Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, 2nd Edition

Book description

Use Predictive Analytics to Uncover Hidden Patterns and Correlations and Improve Decision-Making

Using predictive analytics techniques, decision-makers 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 state-of-the-art 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 real-world 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 time-series forecasting)

* Understand predictive algorithms drawn from traditional statistics and advanced machine learning

* Discover cutting-edge techniques, and explore advanced applications ranging from sentiment analysis to fraud detection


Table of contents

  1. Cover Page
  2. About This eBook
  3. Title Page
  4. Copyright Page
  5. Dedication
  6. Contents at a Glance
  7. Contents
  8. Foreword
  9. Acknowledgments
  10. About the Author
  11. Credits
  12. 1. Introduction to Analytics
    1. What’s in a Name?
    2. Why the Sudden Popularity of Analytics and Data Science?
    3. The Application Areas of Analytics
    4. The Main Challenges of Analytics
    5. A Longitudinal View of Analytics
    6. A Simple Taxonomy for Analytics
    7. The Cutting Edge of Analytics: IBM Watson
    8. Summary
    9. References
  13. 2. Introduction to Predictive Analytics and Data Mining
    1. What Is Data Mining?
    2. What Data Mining Is Not
    3. The Most Common Data Mining Applications
    4. What Kinds of Patterns Can Data Mining Discover?
    5. Popular Data Mining Tools
    6. The Dark Side of Data Mining: Privacy Concerns
    7. Summary
    8. References
  14. 3. Standardized Processes for Predictive Analytics
    1. The Knowledge Discovery in Databases (KDD) Process
    2. Cross-Industry Standard Process for Data Mining (CRISP-DM)
    3. SEMMA
    4. SEMMA Versus CRISP-DM
    5. Six Sigma for Data Mining
    6. Which Methodology Is Best?
    7. Summary
    8. References
  15. 4. Data and Methods for Predictive Analytics
    1. The Nature of Data in Data Analytics
    2. Preprocessing of Data for Analytics
    3. Data Mining Methods
    4. Prediction
    5. Classification
    6. Decision Trees
    7. Cluster Analysis for Data Mining
    8. k-Means Clustering Algorithm
    9. Association
    10. Apriori Algorithm
    11. Data Mining and Predictive Analytics Misconceptions and Realities
    12. Summary
    13. References
  16. 5. Algorithms for Predictive Analytics
    1. Naive Bayes
    2. Nearest Neighbor
    3. Similarity Measure: The Distance Metric
    4. Artificial Neural Networks
    5. Support Vector Machines
    6. Linear Regression
    7. Logistic Regression
    8. Time-Series Forecasting
    9. Summary
    10. References
  17. 6. Advanced Topics in Predictive Modeling
    1. Model Ensembles
    2. Bias–Variance Trade-off in Predictive Analytics
    3. Imbalanced Data Problems in Predictive Analytics
    4. Explainability of Machine Learning Models for Predictive Analytics
    5. Summary
    6. References
  18. 7. Text Analytics, Topic Modeling, and Sentiment Analysis
    1. Natural Language Processing
    2. Text Mining Applications
    3. The Text Mining Process
    4. Text Mining Tools
    5. Topic Modeling
    6. Sentiment Analysis
    7. Summary
    8. References
  19. 8. Big Data for Predictive Analytics
    1. Where Does Big Data Come From?
    2. The Vs That Define Big Data
    3. Fundamental Concepts of Big Data
    4. The Business Problems That Big Data Analytics Addresses
    5. Big Data Technologies
    6. Data Scientists
    7. Big Data and Stream Analytics
    8. Data Stream Mining
    9. Summary
    10. References
  20. 9. Deep Learning and Cognitive Computing
    1. Introduction to Deep Learning
    2. Basics of “Shallow” Neural Networks
    3. Elements of an Artificial Neural Network
    4. Deep Neural Networks
    5. Convolutional Neural Networks
    6. Recurrent Networks and Long Short-Term Memory Networks
    7. Computer Frameworks for Implementation of Deep Learning
    8. Cognitive Computing
    9. Summary
    10. References
  21. A. KNIME and the Landscape of Tools for Business Analytics and Data Science
    1. Project Constraints: Time and Money
    2. The Learning Curve
    3. The KNIME Community
    4. Correctness and Flexibility
    5. Extensive Coverage of Data Science Techniques
    6. Data Science in the Enterprise
    7. Summary and Conclusions
    8. Acknowledgment
  22. Index

Product information

  • Title: Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, 2nd Edition
  • Author(s): Dursun Delen
  • Release date: December 2020
  • Publisher(s): Pearson FT Press
  • ISBN: 9780135946527