and aggregation operations. 

Common Challenges in Feature Engineering

Data quality: 

In feature engineering, ensuring the quality of the data is a major challenge. Data quality means completeness, correctness, consistency, and relevance of the data under consideration. Poor quality data can lead to inaccurate or biased models, or might even prevent the models from learning at all.

Feature Selection:

Selecting the most relevant and informative features for the ML model is another challenge. Feature selection is the process of reducing the dimensionality of the data by removing irrelevant, redundant, or noisy features. 

Over-engineering: 

If there are too many features created (including weak/derived features), then the model will have too many parameters to learn effectively from the available data, increasing overfitting. 

Data Leakage:

When engineered features unexpectedly utilize future information or test set data, you leak the target and obtain overly optimistic results. Ensure that you build features only with the aid of training data. 

Need for domain knowledge: 

Good features require a deep understanding of the domain. If domain understanding is not there, it will rarely yield strong signals. 

Interpretability vs. complexity: 

When there are deep feature transformations, it can end up producing features that can be hard to interpret. In domains like healthcare or finance, where explainability matters, this becomes an issue. 

Best Practices for Feature Engineering

• On the baseline model, use only raw or minimally preprocessed features. Later, you can add new engineered features and measure their impact
• Work closely with subject matter experts. They would be able to direct you to the right derived variable
• Assess whether your engineered features are improving their performance on unseen data
• Use libraries like that of scikit-learn’s Pipeline to 
• Understand the problem that the model aims to solve, before applying feature engineering techniques
• Scale numerical variables, encode categorical variables, and apply mathematical transformations to enhance model performance
• Standardize your feature engineering pipeline since you will be collaborating with others

Future of Feature Engineering

Let’s look at what the future holds for feature engineering. 

Role of AutoML and AI-driven feature discovery:

• It looks like the tools and research are gearing toward automated feature engineering. In such a scenario, algorithms propose transformations and select features autonomously. A few automation frameworks have even been credited with reducing manual effort in feature creation and boosting accuracy. 
• Techniques that combine Large Language Models (LLM) with evolutionary search (like LLM-FE) are allowing domain knowledge and data-driven feedback to merge. 
• ELATE, a time series method, shows how context-aware automated feature engineering can yield significant results. 

Will feature engineering be fully automated?

While we will see significant progress in automation, expert intuition and creativity are still pivotal for tackling unique business problems. Even the most advanced symptoms only stand to gain from human guidance, interpretation, and troubleshooting. 

How TAL (Tredence Academy of Learning) Bridges the Skill Gap

TAL plays a crucial role in empowering Tredence employees to better their data science capabilities, particularly in data preprocessing and feature engineering. It enables them to stay ahead and be updated with evolving business needs. It does so by providing a structured learning path.

• They offer a hands-on module that covers Python, ML pipelines, data preprocessing, and feature design
• The case-based learning, where learners engineer features on real datasets
• Mentoring and project review sessions that help sharpen domain insight
• There is a huge focus on deployment, where you learn how to integrate feature pipelines into production systems

Conclusion

You might have the most powerful algorithm, but if you don’t have well-engineered features, its performance will be limited. On the other hand, even a simple model with meaningful features can outperform a complex model with poor features. Feature engineering rests on a solid foundation of data preprocessing, a discipline that is important for accurate data mining and robust machine learning. To build high-performing machine learning models, understanding feature engineering is essential. Using the right tools can streamline the process by a huge margin. 

Even though the tools to use and the level of automation keep evolving, the underlying principle remains the same: The quality of your inputs determines the quality of the outputs. When you understand this concept and creatively enhance your data, you will be able to find out what artificial intelligence is capable of. 

If you would like to take the next step in your data science journey, master feature engineering with real-world datasets. Check out the expert-led programs at Tredence Academy of Learning, and build job-ready ML skills (Source: Tredence). 

Tredence empowers its data science engineers through the Tredence Academy of Learning, fostering continuous growth in ML and feature engineering. Explore open roles on our Careers page to join this culture of learning (Source: Tredence)

FAQs

1. What is feature engineering for beginners? 

It transforms raw data into information that is relevant for use by machine learning models. It helps create predictive model features. With feature engineering, data quality is safeguarded, and attributes are transformed into features. 

2. What is an example of feature engineering?

Calculating “cost per sqft” from the total price and size in house price prediction is an example of feature engineering. 

3. What are the 4 main processes of feature engineering?

Feature creation, feature transformation, feature extraction, and feature selection, are the main processes of feature engineering. 

4. What is the main tool for feature engineering?

There is no single main tool for feature engineering. But if you want a few names, then pandas and scikit-learn are some of the most popularly used tools in feature engineering. 

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