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|a 9780133412963
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|a 9780133412932
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|a HD30.27
|b .M55547 2014
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|a 658.40352
|q OCoLC
|2 23/eng/20230216
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049 |
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|a UAMI
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100 |
1 |
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|a Miller, Thomas W.,
|d 1946-
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245 |
1 |
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|a Modeling techniques in predictive analytics :
|b business problems and solutions with R /
|c Thomas W. Miller.
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|a Business problems and solutions with R
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260 |
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|a Upper Saddle River, N.J. :
|b Pearson Education,
|c 2013, ©2014.
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300 |
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|a 1 online resource (1 volume) :
|b illustrations
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336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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588 |
0 |
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|a Print version record.
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504 |
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|a Includes bibliographical references and index.
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505 |
0 |
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|t Analytics and data science --
|t Advertising and promotion --
|t Preference and choice --
|t Market basket analysis --
|t Economic data analysis --
|t Operations management --
|t Text analytics --
|t Sentiment analysis --
|t Sports analytics --
|t Brand and price --
|t Spatial data analysis --
|t The big little data game --
|g [Appendix] A.
|t There's a pack' for that --
|g [Appendix] B.
|t Measurement --
|g [Appendix] C.
|t Code and utilities.
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520 |
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|a Today, successful firms compete and win based on analytics. Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. Thomas W. Miller's unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. For each problem, Miller explains why the problem matters, what data is relevant, how to explore your data once you've identified it, and then how to successfully model that data. You'll learn how to model data conceptually, with words and figures; and then how to model it with realistic R programs that deliver actionable insights and knowledge. Miller walks you through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. All example code is presented in R, today's #1 system for applied statistics, statistical research, and predictive modeling; code is set apart from other text so it's easy to find for those who want it (and easy to skip for those who don't).
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590 |
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|a O'Reilly
|b O'Reilly Online Learning: Academic/Public Library Edition
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650 |
|
0 |
|a Business forecasting
|x Mathematical models.
|
650 |
|
0 |
|a Business forecasting
|x Data processing.
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650 |
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0 |
|a R (Computer program language)
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650 |
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6 |
|a Prévision commerciale
|x Modèles mathématiques.
|
650 |
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6 |
|a Prévision commerciale
|x Informatique.
|
650 |
|
6 |
|a R (Langage de programmation)
|
650 |
|
7 |
|a Business forecasting
|x Data processing.
|2 fast
|0 (OCoLC)fst00842701
|
650 |
|
7 |
|a Business forecasting
|x Mathematical models.
|2 fast
|0 (OCoLC)fst00842702
|
650 |
|
7 |
|a R (Computer program language)
|2 fast
|0 (OCoLC)fst01086207
|
776 |
0 |
8 |
|i Print version:
|a Miller, Thomas W., 1946-
|t Modeling techniques in predictive analytics.
|d Upper Saddle River, New Jersey : Pearson Education, Inc., [2014]
|z 9780133412932
|w (DLC) 2013946325
|w (OCoLC)829989806
|
856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9780133412963/?ar
|z Texto completo (Requiere registro previo con correo institucional)
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994 |
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|a 92
|b IZTAP
|