Step 1: Understand what features are in a dataset. Features are the individual measurable properties or characteristics used by a machine learning model.
Step 2: Learn about feature engineering. This is the process of using domain knowledge to create new features or modify existing ones.
Step 3: Recognize the importance of feature engineering. It helps improve the model's ability to learn from the data.
Step 4: Identify how new features can be created. This can include combining existing features, transforming them, or extracting new information.
Step 5: Understand that better features lead to better model performance. When features accurately represent the data, the model can make better predictions.
