What is Feature Engineering in Machine Learning: Steps, Techniques, Tools and Advantages

What is Feature Engineering for Machine Learning?

Feature engineering is a crucial aspect of data science and machine learning. Feature engineering in data science involves extracting, transforming, and creating meaningful features from raw data in order to enhance machine learning models’ performance. Properly engineered features can significantly increase model accuracy, generalization, and effectiveness.  

Need for Feature Engineering in Machine Learning

Data can often contain noise, missing values and irrelevant information, which hinder model performance. Feature engineering machine learning addresses this by turning raw data into meaningful features that provide essential information for the model and decreasing dimensionality to make its computation more manageable and computationally efficient.

Feature Engineering Steps

Following are the feature engineering steps:

  • Data Cleaning and Preprocessing: Data cleansing and preprocessing are integral first steps of feature engineering. Addressing missing values by imputing or removing them to eliminate bias. Eliminating outliers to ensure fair treatment during model training.
    Normalizing data scale features to a standard range and normalizing them with the data facilitates fair treatment during training and convergence, increasing data quality and consistency, thereby setting the stage for more efficient feature engineering.
  • Feature Selection: Selecting the most pertinent features from an available set is essential to selecting an effective model since irrelevant or redundant features can lead to overfitting or reduced model performance.
    Techniques such as correlation analysis can assess their relationship to target variables, while recursive feature elimination involves iteratively eliminating less important ones. Selecting essential features also reduces model complexity while improving generalization and decreasing computational burden.
  • Feature Transformation: Transforming features can improve model performance by altering their representation. Log transformation can reduce the impact of skewed distributions by making data more symmetric; Min-Max scaling or Z-score normalization brings all features to an equal scale, preventing certain features from dominating.
    Feature-transformed data are better suited for various machine learning algorithms leading to improved convergence and performance.
  • Feature Creation: Feature creation is an art that involves deriving new features from existing ones or domain knowledge, often by drawing parallels between the original data and existing features that do not capture important patterns or relationships that might otherwise not be apparent, such as creating “total square footage” features from individual room areas for better housing price predictions.
    Mathematical operations, domain expertise or interactions between features may lead to insightful new attributes that improve model predictions – feature creation is thus one key driver of model performance improvement.

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Examples of Feature Engineering

To illustrate the concept of feature engineering, let’s consider an example in which house prices are predicted with machine learning. Our dataset contains characteristics like bedrooms and bathrooms per house, age and location information, and selling price data for houses for prediction.

  • Total Square Footage: Total square footage is essential to predicting house prices. Instead of considering individual room areas separately, we can aggregate them all and create a new feature called total square footage – larger properties often demand higher prices, so this aggregated feature could greatly strengthen a model’s predictive power.
  • Price per Square Foot: While total square footage provides useful data, it may not adequately capture the market’s pricing dynamics directly. Therefore, another feature can be created that helps capture such dynamics: price per square foot. By dividing the selling price by total square footage, we gain a metric representing property price per unit area which could help the model understand relative price trends among neighborhoods and property types.
  • Age of the House: House age can often play an influential role in its value, so instead of using raw age data alone as our indicator feature, we can create a “house age category.” For instance, houses could be divided into “old,” “middle-aged,” and “new.” Creating this categorical representation helps the model more efficiently identify any changes related to house age that affect its price more accurately.

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Feature Engineering Techniques for Machine Learning

Following are the techniques used in Feature Engineering for Machine Learning:

1. Imputation

Imputation is an essential way of handling missing values in datasets. Missing values are common and can negatively impact model performance if left unaddressed. Using meaningful approximations derived from existing data sources, imputation attempts to preserve as much valuable information as possible while eliminating bias. Based on the nature of the dataset used, for example, mean, median mode imputation or K-nearest neighbors imputation may be employed depending on its structure and purpose.

2. Handling Outliers

Outliers are extreme data points that differ significantly from the rest of the data set and may adversely impact model performance and predictive accuracy. Handling outliers involves identifying these data points and making decisions on whether to remove, transform or treat them as special cases; techniques like Z-Score (z-score is for zero) Interquartile Range (IQR) tests can help detect and handle them appropriately, preventing them from altering predictions in models.

3. Log Transform

Logarithmic transformation can help features with highly skewed distributions, particularly those with long tails toward higher values, to better match those assumed by machine learning algorithms that assume normal distributions. By applying a log transform, data are scaled down until their distribution becomes more symmetric, helping reduce extreme values while increasing robustness in models.

