What is the goal of feature engineering during the Data Preparation stage?

The goal of feature engineering during the Data Preparation stage is to create meaningful characteristics for machine learning. This process involves transforming raw data into a format that better represents the underlying problem to be solved. By constructing new features or modifying existing ones, you can enhance the predictive power of your models significantly.

Effective feature engineering can reveal relationships in the data that may not be immediately apparent, leading to improved model performance. For example, in a dataset about housing prices, creating a feature that combines the number of bedrooms and bathrooms into a single metric might yield better insights than using them separately.

In contrast, while addressing missing data values and removing duplicate data values are important data cleaning tasks, they do not focus on creating new informative features from raw data. Instead, these activities primarily ensure that the dataset is free from inaccuracies and that the existing attributes are optimally utilized, which is essential but distinct from the creative and transformative nature of feature engineering.