Exploit the power of data in your business by building advanced predictive modeling applications 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
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
Style and approach
This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to convey insights through insightful charts and reporting.
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