What is Feature Engineering in Machine Learning | Feature Engineering Techniques

If you are into machine learning, then you probably know that feature engineering is an important step in building a machine-learning model that actually works. Feature engineering is the process of transforming existing features or creating new features to improve the performance of a machine-learning model. Feature engineering is the process of taking raw data and turning it into something that a machine learning algorithm can use to make predictions. But why is it so important?

Why Feature Engineering is important

Imagine you are trying to teach a computer to recognize pictures of cats. It’s not enough to just show a bunch of cat pictures. You need to give the computer some structured information to work with, like the color of the cat’s fur, the shape of its ears, or the size of its whiskers. This is where feature engineering comes in. It helps you extract the right kind of information from your data and turn it into something that a machine learning algorithm can actually use.

In this article, we will cover the basics of feature engineering, including what it is, why it is important, and how to do it effectively. We will explore different techniques for feature engineering and explain how to apply these techniques to your own data sets. Whether you’re a data scientist, a machine learning enthusiast, or just curious about the world of AI, this article will provide you with the knowledge and skills you need to apply to your machine learning model.

Techniques for Feature Engineering

1 – Handling Missing Data

Many machine learning algorithms do not accept data with missing values, so handling missing data is critical. Missing values in a data set can lead to errors and poor performance of the machine learning model. Missing data can cause a variety of issues, such as reducing statistical power, causing bias in parameter estimation, and reducing sample representativeness.

There are several ways to handle missing data. One approach is variable deletion, where columns with null values are deleted. If more than half the rows in a column are null, the column can be dropped as it is not an important feature. Another approach is replacing missing values with mean, median, or mode. Mean is the average of all values in a set, median is the middle number in a set of numbers sorted by size, and mode is the most common numerical value for two or more sets. This can be done on numerical data to replace missing values.

2 – Handling Continuous Features

Continuous features in a data set consist of a distinct range of values. Before training machine learning algorithms, it is critical to deal with continuous features. Two common methods to deal with continuous features are normalization and standardization.

Normalization is a scaling technique used to change the values of numerical columns in a data set to a common scale. It scales the values ranging from 0 to 1, making it useful when features in the data set are of different scales. Standardization, on the other hand, centers the values around the mean with a unit standard deviation. It ensures that the data set has a mean of 0 and a standard deviation of 1. Standardization is useful for plotting similarities and differences between data points.

3 – Handling Categorical Features

Categorical data is used to group information with similar characteristics, while numerical data expresses information in the form of numbers. Most machine learning libraries require non-numerical values to be transformed into integers or floats. Two common methods to deal with categorical features are label encoding and one-hot encoding.

Label encoding involves converting each value in a column into a number. Each label is given a unique integer based on alphabetical order. This can be done using the LabelEncoder class from the scikit-learn library. One-hot encoding, on the other hand, converts categorical data into numeric data by splitting the column into multiple columns. The numbers are replaced by ones and zeros, representing the presence or absence of a particular category. This can be done using the OneHotEncoder class from the scikit-learn library.

4 – Feature Selection

Feature selection is the process of selecting the most appropriate and relevant features to be used in model building. It can be done automatically or manually and helps reduce the complexity of a machine learning model, improve accuracy, reduce overfitting, and allow models to train faster.

There are several methods for feature selection. One approach is removing features with low variance, where features whose variance does not meet a certain threshold are removed. Another approach is using statistical tests, such as the chi-square test, to select features with the strongest relationship with the target feature. Recursive feature elimination is a technique that eliminates less important features one by one until the desired number of features is reached. Feature selection can also be done using machine learning models, such as selecting features based on feature importances or using sequential feature selection algorithms like forward or backward selection. Additionally, correlation matrix heat maps can be used to visualize the relationship between features.


Feature engineering is a crucial step in building effective machine learning models. It involves transforming raw data into features that can be used to create predictive models. In this article, we covered the basics of feature engineering, including techniques for handling missing data, continuous features, and categorical features. We also discussed different methods for feature selection.

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