An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)

An Introduction to Statistical Learning provides a comprehensive and accessible overview of statistical learning, a critical toolkit for interpreting complex datasets across diverse fields such as biology, finance, marketing, and astrophysics. This book introduces essential modeling and prediction techniques, supported by real-world examples and color graphics to illustrate key concepts. It covers a wide range of topics, including linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, and multiple testing. The text is designed for both statisticians and non-statisticians who aim to apply cutting-edge techniques to data analysis. Building on the success of its predecessor, An Introduction to Statistical Learning with Applications in R (ISLR), this edition (ISLP) adapts the material for the Python programming language. As Python has grown in popularity within the data science community, this book addresses the demand for a Python-based alternative to the original text. Each chapter includes practical tutorials and labs implemented in Python, making it a valuable resource for both novices and experienced users of the language. These labs ensure readers can directly apply the statistical methods discussed using the Python scientific computing environment. This volume serves as both a foundational textbook for undergraduate and graduate classrooms and a practical reference for professional data scientists. By combining theoretical concepts with hands-on application, it equips readers with the skills necessary to navigate the modern landscape of data science. Whether used for academic study or professional development, the book offers a clear pathway to mastering statistical learning through the lens of Python programming.