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

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

Author Dursun Delen
Release Date: 2020/12/01
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Book Description

Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data, and leverage this insight to improve a wide range of business decisions. In Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students. Delen’s holistic approach covers all this, and more:

  • Data mining processes, methods, and techniques

  • The role and management of data

  • Predictive analytics tools and metrics

  • Techniques for text and web mining, and for sentiment analysis

  • Integration with cutting-edge Big Data approaches

Throughout, Delen promotes understanding by presenting numerous conceptual illustrations, motivational success stories, failed projects that teach important lessons, and simple, hands-on tutorials that set this guide apart from competitors.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Contents
  4. Table of Contents
  5. Chapter 1. Introduction to Predictive Analytics
    1. What Are the Reasons for the Sudden Popularity of Analytics and Data Science?
    2. The Application Areas of Analytics
    3. The Main Challenges of Analytics
    4. A Longitudinal View of Analytics
    5. A Simple Taxonomy for Analytics
    6. The Cutting Edge of Analytics: IBM Watson
    7. Summary
    8. References
  6. Chapter 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. Application Example: Data Mining for Hollywood Managers
    8. Conclusions
    9. Summary
    10. References
  7. Chapter 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. An Exemplary Data Mining Case: Mining Cancer Data for New Knowledge
    8. Summary
    9. References
  8. Chapter 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
  9. Chapter 5. Algorithms for Predictive Analytics
    1. Naïve 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. Application Example: Data Mining for Complex Medical Procedures
    10. Summary
    11. References
  10. Chapter 6. Advanced Topics in Predictive Modeling
    1. Model Ensembles
    2. Bias-Variance Tradeoff in Predictive Analyztics
    3. Imbalanced Data Problem in Predictive Analytics
    4. Explainability of Machine Learning Models for Predictive Analytics
    5. Application Case: To Imprison or Not to Imprison—A Predictive Analytics-Based Decision Support System for Drug Courts
    6. Summary
    7. References
  11. Chapter 7. Text Analytics, Topic Modeling, and Sentiment Analysis
    1. Natural Language Processing
    2. Text Mining Applications
    3. The Text Mining Process
    4. Application Example: Text Mining of Research Literature
    5. Text Mining Tools
    6. Topic Modeling
    7. Sentiment Analysis
    8. Application Example: Text-Based Deception Detection
    9. Summary
    10. References
  12. Chapter 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. Application Example: Big Data for Political Campaigns
    10. Summary
    11. References
  13. Chapter 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. Application Case: Fighting Fraud with Deep Learning
    10. Summary
    11. References
  14. Appendix 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
    8. References
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