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Über dieses Buch

Automate the predictive analytics process using Oracle Data Miner and Oracle R Enterprise. This book talks about how both these technologies can provide a framework for in-database predictive analytics. You'll see a unified architecture and embedded workflow to automate various analytics steps such as data preprocessing, model creation, and storing final model output to tables.

You'll take a deep dive into various statistical models commonly used in businesses and how they can be automated for predictive analytics using various SQL, PLSQL, ORE, ODM, and native R packages. You'll get to know various options available in the ODM workflow for driving automation. Also, you'll get an understanding of various ways to integrate ODM packages, ORE, and native R packages using PLSQL for automating the processes.

Data Science Automation Using Oracle Data Miner and Oracle R Enterprise starts with an introduction to business analytics, covering why automation is necessary and the level of complexity in automation at each analytic stage. Then, it focuses on how predictive analytics can be automated by using Oracle Data Miner and Oracle R Enterprise. Also, it explains when and why ODM and ORE are to be used together for automation.

The subsequent chapters detail various statistical processes used for predictive analytics such as calculating attribute importance, clustering methods, regression analysis, classification techniques, ensemble models, and neural networks. In these chapters you will also get to understand the automation processes for each of these statistical processes using ODM and ORE along with their application in a real-life business use case.

What you'll learn

Discover the functionality of Oracle Data Miner and Oracle R EnterpriseGain methods to perform in-database predictive analyticsUse Oracle's SQL and PLSQL APIs for building analytical solutionsAcquire knowledge of common and widely-used business statistical analysis techniques

Who this book is for

IT executives, BI architects, Oracle architects and developers, R users and statisticians.



Chapter 1. Getting Started With Oracle Advanced Analytics

In past few years, there has been a tremendous growth in unstructured as well as structured data, and they continue growing exponentially. Organizations are looking out for ways to derive value and make sense out of this data. They are looking for answers from the data that they have never asked before.

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Chapter 2. Installation and Hello World

“The secret of getting ahead is getting started.”

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Chapter 3. Clustering Methods

Mobile and tablet devices and easy access to the Internet are making the world go digital. Retail business is no longer confined to brick and mortar stores. There are multiple channels through which business is carried out. Some consumers prefer to shop using mobile or web applications; but they never stop visiting brand stores. At each touch point, customers leave their footprints and are captured through different attributes.

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Chapter 4. Association Rules

Almost anyone who steps into the field of data science would have certainly heard the famous diaper and beer story. An analytical study conducted by a major grocery store on their transactional purchase history found that a man between 30 and 40 years of age, shopping between 5 p.m. to 7 p.m. on Fridays who buys diapers were most likely to have beers in their cart.

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Chapter 5. Regression Analysis

Knowing the future is something everyone wishes for. People are obsessed with knowing the future, believing that if they know tomorrow, they will adjust their activities today. It is impossible to get data from the future, but there is a way to know the future from reviewing the past.

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Chapter 6. Classification Methods

In the last chapter, we learned about regression analysis as one of the supervised machine learning methods. It is useful for cases in which we want to predict the future sales of a product, expenditures, or anything that has target labels with continuous numerical data.

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Chapter 7. Advanced Topics

“Data Scientist is the sexiest job of the 21st century,” quotes Harvard Business Review. We went through many machine learning algorithms, data science techniques, and use cases, which are necessary to be a data scientist. However, this is just the beginning.

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Chapter 8. Solutions Deployment

We are often required to move the models and their associated objects to different environments in their lifetime. For example, we develop a model in a development environment, move to its test environment for user testing, and make it live in a production environment. It is not feasible to create the models from scratch in every environment, and doing so might introduce human errors at certain instances.

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