Define the concept of feature engineering, providing three examples of how it can be applied to improve the accuracy of a predictive model.

Feature engineering is the process of transforming raw data into features that are more suitable and effective for training machine learning models. Instead of directly feeding raw data into a model, feature engineering involves creating new features or modifying existing ones, to better capture underlying patterns and relationships in the data. The main goal is to improve a model's performance by enhancing its ability to learn from the data. Feature engineering is a crucial step in the data science pipeline and often has a greater impact on model accuracy than simply tuning model parameters. Feature engineering is also a highly iterative process where the data scientist needs to think about all the relevant areas that influence the data, and how they might affect the models that are subsequently trained. Here are three examples of feature engineering and how they can improve the accuracy of predictive models: 1. Creating Interaction Features: Interaction features are new features that combine two or more existing features to capture the combined effect of those features on the target variable. For instance, consider a dataset used to predict house prices, and the dataset has features such as 'square footage' and 'number of bedrooms' as separate features. Instead of using these features in isolation, we can create an interaction feature like 'square footage number of bedrooms'. This new feature can capture the notion that a larger house with more bedrooms might have a different price dynamic than either its size or number of bedrooms sep....