4. Binning

Binning is grouping continuous numerical data into discrete bins for analysis. This technique can help transform continuous into categorical features when dealing with nonlinear relationships between target variables and source data or when specific ranges exist that capture patterns rather than using exact numerical values as indicators for categorization purposes. Binning can also help support models which benefit from treating data at intervals differently as separate categories.

5. Feature Split

A feature split involves breaking apart information from one feature into multiple, usually to extract useful pieces of data from strings or composite attributes such as dates. For instance, splitting out these different components as individual features can improve model understanding by providing more granular insights than was available with its original feature.

6. One-Hot Encoding

This technique converts categorical variables to binary vectors compatible with machine learning algorithms that require numerical input.
Under one-hot encoding, each category in the original feature is represented as a binary column where 1 indicates the presence and 0 absence – this prevents any ordinal relationship from being assigned between categorical data points, thus eliminating bias from predictions made by models using categorical data sets. and you can learn them through the Advanced Certificate Programme in Machine Learning & NLP from IIITB.

Feature Engineering in ML Lifecycle

Lifecycle Feature engineering should not be undertaken once-off. Still, it should instead occur continuously throughout the machine learning lifecycle as models evolve, thus increasing requirements for relevant features.

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Tools for Feature Engineering

Several libraries and tools exist to aid feature engineering in Python, such as Pandas, NumPy, and Scikit-learn; these provide capabilities to manipulate and transform data efficiently. We will take a brief look at them below:

  • Panda is an open-source library used for data manipulation and analysis. Its DataFrames and Series collection, which allows data scientists to manage large datasets efficiently and functions that clean data, handle missing values efficiently, and create new features using grouping/merging/pivoting, makes Pandas an indispensable asset in feature engineering workflows.
  • NumPy is the essential library for numerical computing in Python, providing users with a powerful array of objects and an extensive collection of mathematical functions for efficient numerical operations. NumPy’s array manipulation capabilities make it particularly helpful in feature engineering tasks as it transforms and reshapes data efficiently, while its functions have been optimized to perform at maximum performance – making NumPy an indispensable resource when handling large-scale data and numerical calculations during feature engineering projects.
  • Scikit-learn is a widely utilized machine learning library offering various data preprocessing and feature engineering tools. These include utilities for scaling features, encoding categorical variables, handling missing values and selecting features. Scikit-learn’s user-friendly interface integrates feature engineering easily into machine learning pipelines; additionally, various transformers and preprocessing techniques enhance its effectiveness as a feature engineering tool.
  • Feature Tools is a library built specifically to assist data scientists in automating feature engineering.
    It works by automatically creating new features from raw data using specified relationships and aggregation functions, using deep feature synthesis techniques for meaningful feature generation – making Featuretools an invaluable tool when working with high-dimensional datasets that require deep feature synthesis techniques to produce meaningful ones.
  • Automating feature engineering saves both time and effort for data scientists while guaranteeing relevant features are created.
  • TPOT is an automated machine learning library with automated feature engineering capabilities, using genetic algorithms to find the optimal combination of features and algorithms that optimize model performance. This platform also handles data preprocessing, feature selection and engineering – making TPOT an invaluable tool for novice and experienced data scientists.

Advantages and Drawbacks of Feature Engineering

Following are the advantages and disadvantages of Feature Engineering:

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Enhancement in model performance and accuracy. Improved generalization to new data. Decreased overfitting and improvement of model interpretability are among its numerous advantages.


Feature engineering can be time-consuming and cumbersome for large datasets, and domain expertise must be used to produce meaningful features. There may also be risks of bias introduced during the feature engineering process.


Feature engineering is integral to machine learning by providing data for models to utilize and improving their predictive abilities. This process involves carefully combining domain expertise, creativity and analytical abilities to extract the most important insights from raw data sources. Data scientists can utilize appropriate techniques to transform raw data into formats that enable machine learning models to make accurate and insightful predictions. You can comprehend these via Master of Science in Machine Learning & AI from LJMU.


Which are some common feature engineering techniques used in machine learning?

Examples include imputation, handling outliers, log Transform, binning feature split and one-hot encoding.

Are any automated or semi-automated methods available for feature engineering?

Absolutely - automated feature engineering tools such as Featuretools and TPOT can assist in automatically creating features or suggesting potential ones for consideration.

Do you have any best practices or guidelines for feature engineering?

Some best practices of feature engineering include understanding the data domain, conducting exploratory data analysis, handling missing values appropriately, and validating engineered features' impact on model performance.

How can I identify and select relevant features for my machine-learning model?

Various feature selection techniques, such as correlation analysis, recursive feature elimination, and feature importance scores from tree-based models, can help identify the most pertinent features for your model.

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