Book description
Use Predictive Analytics to Uncover Hidden Patterns and Correlations and Improve DecisionMaking
Using predictive analytics techniques, decisionmakers can uncover hidden patterns and correlations in their data and leverage these insights to improve many key business decisions. In this thoroughly updated guide, Dr. Dursun Delen illuminates stateoftheart best practices for predictive analytics for both business professionals and students.
Delen provides a holistic approach covering key data mining processes and methods, relevant data management techniques, tools and metrics, advanced text and web mining, big data integration, and much more. Balancing theory and practice, Delen presents intuitive conceptual illustrations, realistic example problems, and realworld case studiesincluding lessons from failed projects. It is all designed to help you gain a practical understanding you can apply for profit.
* Leverage knowledge extracted via data mining to make smarter decisions
* Use standardized processes and workflows to make more trustworthy predictions
* Predict discrete outcomes (via classification), numeric values (via regression), and changes over time (via timeseries forecasting)
* Understand predictive algorithms drawn from traditional statistics and advanced machine learning
* Discover cuttingedge techniques, and explore advanced applications ranging from sentiment analysis to fraud detection
.
Table of contents
 Cover Page
 About This eBook
 Title Page
 Copyright Page
 Dedication
 Contents at a Glance
 Contents
 Foreword
 Acknowledgments
 About the Author
 Credits
 1. Introduction to Analytics
 2. Introduction to Predictive Analytics and Data Mining
 3. Standardized Processes for Predictive Analytics

4. Data and Methods for Predictive Analytics
 The Nature of Data in Data Analytics
 Preprocessing of Data for Analytics
 Data Mining Methods
 Prediction
 Classification
 Decision Trees
 Cluster Analysis for Data Mining
 kMeans Clustering Algorithm
 Association
 Apriori Algorithm
 Data Mining and Predictive Analytics Misconceptions and Realities
 Summary
 References
 5. Algorithms for Predictive Analytics
 6. Advanced Topics in Predictive Modeling
 7. Text Analytics, Topic Modeling, and Sentiment Analysis
 8. Big Data for Predictive Analytics

9. Deep Learning and Cognitive Computing
 Introduction to Deep Learning
 Basics of “Shallow” Neural Networks
 Elements of an Artificial Neural Network
 Deep Neural Networks
 Convolutional Neural Networks
 Recurrent Networks and Long ShortTerm Memory Networks
 Computer Frameworks for Implementation of Deep Learning
 Cognitive Computing
 Summary
 References
 A. KNIME and the Landscape of Tools for Business Analytics and Data Science
 Index
Product information
 Title: Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, 2nd Edition
 Author(s):
 Release date: December 2020
 Publisher(s): Pearson FT Press
 ISBN: 9780135946527
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