In the following example, we will consider the problem of separating text messages sent between cell phone users. Some of these messages are spam advertisements, and the objective is to separate these from normal communications (Almeida, Tiago A., José María G. Hidalgo, and Akebo Yamakami. Contributions to the study of SMS spam filtering: new collection and results. Proceedings of the 11th ACM symposium on Document engineering. ACM, 2011). By looking for patterns of words that are typically found in spam advertisements, we could potentially derive a smart filter that would automatically remove these messages from a user's inbox. However, while in previous chapters we were concerned with fitting a predictive model for this kind of problem, here we will be shifting focus to cleaning up the data, removing noise, and extracting features. Once these tasks are done, either simple or lower-dimensional features can be input into many of the algorithms we have already studied...
Mastering Predictive Analytics with Python
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Mastering Predictive Analytics with Python
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Overview of this book
The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations.
In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services.
Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life
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Table of Contents (16 chapters)
Mastering Predictive Analytics with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
From Data to Decisions – Getting Started with Analytic Applications
Exploratory Data Analysis and Visualization in Python
Finding Patterns in the Noise – Clustering and Unsupervised Learning
Connecting the Dots with Models – Regression Methods
Putting Data in its Place – Classification Methods and Analysis
Words and Pixels – Working with Unstructured Data
Learning from the Bottom Up – Deep Networks and Unsupervised Features
Sharing Models with Prediction Services
Reporting and Testing – Iterating on Analytic Systems
Index
Customer Reviews