000			04552cam a2200673 i 4500
001			u12551
003			SA-PMU
005			20210418123306.0
008			160114s2017 nju 001 0 eng c
010			_a 2015048305
040			_aWaSeSS/DLC _beng _erda _cDLC _dOCLCF _dYDXCP _dMYG _dI3U _dBTCTA _dBDX _dCHVBK _dYDX
019			_a913923771 _a914218233 _a973768767
020			_a9781118877432 (cloth)
020			_a1118877438 (cloth)
020			_z9781118877524 (epub)
035			_a(OCoLC)939596191 _z(OCoLC)913923771 _z(OCoLC)914218233 _z(OCoLC)973768767
042			_apcc
050	0	0	_aHF5691 _b.S43245 2017
082	0	0	_a006.3/12 _223
100	1		_aShmueli, Galit, _d1971- _eauthor.
245	1	0	_aData mining for business analytics : _bconcepts, techniques, and applications in JMP Pro / _cGalit Shmueli, Peter C. Bruce, Mia L. Stephens, Nitin R. Patel.
264		1	_aHoboken, New Jersey : _bWiley, _c2017.
300			_axxii, 442 pages ; _c27 cm
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
500			_aIncludes index.
504			_aIncludes bibliographical references and index.
505	0		_aOverview of the data mining process -- Data visualization -- Dimension reduction -- Evaluating predictive performance -- Multiple linear regression -- K-nearest neighbors (kNN) -- The naive Bayes classifier -- Classification and regression trees -- Logistic regression -- Neural nets -- Discriminant analysis -- Combining methods : ensembles and uplift modeling -- Cluster analysis -- Handling time series -- Regression-based forecasting -- Smoothing methods -- Cases.
520			_aData Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter; End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material; Data-rich case studies to illustrate various applications of data mining techniques; A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field.--Publisher website.
630	0	0	_aJMP (Computer file)
630	0	7	_aJMP (Computer file) _2fast _0(OCoLC)fst01385779
650		0	_aBusiness mathematics _xComputer programs.
650		0	_aBusiness _xData processing.
650		0	_aData mining.
650		7	_aBusiness _xData processing. _2fast _0(OCoLC)fst00842293
650		7	_aBusiness mathematics _xComputer programs. _2fast _0(OCoLC)fst00842783
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
650		7	_aData Mining _2gnd
650		7	_aBusiness Intelligence _2gnd
700	1		_aBruce, Peter C., _d1953- _eauthor.
700	1		_aStephens, Mia L., _eauthor.
700	1		_aPatel, Nitin R. _q(Nitin Ratilal), _eauthor.
938			_aBrodart _bBROD _n119067862
938			_aBaker and Taylor _bBTCP _nBK0017427427
938			_aBrodart _bBROD _n113406452
938			_aYBP Library Services _bYANK _n12529712
029	1		_aAU@ _b000057129962
029	1		_aCHNEW _b000854130
029	1		_aCHVBK _b365605824
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029	1		_aCHDSB _b006678549
029	1		_aCHVBK _b484447750
029	1		_aCHBIS _b010636962
942			_cBOOK
994			_aZ0 _bSUPMU
948			_hNO HOLDINGS IN SUPMU - 27 OTHER HOLDINGS
596			_a1 2
999			_c2437 _d2437