Mastering Predictive Analytics with Python

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 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 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."