What is feature engineering?
<ul>
<li>It is the process of using domain knowledge to extract features from raw data</li>
<li>In the machine learning development life cycle, this is the second step after performing EDA </li>
</ul>
Why perform feature engineering?
<ul>
<li>Feature engineering will change the accuracy/prediction made by the machine learning algorithm</li>
</ul>
Steps Involved in Feature Engineering
<ul>
<li>Feature transformation</li>
</ul>
In this step, we convert/transform the already available features into new features by applying some sort of mathematical function to the feature.
<ul>
<li>Feature Construction</li>
</ul>
In this step using domain knowledge, we generate new features which are not available in the original data set.
<ul>
<li>Feature Extraction</li>
</ul>
In this step, we use the already available feature and generate a new feature which will follow/have the properties of the features used for making it
<ul>
<li>Feature Selection</li>
</ul>
This is the last and final step of feature engineering in which we will remove some already available features in the data set which have no role to play in the prediction which is being made by the machine learning algorithm

What is feature engineering?
-  It is the process of using domain knowledge to extract features from raw data
- In the machine learning development life cycle, this is the second step after performing EDA 


Why perform feature engineering?
- Feature engineering will change the accuracy/prediction made by the machine learning algorithm

Steps Involved in Feature Engineering

- **Feature transformation**

In this step, we convert/transform the already available features into new features by applying some sort of mathematical function to the feature.

- **Feature Construction**

In this step using domain knowledge, we generate new features which are not available in the original data set.

- **Feature Extraction**

In this step, we use the already available feature and generate a new feature which will follow/have the properties of the features used for making it

- **Feature Selection**

This is the last and final step of feature engineering in which we will remove some already available features in the data set which have no role to play in the prediction which is being made by the machine learning algorithm








