000			03533nam a22005175i 4500
005			20250930142236.0
007			cr nn 008mamaa
008			180322s2017 xxu| s |||| 0|eng d
020			_a9781484225141 _9978-1-4842-2514-1
024	7		_a10.1007/978-1-4842-2514-1 _2doi
035			_a(DE-He213)978-1-4842-2514-1
039		9	_a201806061501 _bfati _c201803281130 _drasyilla _y03-22-2018 _zhafiz _wSpringerNature_Books_MARC21_20180201_025518.old _x7
100	1		_aHodeghatta, Umesh R. _eauthor.
245	1	0	_aBusiness Analytics Using R - A Practical Approach [electronic resource] / _cby Umesh R. Hodeghatta, Umesh Nayak.
264		1	_aBerkeley, CA : _bApress : _bImprint: Apress, _c2017.
300			_aXVII, 280 p. 278 illus. _b1online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
520			_aLearn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. You will: ? Write R programs to handle data ? Build analytical models and draw useful inferences from them ? Discover the basic concepts of data mining and machine learning ? Carry out predictive modeling ? Define a business issue as an analytical problem.
650		0	_aComputer science.
650		0	_aComputer programming.
650		0	_aProgramming languages (Electronic computers). _960777
650		0	_aMathematical statistics.
650		0	_aData mining.
650		0	_aInformation storage and retrieval. _962934
650	1	4	_aComputer Science.
650	2	4	_aBig Data.
650	2	4	_aProgramming Techniques.
650	2	4	_aProgramming Languages, Compilers, Interpreters.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aInformation Storage and Retrieval. _962934
650	2	4	_aProbability and Statistics in Computer Science.
700	1		_aNayak, Umesh. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer e-books
776	0	8	_iPrinted edition: _z9781484225134
856	4	0	_uhttps://eresourcesptsl.ukm.remotexs.co/user/login?url=http://doi.org/10.1007/978-1-4842-2514-1
907			_a.b16575155 _b2023-02-07 _c2019-11-12
942			_n0
914			_avtls003632589
998			_ae _b2018-09-03 _cm _dz _feng _gxxu _y0 _z.b16575155
999			_c625964 _d625964