001484681 000__ 03460cam\\2200541\i\4500
001484681 001__ 1484681
001484681 003__ OCoLC
001484681 005__ 20240117003333.0
001484681 006__ m\\\\\o\\d\\\\\\\\
001484681 007__ cr\cn\nnnunnun
001484681 008__ 231212s2023\\\\sz\a\\\\ob\\\\001\0\eng\d
001484681 019__ $$a1411278183$$a1412620653
001484681 020__ $$a9783031456305$$q(electronic bk.)
001484681 020__ $$a3031456300$$q(electronic bk.)
001484681 020__ $$z9783031456299
001484681 020__ $$z3031456297
001484681 0247_ $$a10.1007/978-3-031-45630-5$$2doi
001484681 035__ $$aSP(OCoLC)1413447929
001484681 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP
001484681 049__ $$aISEA
001484681 050_4 $$aQA76.9.Q36
001484681 08204 $$a001.4/2$$223/eng/20231212
001484681 1001_ $$aAcito, Frank,$$eauthor.
001484681 24510 $$aPredictive analytics with KNIME :$$banalytics for citizen data scientists /$$cFrank Acito.
001484681 264_1 $$aCham :$$bSpringer,$$c[2023]
001484681 264_4 $$c©2023
001484681 300__ $$a1 online resource (xiii, 314 pages) :$$billustrations (chiefly color)
001484681 336__ $$atext$$btxt$$2rdacontent
001484681 337__ $$acomputer$$bc$$2rdamedia
001484681 338__ $$aonline resource$$bcr$$2rdacarrier
001484681 504__ $$aIncludes bibliographical references and index.
001484681 5050_ $$aChapter 1 Introduction to analytics -- Chapter 2 Problem definition -- Chapter 3 Introduction to KNIME -- Chapter 4 Data preparation -- Chapter 5 Dimensionality reduction and feature extraction -- Chapter 6 Ordinary least squares regression -- Chapter 7 Logistic regression -- Chapter 8 Decision and regression trees -- Chapter 9 Naïve Bayes -- Chapter 10 k nearest neighbors -- Chapter 11 Neural networks -- Chapter 12 Ensemble models -- Chapter 13 Cluster analysis -- Chapter 14 Communication and deployment.
001484681 506__ $$aAccess limited to authorized users.
001484681 520__ $$aThis book is about data analytics, including problem definition, data preparation, and data analysis. A variety of techniques (e.g., regression, logistic regression, cluster analysis, neural nets, decision trees, and others) are covered with conceptual background as well as demonstrations of KNIME using each tool. The book uses KNIME, which is a comprehensive, open-source software tool for analytics that does not require coding but instead uses an intuitive drag-and-drop workflow to create a network of connected nodes on an interactive canvas. KNIME workflows provide graphic representations of each step taken in analyses, making the analyses self-documenting. The graphical documentation makes it easy to reproduce analyses, as well as to communicate methods and results to others. Integration with R is also available in KNIME, and several examples using R nodes in a KNIME workflow are demonstrated for special functions and tools not explicitly included in KNIME.
001484681 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 12, 2023).
001484681 63000 $$aKNIME (Computer file).
001484681 650_0 $$aPredictive analytics.
001484681 650_0 $$aPredictive analytics$$xComputer programs.
001484681 655_0 $$aElectronic books.
001484681 77608 $$iPrint version: $$z3031456297$$z9783031456299$$w(OCoLC)1396143634
001484681 852__ $$bebk
001484681 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-45630-5$$zOnline Access$$91397441.1
001484681 909CO $$ooai:library.usi.edu:1484681$$pGLOBAL_SET
001484681 980__ $$aBIB
001484681 980__ $$aEBOOK
001484681 982__ $$aEbook
001484681 983__ $$aOnline
001484681 994__ $$a92$$bISE