Data Representation, Preprocessing, and Dimensionality Reduction

Data Representation, Preprocessing, and Dimensionality Reduction provides a comprehensive introduction to the essential techniques used for preparing data for analytics, machine learning, and artificial intelligence applications. The book explores methods for representing structured and unstructured data, handling missing values, noise, and inconsistencies, and transforming raw data into meaningful formats. It also covers feature selection, feature extraction, normalization, encoding, and dimensionality reduction techniques such as PCA and other modern approaches. Through practical examples and real-world applications, the book demonstrates how effective data preprocessing and dimensionality reduction improve model performance, computational efficiency, and decision-making accuracy in data-driven environments.