Feature Extraction is the process of transforming raw data into a smaller set of meaningful characteristics that capture the most important information. It helps machine learning models focus on relevant patterns while reducing unnecessary or complex data.
For example, an image recognition system can extract features such as edges, shapes, textures, and patterns from a photograph instead of processing every pixel individually. These extracted features make data easier to analyze and can improve model efficiency, accuracy, and performance
