000			02096nam a22003138a 4500
999			_c29551 _d29551
001			64543
008			220214b xxu||||| |||| 00| 0 eng d
010			_a 2015048305
020			_a9781118877432
040			_aWaSeSS/DLC
082	0	0	_a006.3/12
100	1		_aShmueli, Galit, _d1971- _936053
245	1	0	_aData mining for business analytics : _bconcepts, techniques, and applications in JMP Pro _cGalit Shmueli ... [et al.]
260			_aHoboken, N.J. : _bJohn Wiley & Sons, _cc2017.
300			_axxii, 442 p. : _bill. ; _c26 cm.
500			_aIncludes 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			_aFeaturing 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.
526			_aMBAS904
650		0	_aBusiness mathematics _xComputer programs _936054
650		0	_aBusiness _xData processing _92634
650		0	_aData mining _9312
650		7	_aComputers _xgeneral _919685
700	1		_aBruce, Peter C., _d1953- _936055
700	1		_aStephens, Mia L. _936056
700	1		_aPatel, Nitin R. _936057
856			_uhttps://uow.primo.exlibrisgroup.com/permalink/61UOW_INST/otb3u8/cdi_askewsholts_vlebooks_9781118956625 _zeBook
942			_cREGULAR _2ddc