Feature Engineering
Explore feature engineering techniques critical for preparing datasets in customer segmentation. Understand how to encode categorical variables, handle feature scaling, and analyze correlations to optimize clustering models effectively.
Machine learning models expect data to be formatted in a specific way. Feature engineering is the phase we use when we bring out all our data-transformation tools and techniques to shape and create features before feeding them into our model. Feature engineering techniques include, but are not limited to:
Missing value handling
Handling outliers
Categorical encoding
Feature splitting
Variable transformation
Categorical encoding
The customer dataset contains a couple of categorical features in a string format. Machine learning algorithms expect only numeric values as parameters, so we need to convert categorical features into numerical ones. This is known as encoding. There are different ways to encode ...