Feature engineering is one of the key components that contribute to the model's performance. A simple model with the right features can perform better than a complicated one with poor features. You can think of the feature engineering process as the most important step in determining your predictive model's success or failure. Feature engineering will be much easier if you understand the data.
Feature engineering is used extensively by anyone who uses machine learning to solve only one question, which is: how do you get the most out of your data samples for predictive modeling? This is the problem that the process and practice of feature engineering solves, and the success of your data science skills starts by knowing how to represent your data well.
Predictive modeling is a formula or rule that transforms a list of features or input variables (x1...
