Yes, feature extraction can be used for both categorical and numerical data. Feature extraction refers to the process of selecting or creating a subset of features from the original dataset that are more relevant for a particular task, while reducing dimensionality and preserving important information.

  For categorical data, feature extraction can be achieved through techniques such as one-hot encoding or label encoding. One-hot encoding converts each categorical variable into binary vectors, where each category is represented by a binary feature. This allows algorithms to interpret and utilize categorical data for analysis. Label encoding assigns a numerical label to each category, replacing the original categorical values with corresponding numerical values.

  For numerical data, feature extraction involves transforming the original features into different representations that capture the underlying patterns or relationships. This can be done using techniques such as principal component analysis (PCA), which identifies the directions of maximum variance in the data and projects the data onto a lower-dimensional subspace. Other techniques include singular value decomposition (SVD), independent component analysis (ICA), and non-negative matrix factorization (NMF).

  In summary, feature extraction is applicable to both categorical and numerical data. It enables dimensionality reduction and enhances the performance of machine learning models by representing the data in a more meaningful and informative way. However, the specific techniques used may vary depending on the nature and characteristics of the data.