Mastering Predictive Analytics with Python (Paperback)

Exploit the power of data in your business by predicting probabilities and trends and creating advanced analytic solutions with Python

About This Book

• Master the use of 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 black box thinking 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 work through a step-by-step process to turn raw data into powerful insights. Power-packed with case studies and code examples using popular open-source Python libraries, this volume illustrates the complete development process for analytic applications. The detailed examples illustrate robust and scalable applications for common use cases. You will learn to quickly apply these methods to your own data.

Covering a wide range of algorithms for classification, regression, clustering, and cutting-edge techniques such as deep learning, you will learn not only how these methods work, but how to implement them in practice. You will also gain the skill to choose the right approach for your problem. This guide also explains how to develop engaging visualizations from these algorithms, to bring the insights of predictive modeling to life for the analyst and their stakeholders.