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Mastering Predictive Analytics with Python

You're reading from  Mastering Predictive Analytics with Python

Product type Book
Published in Aug 2016
Publisher
ISBN-13 9781785882715
Pages 334 pages
Edition 1st Edition
Languages
Author (1):
Joseph Babcock Joseph Babcock
Profile icon Joseph Babcock

Table of Contents (16) Chapters

Mastering Predictive Analytics with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. From Data to Decisions – Getting Started with Analytic Applications 2. Exploratory Data Analysis and Visualization in Python 3. Finding Patterns in the Noise – Clustering and Unsupervised Learning 4. Connecting the Dots with Models – Regression Methods 5. Putting Data in its Place – Classification Methods and Analysis 6. Words and Pixels – Working with Unstructured Data 7. Learning from the Bottom Up – Deep Networks and Unsupervised Features 8. Sharing Models with Prediction Services 9. Reporting and Testing – Iterating on Analytic Systems Index

Persisting information with database systems


Our prediction service will use data in a number of ways. When we start the service, we have standard configurations we would like to retrieve (for example, the model parameters), and we might also like to log records of the requests that the application responds to for debugging purposes. As we score data or prepare trained models, we would ideally like to store these somewhere in case the prediction service needs to be restarted. Finally, as we will discuss in more detail, a database can allow us to keep track of application state (such as which tasks are in progress). For all these uses, a number of database systems can be applied.

Databases are generally categorized into two groups: relational and non-relational. Relational databases are probably familiar to you, as they are used in most business data warehouses. Data is stored in the form of tables, often with facts (such as purchases or search events) containing columns (such as user account...

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