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001			23306575
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015			_aGBC285151 _2bnb
016	7		_a020584938 _2Uk
020			_a9780128242711 _q(pbk)
035			_a23306575
035			_a(OCoLC)1286790517
040			_aYDX _beng _erda _cYDX _dBDX _dUKMGB _dOCLCF _dSISPL _dCTL _dFIE _dDLC _dCSPC
042			_alccopycat
050	0	0	_aHF1008 _b.F38 2023
082	0	4	_a658.05631 _bF278d _223
100	1		_aFávero, Luiz Paulo, _eauthor.
245	1	0	_aData science, analytics and machine learning with R / _cLuiz Paulo Fávero, Patrícia Belfiore, and Rafael de Freitas Souza.
250			_aFirst edition.
264		1	_aLondon, United Kingdom ; _aSan Diego, California : _bAcademic Press, an imprint of Elsevier, _c2023.
300			_axii, 648 pages : _billustrations (black and white, and colour) ; _c28 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
504			_aIncludes bibliographic references and index.
505	0		_aI. Introduction -- Overview of data science, analytics, and machine learning -- Introduction to R-based language -- II. Applied statistics and data visualization -- Types of variables, measurement scales, and accuracy scales -- Univariate descriptive statistics -- Bivariate descriptive statistics -- Hypotheses tests -- Data visualization and multivariate graphs -- III. Data mining and preparation -- Webscraping and handcrafted robots -- Using application programming interfaces to collect data -- Managing data -- IV. Unsupervised machine learning techniques -- Cluster analysis -- Principal component factor analysis -- Simple and multiple correspondence analysis - V. Supervised machine learning techniques -- Simple and multiple regression models -- Binary and multinomial logistics -- Count-data and zero-inflated -- Generalized linear mixed models -- VI. Improving performance -- Support vector machines -- Classification and regression trees -- Boosting and bagging -- Random forests -- Artificial neural networks -- VII. Spatial analysis -- Working on shapefiles -- Dealing with simple feature objects -- Raster objects -- Exploratory spatial analysis -- VIII. Adding value to your work -- Enhanced and interactive graphs -- Dashboards with R.
520			_aData Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear. -- _cProvided by publisher.
650		0	_aBusiness _xData processing.
650		0	_aMachine learning.
650		0	_aR (Computer program language).
700	1		_aBelfiore, Patrícia Prado, _eauthor.
700	1		_aDe Freitas Souza, Rafael, _eauthor
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2ddc _cBK _e23 _h658.05631 _iF278d _kGRD _m2023 _n0
999			_c30237 _d30237