000			01913nam a22002895i 4500
001			91730
005			20240604115359.0
010			_a978-3-030-22625-1 _dcompra
090			_a91730
100			_a20231023d2019 k||y0pory50 ba
101	0		_aeng
102			_aCH _bCham
200	1		_aTargeting uplift _bDocumento eletrónico _ean introduction to net scores _fRené Michel, Igor Schnakenburg, Tobias von Martens
210			_aCham _cSpringer International Publishing _d2019
215			_aXXXII, 352 p. _cil.
303			_aThis book explores all relevant aspects of net scoring, also known as uplift modeling: a data mining approach used to analyze and predict the effects of a given treatment on a desired target variable for an individual observation. After discussing modern net score modeling methods, data preparation, and the assessment of uplift models, the book investigates software implementations and real-world scenarios. Focusing on the application of theoretical results and on practical issues of uplift modeling, it also includes a dedicated chapter on software solutions in SAS, R, Spectrum Miner, and KNIME, which compares the respective tools. This book also presents the applications of net scoring in various contexts, e.g. medical treatment, with a special emphasis on direct marketing and corresponding business cases. The target audience primarily includes data scientists, especially researchers and practitioners in predictive modeling and scoring, mainly, but not exclusively, in the marketing context. .
606			_96944 _aEstatística _xProcessamento de dados
680			_aQA276.4
700			_973035 _aMichel _bRené
701			_973036 _aSchnakenburg _bIgor _4070
701			_973037 _avon Martens _bTobias _4070
801		0	_aPT _gRPC
856	4		_uhttps://doi.org/10.1007/978-3-030-22625-1
942			_2lcc _cF _n0