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Data mining for business analytics : concepts, techniques and applications in Phyton / Galit Shmueli [y otros tres].

Contributor(s): Shmueli, Galit, 1971- [autor] | Bruce, Peter C [autor] | Gedeck, Peter [autor] | Patel, Nitin R [autor]Material type: TextTextLanguage: inglés Publisher: Hoboken, New Jersey : Wiley, 2020Edition: First editionDescription: xx, 574 páginas : ilustraciones, gráficas ; 26 cmContent type: texto Media type: sin mediación Carrier type: volumenISBN: 9781119549840Subject(s): Minería de datos -- Enseñanza | Análisis de datos -- Estrategia y técnicas | Aprendizaje automático (Inteligencia Artificial) | Procesamiento de datos en línea | Inteligencia de negocios | Toma de decisiones | Adm_Informática | Adm_Informática_Tic aplicado a la toma de decisionesDDC classification: 005.74 Online resources: Recurso Digital
Incomplete contents:
Chapter 2. Overview of the data mining process ; Chapter 3. Data exploration and dimension reduction 4. Dimension reduction ; Chapter 5. Evaluating predictive performance ; Chapter 6. Multple linear regression ; Chapter 7. K-Nearest neighbors (KNN) ; Chapter 8.The naive bayes classifier ; Chapter 9.Classification and regression trees ; Chapter 10.Logistic regression ; Chapter 11.Neural nets ; Chaprter 12.Discriminant analysis ; Chapter 18.Smoothing methods ; Chapter 20.Cases
Abstract: Is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
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Reserva Reserva Biblioteca CESA

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Reserva 005.74 / D232 2020 (Browse shelf(Opens below)) Ej.1 Available 7103001981
Total holds: 0

Includes index.

Disponible en OverDrive.

Chapter 2. Overview of the data mining process ; Chapter 3. Data exploration and dimension reduction 4. Dimension reduction ; Chapter 5. Evaluating predictive performance ; Chapter 6. Multple linear regression ; Chapter 7. K-Nearest neighbors (KNN) ; Chapter 8.The naive bayes classifier ; Chapter 9.Classification and regression trees ; Chapter 10.Logistic regression ; Chapter 11.Neural nets ; Chaprter 12.Discriminant analysis ; Chapter 18.Smoothing methods ; Chapter 20.Cases

Is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

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