Today, every business sector uses machine learning algorithms to transform data into information. In data science, the extraction of relevant data to generate precise results is crucial. Machine learning algorithms require efficient methods to train and extract the data. Feature engineering is a process that converts raw data into features (variables) to enhance the precision of data models. Let’s understand what is feature engineering in data science in this article.

Table of contents:

What is Feature Engineering in Data Science?

Components Involved in Feature Engineering in Data Science

Feature Creation

Feature Transformation

Feature Extraction

Feature Selection

Steps Involved in Feature Engineering in Data Science

● Data Preparation

● Exploratory Data Analysis (EDA)

● Benchmark

Feature Engineering Techniques in Data Science

Imputation (Handling Missing Values)

Label Encoding and One Hot Encoding:

Polynomial feature creation -

Logarithmic Transform -

Bucketing or Binning -

Example of Feature Engineering in Data Science

Kickstart Your Career with Feature Engineering in Data Science

FAQs on Feature Engineering in Data Science

What is Feature Engineering in Data Science?

The predictive models in machine learning need a set of inputs and data points to train the data models. In order to train data models for accurate results, the training set needs to have new data points and features. Efficient features will generate the desired outcomes in a statistical model using different architectures and algorithms. 

The feature engineering process helps the machine learning models predict accurate outputs by creating new input variables (features) from raw and existing data. Feature engineering is a method that selects, converts, manipulates, and combines existing or raw data into variables or features. Feature engineering for machine learning is one of the best techniques used to optimise the data models if executed correctly.

Alt text: Understanding what is feature learning in data science

In machine learning, a feature is a statistically measurable quantity of a data point that is extracted from the already existing data. This quantity is used as an input variable for machine learning data models to learn and predict accurate results. The new features (variables or attributes) help you gain better insight into the predictive model and provide a structure to overcome business challenges with machine learning.

Components Involved in Feature Engineering in Data Science

Now that we know what is feature engineering in data science. Let’s explore the different processes and components involved in feature engineering to create new features for better-predicted results:

Feature Creation

The process of feature creation requires the identification of the most crucial features in a dataset that can be helpful for predictive models. It is a process of creating new variables or features based on pattern observation and recognition and domain-centric knowledge by adding, selecting, removing, or manipulating the existing data. The derived features from the existing variables are then used to increase the predictive accuracy of the data model. The feature creation may include different data-driven techniques like encoding, splitting, feature calculation, binning, etc.

Feature Transformation

Feature transformation is a process where a function transforms the features from one form to another for better predictive model representation. It can be done by removing or replacing the missing number of features or variables to make the predictive model suitable for swift data training. It enhances the accuracy of machine learning models by removing redundant features and attributes and minimises the error possibilities as all variables are under the manageable data model range.

Feature Extraction

Feature extraction is the process of creating and extracting new features or variables from the raw or existing data to provide a detailed understanding of data models. The process involves the creation of new features with useful data without interrupting or manipulating the significant relationships and information. It can decrease the volume of data automatically and make the dataset in the data model more efficient and handleable. Feature extraction can include feature transformation, dimensionality reduction with different algorithms, and much more.

Feature Selection

The process of feature selection in data science feature engineering analyses the data in various aspects to deduct the irrelevant data from the dataset. The unnecessary features of the model are removed, and relevant variables that are useful to enhance the training speed and precision of the predictive models should be given priority. The important information from the dataset is put into a subset without adding the redundant data. The types of feature learning may include wrapper, filter, and embedded methods.

Steps Involved in Feature Engineering in Data Science

Different data scientists have different approaches to executing feature engineering into the data models. Some of the common steps involved in feature engineering are as follows:

• Data Preparation

The data preparation involves organising, preparing, manipulating, and extracting raw data from various sources and converting it into a standardised format. The organisation and preparation of the data can include cleansing the data by removing errors in the dataset, using data augmentation, loading, inserting, or fusing the data.

According to the survey conducted by CrowdFlower, Scientists spend about 80% of their work time on cleaning, organising, and collecting data.

‍

• Exploratory Data Analysis (EDA)

EDA is a simple yet powerful tool that is used to recognise the prime characteristics of a selected data set with the help of data analytics and investigation. Data scientists use different properties and data visualisation tools for a large amount of data to find new patterns and behaviours that have not been observed previously. It can help to identify new variables and increase the data model accuracy.

• Benchmark

As the name suggests, the purpose of the benchmark method is to set an accuracy standard baseline, where all the features and variables are compared on the basis of this standard. The method is used to increase the predictability of the model by reducing any computational errors or redundant features. 

The good practice for data scientists is to run and test the new datasets and see if the predictive machine learning model fits the standard set by the benchmark format.

