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

What you will learn

Gain an insight into components and design decisions for an analytical application

Get to grips with applying regression, classification, clustering, and deep learning algorithms

Discover the advanced methods to analyze structured and unstructured data

Find out how to deploy a machine learning model in a production environment

Visualize the performance of models and the insights they produce

Scale your solutions as your data grows using Python

Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis

About the Author

Joseph Babcock

has spent almost a decade exploring complex datasets and combining predictive modeling with visualization to understand correlations and forecast anticipated outcomes. He received a PhD from the Solomon H. Snyder Department of Neuroscience at The Johns Hopkins University School of Medicine, where he used machine learning to predict adverse cardiac side effects of drugs. Outside the academy, he has tackled big data challenges in the healthcare and entertainment industries.

Table of Contents

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

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