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Attend our Free Webinar on How to Nail Your Next Technical Interview


How To Nail Your Next Tech Interview

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Feature Engineering: Transforming Raw Data into Informative Features

Data is gold for every organization in the world. With the right data, any organization can embark on progressive growth and success. Machine learning algorithms play a major role in processing data for the best use. Every algorithm for machine learning uses a certain type of input data to produce outputs wherein this input data comprises features. Feature engineering is the process of using analytical or machine-learning techniques to transform raw data into desired characteristics. 

Here’s what we’ll cover in the article:

  • What is Feature Engineering in Machine Learning?
  • Processes of Feature Engineering in Machine Learning
  • Techniques of Feature Engineering for Machine Learning
  • Steps of Machine Learning: Feature Engineering Pipeline 
  • Tools for Feature Engineering
  • Kickstart Your Machine Learning Feature Engineering Journey!
  • FAQs on Feature Engineering

What is Feature Engineering in Machine Learning?

Model features comprise the inputs that machine learning (ML) models utilize to create forecasts during the inference and training phases. Accurate feature selection and composition are essential for ML model accuracy. A bad feature can directly impact the model regardless of the architecture or the data. Creating features may require a good deal of engineering effort. 

Feature engineering in machine learning 

A machine learning (ML) technique called feature engineering uses data to generate fresh variables that are not present in the training dataset. It can generate fresh features for each unsupervised and supervised learning, aiming to stream and accelerate data transformations while improving model precision. While interacting with machine learning models, feature engineering is essential.

The predictive model has predictor variables and an outcome variable, so the feature engineering process chooses the most effective predictor variables for the model. Productive feature engineering revolves around thoroughly understanding the business challenge and data from various sources. You gain a greater understanding of your data and greater analytical knowledge by developing new features. If implemented effectively, feature engineering is one of the most useful techniques in data science.

Processes of Feature Engineering in Machine Learning

Feature engineering consists of various processes: 

Feature Creation

The process of creating new features using domain expertise or discovering trends in the data is known as feature creation. Creating features includes generating new variables that will be most beneficial to our model. This can include introducing or eliminating features. It needs imagination and human interaction to complete this specific procedure. The current features have been combined by subtracting, adding, multiplying, and ratio, resulting in new derived features that have improved predictive accuracy.  


Feature transformation is the method of translating features into an increased acceptable illustration for a machine learning model. Transformation is the modification of the predictor variables to enhance the model performance. It additionally makes sure that the model is adaptable to accept input from different types of data. It ensures that every single variable is of a similar magnitude, making the model more understandable. 

Feature Extraction

Feature extraction is a computerized feature engineering method that creates novel variables by removing these variables from the original data. This stage aims to systematically compress the amount of data into an increased feasible collection for modeling. Feature extraction approaches include text analysis, edge detection, edge detection algorithms, cluster analysis and principal components analysis. It boosts the model's efficiency by adding additional and more significant features to the model, enabling it to discover more valuable trends in the data.

Feature Selection

Feature selection is a method of eliminating redundant, unnecessary, or excessive features from the initial feature set and picking a subset of the most essential features. It is an essential stage in the feature engineering cycle since it has a major effect on the model's effectiveness. It enhances comprehensibility by lowering the overall number of features, making the model's findings simpler to grasp and comprehend. There are three types of feature selection process: 

  • Filter-based method 
  • Wrapper-based method
  • Hybrid method


The benchmark model is the most practical, reliable, precise, and understandable model to compare your product. The effectiveness of various machine learning models, such as support vector machines, neural networks and near and non-linear classifiers, or other methodologies, such as bagging and boosting, are regularly evaluated using these benchmarks. 

Techniques of Feature Engineering for Machine Learning

There are certain kinds of feature engineering techniques that can be used in different algorithms and datasets, such as the following: 

Techniques used in feature engineering
  1. Imputation

Missing values are among the most common problems faced when it requires getting your data prepared for machine learning. Imputation is a method of dealing with missing values. It is intended for managing anomalies inside the dataset. There are two kinds of imputations: 

  • Numerical imputation: Numerical imputation is implemented to fill gaps in assessments or polls when particular data bits are unavailable. 
  • Categorical data imputation: Missing values in categorical data imputation could be replaced with the highest value that occurs in a column.
  1. Outlier Handling 

A method to eliminate outliers from the data set is known as outlier handling. This technique can be applied to various levels to provide more precise data representations. This phase must be carried out before beginning model training. The Z-score and standard deviation might be used for identifying outliers. There are certain ways to handle outliers: 

  • Removal: The distribution is cleaned up by removing items that contain outliers.
  • Replacing Values: Outliers can be interpreted as equivalent missing data and substituted with appropriate imputation.
  • Capping: Replacing the largest and smallest numbers with an arbitrary value or that comes from a variable range.
  • Discretization: It is the method of turning continuous variables, models, and functions into discrete variables, models, and functions. 
  1. One-Hot Encoding

One-hot encoding is a form of encoding wherein each member of a finite set is expressed by its index, with only one component having its index set to “1” and the remainder of the elements being given indices that fall within a specific range. It is a method that transforms categorical data into a format that machine learning algorithms can easily understand and use to produce accurate predictions. 