Feature Engineering Techniques in Data Science

In order to implement the processes and simplify the concept of “what is feature engineering,” one must know the different techniques used in feature engineering to implement the processes and extract new features by combining, manipulating, and bending existing raw data. There are several commonly used techniques of feature engineering, which are as follows:

Alt Text: Feature engineering techniques in machine learning

Imputation (Handling Missing Values)

In machine learning, one issue observed while preparing data for predictive models is missing values due to computational or human-generated errors, data flow obstacles, invasion of data privacy, etc. The imputation technique handles the missing data by filling in missing values. Two imputation methods to fill the missing values in the dataset are:

• Numerical Imputation Technique: The technique replaces the missing values and gaps with an approximate numerical value based on different information sources. Numerical imputation imputes the value with the help of mean, median, and rounded mean.
• Categorical Imputation Technique: Here, the missing value is replaced with the highest value available in the categorical column. The string “missing” can be used to replace the gaps of missing values in categorical variables.

Example:

from sklearn.impute import SimpleImputer 
data_imputed = SimpleImputer(strategy='mean').fit_transform(data)

Label Encoding and One Hot Encoding

In the dataset, a categorical value is represented by an integer. To make the predictions better and to give features better representation, we can split the categorical variables into a column of its own, where 1 represents the class the row it belongs to, and 0 is used to fill the remaining columns. This creates multiple new variables from a single data row or source, and each new variable can help in better accuracy. This process is known as one-hot encoding. Label encoding involves integers, including 0 and 1, to categories in order to convert them to number format.

Example:

		# code snippet:	
  
    from sklearn.preprocessing import OneHotEncoder  
    encoding = OneHotEncoder(sparse_output=False, categories=categories)
  

Polynomial feature creation

Some datasets feature mostly linear relations, which are easily captured by one-dimensional variables. The problem arises when the data is non-linear; in the real world, linearity is a rare occurrence. For a better approximation, creating polynomial features comes in handy in these situations. Polynomial features introduce curves that fit the data much better and reduce the bias of the model by making it more capable of approximating the underlying data distribution. 

Example:

	from sklearn.preprocessing
  import PolynomialFeaturespoly = PolynomialFeatures(degree = 2)
  poly.fit_transform(Your data)

Logarithmic Transform

Most of the time, data can be unsymmetrical, and variables and the underlying distribution can be skewed. A log transformation fixes the distribution of a numerical feature by using the logarithm function on the feature. This operation also makes the variance more stable in a dataset by reducing the range of the data points, making the computation process much more efficient and faster. Log transform improves interpretability by introducing a new variable from an old one while also changing multiplicative relations into addictive ones.

Example:

import numpy as np
transformed_data = np.log(original_data)

Bucketing or Binning

When a continuous numerical variable is divided into discrete bins or categories to produce a new unique variable, it is called bucketing or binning. The first step in this is to decide how many bins or widths you want. Then, each data point is assigned to one of the bins based on its value. In the last step, each bin is labelled with a specific value. Categorical labels can also be assigned to the bins, helping to improve the predictability of the model further.

Example of Feature Engineering in Data Science

Now, let's understand one of the above processes of feature engineering, i.e. feature creation The techniques mentioned above help data scientists daily introduce more important features to their ongoing work on data and solve real-world problems. Let us look at an example and apply one of the components and techniques of feature engineering.

For instance, we have a data frame of three classes named A, B, and C. After importing the packages, we have something like this in our code.

from sklearn.preprocessing import OneHotEncoder
import pandas as pd
data = {'Category': ['A', 'B', 'A', 'C', 'B']}
df = pd.DataFrame(data)encoder = OneHotEncoder()

• After applying the one hot encoding

ohe = encoder.fit_transform(df[['Category']])
one_hot_df = pd.DataFrame(ohe.toarray(),
columns=encoder.get_feature_names(['Category']))

• Here is the result,  and we have three new features.

Kickstart Your Career with IK in Data Science

Machine learning has different algorithms and data models that require feature engineering to create fresh variables with already-existing features. It can help to level up the accuracy and precision of the data model that is present in the training set. It can be challenging to learn different techniques of machine learning, like what is feature engineering in data science. But with the correct guidance and approach, anyone can master it.

According to the statistics, the average rate of machine learning and data scientists is said to have increased by 35% by the year 2032. Fulfil your dreams of becoming a data scientist engineer and understand machine learning to crack the interview at top-notch companies.

Sign up for our machine learning course and kickstart your journey!

FAQs on Feature Engineering in Data Science

Q1. What is the difference between feature extraction and feature construction? 

 Ans. Feature extraction reduces the dimensionality of data, but construction involves creating new features from existing ones

Q2. How does feature engineering improve model performance?

Ans. Feature engineering improves model performance by providing the model with more informative data and capturing the underlying relationship between variables

Q3. How to derive new features from features that already exist?

Ans.  Removing outliers, bucketing and binning, log transformation and creating polynomial features can help in the creation of new features.

Q4. What is the difference between a label and a feature?

Ans. A feature is an input variable, and a target is an output variable in machine learning.

Q5. Why is feature creation important before modelling?

Ans. Feature creation optimizes computational efficiency, handling complex data and in improving model interpretability. 

‍