Predictive analytics for dummies / Anasse Bari; Mohamed Chaouchi; Tommy Jung
By: Bari, Anasse
Contributor(s): Chaouchi, Mohamed -- author | Jung, Tommy -- author
Material type:
TextLanguage: English Series: --For dummiesPublisher: Hoboken, NJ : Wiley, 2017Edition: 2nd editionDescription: viii, 443 p. : ill. ; 24 cmISBN: 9788126567935Subject(s): Decision making -- Data processing. Decision making -- Mathematical models. Management -- Data processing. Management -- Mathematical models. Data mining. Big dataDDC classification: 658.4034 BAR | Item type | Current location | Call number | Status | Date due | Barcode |
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WeSchool, Bangalore | 658.4034 BAR (Browse shelf) | Available | B014362 | |
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WeSchool, Bangalore | 658.4034 BAR (Browse shelf) | Available | B014363 | |
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WeSchool, Bangalore | 658.4034 BAR (Browse shelf) | Available | B014364 | |
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WeSchool, Bangalore | 658.4034 BAR (Browse shelf) | Available | B014365 | |
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WeSchool, Bangalore | 658.4034 BAR (Browse shelf) | Available | B014366 |
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| 658.4034 BAR Predictive analytics for dummies / | 658.4034 BAR Predictive analytics for dummies / | 658.4034 BAR Predictive analytics for dummies / | 658.4034 BAR Predictive analytics for dummies / | 658.4034 BLA Testing business ideas / | 658.4034 BRO Theory and Problems of Operations Research | 658.4034 BRO Theory and Problems of Operations Rresearch |
Includes index.
"Learn to: Analyze structured and unstructured data ; Use algorithms and data analysis techniques ; Build clustering, classificaion and statistical models ; Apply predictive analytics to your website and marketing efforts"--Cover.
Introduction. --
pt. 1. Getting started with predictive analytics. Entering the arena ; Predictive analytics in the wild ; Exploring your data types and associated techniques ; Complexities of data --
pt. 2. Incorporating algorithms in your models. Applying models ; Identifying similarities in data ; Predicting the future using data classification --
pt. 3. Developing a roadmap. Convincing your management to adopt predictive analytics ; Preparing data ; Building a predictive model ; Visualization of analytical results --
pt. 4. Programming predictive analytics. Creating basic prediction examples ; Creating basic examples of unsupervised predictions ; Predictive modeling with R ; Avoiding analysis traps ; Targeting big data --
pt. 5. The part of tens. Ten reasons to implement predictive analytics ; Ten steps to build a predictive analytic model.
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