Mastering Predictive Analytics with Python (Paperback or Softback)

Uncover the insights your business needs with powerful predictive algorithms and advanced analytic solutions with Python

About This Book

• Master open source Python tools to build sophisticated predictive models
• Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide
• Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understanding

Who This Book Is For

This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python.

What You Will Learn

• Gain an insight into components and design decisions for an analytical application
• Master the use Python notebooks for exploratory data analysis and rapid prototyping
• 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

In Detail

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

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