Feature extraction is a fundamental step in data analysis and pattern recognition, where relevant information is extracted from raw data to represent it in a more compact and meaningful way. This process involves identifying and selecting the most important features or attributes of the data that are relevant to the problem at hand. Feature extraction is commonly used in fields such as machine learning, computer vision, and signal processing to reduce the dimensionality of the data, improve computational efficiency, and enhance the performance of algorithms. It helps in simplifying and interpreting complex data sets, making it easier to analyze and draw insights from the data.