000			01696nam a22002057a 4500
008			241120b |||||||| |||| 00| 0 eng d
020			_a9789811396663 _qpbk
041			_aeng
082			_a006.312 _bOLS
100			_a Olson, David L
245			_a Predictive data mining models _c/ David L. Olson and Desheng Wu
250			_a2nd ed.
260			_a Springer _b Singapore _c2020
300			_axi, 125 p. _bill. _c23 cm.
490			_a Computational risk management
520			_aThis book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R) and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics. The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis.
650			_a Business Data processing
650			_aData Mining
942			_cREF
999			_c565871 _d565871