Data in machine learning (ML) can be represented in various dimensions, including as samples (rows) and features (columns). Preprocessing involves adjusting data structures (e.g., arrays, matrices) and optimizing feature sets to enhance model performance. Dimensionality reduction techniques (e.g., PCA, SVD) can simplify complex datasets, while data analysis methods (e.g., cross-validation, information criteria) evaluate model accuracy. ML tools (e.g., Scikit-learn, TensorFlow) facilitate data processing and analysis, enabling applications such as feature extraction, data visualization, and anomaly detection.
