In machine learning, features are individual independent variables that act like a input in your system. Actually, while making the predictions, models use such features to make the predictions. And using the feature engineering process, new features can also be obtained from old features in machine learning.

To understand in more simple way, lets take an example, where you can consider one column of your data set to be one feature which is also know as “variables or attributes” and the more number of features are known as dimensions. And depending on what you are trying to analyze the features you include in your dataset can vary widely.

What is Feature Engineering in Machine Learning?

Feature engineering is the process of using the domain knowledge of the data to create features that makes machine learning algorithms work properly. If feature engineering is performed properly, it helps to improve the power of prediction of machine learning algorithms by creating the features using the raw data that facilitate the machine learning process.

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