  1. Log Transform

The log transform is commonly used to convert a skewed distribution into a normal or less skewed one. We get the log of the values in a column and use those figures as the column in this change. It is used to deal with uncertain data, and the data develops into more close to standard applications.

  1. Scaling

Feature scaling represents one of the most prevalent and challenging aspects of machine learning. We require data with a predetermined set of standard attributes that can be adjusted upward or downward as necessary when training a predictive model. There are two standard methods of scaling: 

  • Normalization: In this process, all values are scaled between a particular range between 0 and 1.
  • Standardization: It is also known as Z-score normalization. It is the process of measuring values while considering their standard deviation.
  1. Binning

Among the key problems that affect the effectiveness of the model in machine learning is overfitting, which happens because of more parameters and inaccurate data. Binning is a method for converting continuously varying variables to categorical variables. In this procedure, the continuous variable's spectrum of values is divided into numerous bins, and each one gets allocated a category value.

Steps of Machine Learning: Feature Engineering Pipeline 

Here is an easy and visual representation of the feature engineering process.

A visual representation of the pipeline of feature engineering in machine learning

Tools for Feature Engineering

There are certain well-known libraries and tools that are used in the ML feature engineering process. Some of them are discussed below: 


Featuretools is a platform for performing automated feature engineering. It connects with the software you currently use to develop machine learning pipelines. Featuretools comes with a minimal function library that could be used to create features. One of the foremost fundamental elements of Featuretools is that it constructs features using deep feature synthesis (DFS). 


An excellent open-source feature engineering library is Autofeat. It automates feature creation, selection, and placement in an ordered machine-learning model. The Autofeat algorithm is a very basic one. You can choose the value units of the input variables in AutoFeat to avert the creation of technically unreasonable features.

Feature Selector 

Feature Selector is a Python library for selecting features. It is a modest library with only a few fundamental selections. The feature importance is calculated using 'lightgbm' tree-based learning techniques. Various visualization techniques are also included in the package, which might give you additional details about the dataset.

One BM

OneBM works directly with the unprocessed tables of a database. As it moves slowly along the relational tree, it integrates the tables in various ways. It distinguishes between basic data types (numerical or categorized) and intricate data types (sets of numbers, sets of classes, cycles, time series, and written content) in the combined results. Then, it uses pre-defined feature engineering techniques on the types that have been given.

Kickstart Your Machine Learning Feature Engineering Journey!

Featuring engineering is an important part of every machine learning cycle. It includes the initial phases of machine learning. Since every organization nowadays wants to access data over the internet for their growth, they are looking around for the best engineers for this purpose. Data scientists and machine learning engineers are the leading jobs in this sector. These engineers should be well-versed in the various phases of a machine learning model, including feature engineering. Interview Kickstart has always flourished to provide aspiring engineers with extensive content and prepare them for interviews with popular tech organizations. Sign up for our machine learning program today!

FAQs on Feature Engineering

Q 1. What is the difference between feature engineering and feature extraction?

Feature engineering is the process of transforming unstructured data into features and qualities that more accurately reflect it. Feature extraction is the procedure for converting raw data into the format of choice.

Q 2. What is feature engineering also known as?

Feature engineering is also known as feature discovery. 

Q 3. What is feature engineering in data science?

The process of selecting and transforming the most pertinent variables from unprocessed data while developing a predictive model with machine learning or statistical models is known as feature engineering in data science.

Q 4. Who is responsible for feature engineering?

Feature engineering is the job role of data scientists and machine learning engineers. 

Q 5. What is an example of feature engineering?

The examples of feature engineering would include datasets with categorical data, text, normalization data, continuous data, missing values and more.

Last updated on: 
October 27, 2023

Abhinav Rawat

Product Manager @ Interview Kickstart | Ex-upGrad | BITS Pilani. Working with hiring managers from top companies like Meta, Apple, Google, Amazon etc to build structured interview process BootCamps across domains

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