000			02420nam a22002657a 4500
005			20260710131651.0
008			250813b |||||||| |||| 00| 0 eng d
020			_a9783031387463
028			_a: Zafaa Books & Distributors _b: 313/103A, H. No. 15, Tulsi Nagar, Inderlok, Delhi
082			_a519.5 JAM
100			_a James, Gareth.
100			_a; Witten, Daniela.
100			_a; Hastie, Trevor.
245			_aAn Introduction to Statistical Learning _b: With Applications in Python. _c/ Gareth James.
260			_a, New York _b: Springer _c, 2023.
300			_axv, 607p. _c: 24cm.
365			_b3265.5
365			_b5784
520			_aAn Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
650			_aStatistical Learning
650			_aStatistical Learning with Python
650			_aPrediction techniques
700			_aGareth James
942			_cBOOK
999			_c66001 _d66001