Feature extraction is a process used in data analysis and machine learning to identify and isolate important characteristics or attributes from raw data. This helps simplify the data while retaining its essential information, making it easier for algorithms to analyze and learn from it. For example, in image processing, features might include edges, colors, or shapes that help distinguish one object from another. In natural language processing, feature extraction can involve identifying keywords, phrases, or sentiment from text data. By focusing on these key features, models can better understand and classify the information, leading to improved performance in tasks like text classification or sentiment analysis.