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Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition, by Bruce Ratner is a reference book covering the application of statistical methods and machine learning techniques in data mining, predictive modeling, and big data analysis.

The book explains various quantitative techniques for discovering patterns, relationships, and meaningful information from complex datasets. It integrates classical statistical approaches with machine-learning methods to develop more effective and accurate predictive models.

The topics include linear and logistic regression, principal component analysis (PCA), market segmentation, classification, incomplete data analysis, predictive model evaluation, bootstrapping, and text mining.

The third edition has been revised and expanded with 13 new chapters covering developments in data science and various modern analytical techniques. It also provides practical examples of applying these methods to real-world problems in business analytics, marketing, and big data processing.

This book is particularly useful for students, lecturers, researchers, data scientists, data analysts, and statistical practitioners who wish to deepen their knowledge of data mining, machine learning, and data-driven predictive modeling.