Foundations Of Predictive Analytics: Unveiling the Power of Data

Are you ready to unlock the potential of your data and make accurate predictions? Discover the fascinating world of predictive analytics with the eminent book, Foundations Of Predictive Analytics.

Understanding Predictive Analytics

In today's data-driven world, businesses are constantly exploring new methods to extract valuable insights from their vast databases. One powerful technique that has gained significant attention is predictive analytics.

Foundations of Predictive Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

by James Wu (1st Edition, Kindle Edition)

5 out of 5

Language	:	English
File size	:	9530 KB
Screen Reader	:	Supported
Print length	:	338 pages

FREE

Predictive analytics is the practice of using statistical algorithms and machine learning techniques to analyze historical data and make forecasts about future events or behaviors. It enables organizations to make informed decisions, identify trends, mitigate risks, and optimize business processes.

Why Foundations Of Predictive Analytics?

Chapman Hallcrc Data Mining And Knowledge Discovery Series presents Foundations Of Predictive Analytics, an indispensable guide for both beginners and experts seeking to master the art of predictive analytics.

Written by renowned data mining experts, this comprehensive book covers the fundamental concepts, methodologies, and applications of predictive analytics. It equips readers with the knowledge and skills necessary to harness the power of data for successful prediction.

With over 3000 pages packed with real-world examples, case studies, and hands-on exercises, this book offers a deep dive into the theory and practice of predictive analytics. No matter your level of expertise, the clearly explained concepts and practical examples will enable you to tackle complex analytical challenges in various domains.

Key Features

• Comprehensive Coverage: The book covers a wide range of topics, including data preprocessing, feature selection, multiple regression, decision trees, neural networks, genetic algorithms, and more.
• Real-world Applications: Numerous case studies demonstrate the application of predictive analytics in domains such as healthcare, finance, marketing, and customer relationship management.
• Practical Examples: Step-by-step examples and code snippets guide readers through the implementation of predictive models using popular programming languages such as Python and R.
• Hands-On Exercises: Challenge yourself with the thought-provoking exercises provided at the end of each chapter to reinforce your understanding and enhance your practical skills.
• Expert Insights: Benefit from the extensive knowledge and experience of the authors, who have made significant contributions to the field of data mining and predictive analytics.

Unlock the Power of Data Today

Whether you are an aspiring data scientist, a business professional looking to enhance decision-making, or an educator seeking to further your knowledge and teach the next generation, Foundations Of Predictive Analytics is a must-have reference that will empower you with the essential techniques and strategies for successful predictive modeling.

Don't miss out on the opportunity to transform your data into valuable insights. Get your copy of Foundations Of Predictive Analytics and embark on a journey that will revolutionize the way you approach data analysis and prediction.

Foundations of Predictive Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

by James Wu (1st Edition, Kindle Edition)

5 out of 5

Language	:	English
File size	:	9530 KB
Screen Reader	:	Supported
Print length	:	338 pages

FREE

Drawing on the authors’ two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It also discusses a variety of practical topics that are frequently missing from similar texts.

The book begins with the statistical and linear algebra/matrix foundation of modeling methods, from distributions to cumulant and copula functions to Cornish–Fisher expansion and other useful but hard-to-find statistical techniques. It then describes common and unusual linear methods as well as popular nonlinear modeling approaches, including additive models, trees, support vector machine, fuzzy systems, clustering, naïve Bayes, and neural nets. The authors go on to cover methodologies used in time series and forecasting, such as ARIMA, GARCH, and survival analysis. They also present a range of optimization techniques and explore several special topics, such as Dempster–Shafer theory.

An in-depth collection of the most important fundamental material on predictive analytics, this self-contained book provides the necessary information for understanding various techniques for exploratory data analysis and modeling. It explains the algorithmic details behind each technique (including underlying assumptions and mathematical formulations) and shows how to prepare and encode data, select variables, use model goodness measures, normalize odds, and perform reject inference.

Web Resource
The book’s website at www.DataMinerXL.com offers the DataMinerXL software for building predictive models. The site also includes more examples and information on modeling.