000			a
999			_c33362 _d33362
008			241114b xxu||||| |||| 00| 0 eng d
020			_a9780367686314
082			_a004.678 _bPER
100			_aPerros, Harry G.
245			_aAn introduction to IoT analytics
260			_bCRC Press, _c2021 _aBoca Raton :
300			_a354 p. ; _bill., _c26 cm.
365			_b3362.00 _c₹ _d01
490			_aChapman & Hall/CRC data science series
504			_aIncludes bibliographical references and index.
520			_aAn Introduction to IoT Analytics covers techniques that can be used to analyze data from IoT sensors and also addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so that one can learn how to apply these tools in practice with a good understanding of their inner workings. It is an introductory book for readers that have no familiarity with these techniques. The techniques presented in the book come from the areas of Machine Learning, Statistics, and Operations Research. Machine Learning techniques are described that can be used to analyze IoT data generated from sensors for clustering, classification, and regression. The statistical techniques described can be used to carry out regression and forecasting of IoT sensor data, and dimensionality reduction of data sets. Operations Research is concerned with the performance of an IoT system by constructing a model of a system under study, and then carry out what-if analysis. The book also describes simulation techniques. Key features: IoT analytics is not just Machine Learning but it also involves other tools, such as, forecasting and simulation techniques. Many diagrams and examples are given throughout the book to better explain the material presented. At the end of each chapter, there is a project designed to help the reader to better understand the techniques described in the chapter. The material is this book has been class tested over several semesters
650			_aBusiness and Economics Statistics
650			_aInternet of things
650			_aConfidence interval
650			_aData points
650			_aDecision tree
650			_aExponential distribution
650			_aHyperplane
650			_aIndependent variables
650			_aLinear regression
650			_aNaive Bayes classifier
650			_aNormal distribution
650			_aNull hypothesis
650			_aOverfitting
650			_aP-value
650			_aPrediction interval
650			_aSupport vector machines
650			_aQ-Q plot
942			_2ddc _cBK