Predictive Analytics and Data Mining

Predictive Analytics and Data Mining

Concepts and Practice with RapidMiner

1st Edition - November 27, 2014

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  • Authors: Vijay Kotu, Bala Deshpande
  • eBook ISBN: 9780128016503
  • Paperback ISBN: 9780128014608

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Put Predictive Analytics into ActionLearn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining.You’ll be able to:1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process.2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases.3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at

Key Features

  • Demystifies data mining concepts with easy to understand language
  • Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis
  • Explains the process of using open source RapidMiner tools
  • Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics
  • Includes practical use cases and examples


Data Analysts, Business Intelligence and Data Warehousing Professionals, and Business Analysts

Table of Contents

    • Dedication
    • Foreword
    • Preface
    • Acknowledgments
    • Chapter 1. Introduction
      • 1.1. What Data Mining Is
      • 1.2. What Data Mining is Not
      • 1.3. The Case for Data Mining
      • 1.4. Types of Data Mining
      • 1.5. Data Mining Algorithms
      • 1.6. Roadmap for Upcoming Chapters
    • Chapter 2. Data Mining Process
      • 2.1. Prior Knowledge
      • 2.2. Data Preparation
      • 2.3. Modeling
      • 2.4. Application
      • 2.5. Knowledge
      • What’s Next?
    • Chapter 3. Data Exploration
      • 3.1. Objectives of Data Exploration
      • 3.2. Data Sets
      • 3.3. Descriptive Statistics
      • 3.4. Data Visualization
      • 3.5. Roadmap for Data Exploration
    • Chapter 4. Classification
      • 4.1. Decision Trees
      • 4.2. Rule Induction
      • 4.3. k-Nearest Neighbors
      • 4.4. Naïve Bayesian
      • 4.5. Artificial Neural Networks
      • 4.6. Support Vector Machines
      • 4.7. Ensemble Learners
    • Chapter 5. Regression Methods
      • 5.1. Linear Regression
      • 5.2. Logistic Regression
      • Conclusion
    • Chapter 6. Association Analysis
      • 6.1. Concepts of Mining Association Rules
      • 6.2. Apriori Algorithm
      • 6.3. FP-Growth Algorithm
      • Conclusion
    • Chapter 7. Clustering
      • Clustering to Describe the Data
      • Clustering for Preprocessing
      • 7.1. Types of Clustering Techniques
      • 7.2. k-Means Clustering
      • 7.3. DBSCAN Clustering
    • Chapter 8. Model Evaluation
      • 8.1. Confusion Matrix (or Truth Table)
      • 8.2. Receiver Operator Characteristic (ROC) Curves and Area under the Curve (AUC)
      • 8.3. Lift Curves
      • 8.4. Evaluating The Predictions: Implementation
      • Conclusion
    • Chapter 9. Text Mining
      • 9.1. How Text Mining Works
      • 9.2. Implementing Text Mining with Clustering and Classification
      • Conclusion
    • Chapter 10. Time Series Forecasting
      • 10.1. Data-Driven Approaches
      • 10.2. Model-Driven Forecasting Methods
      • Conclusion
    • Chapter 11. Anomaly Detection
      • 11.1. Anomaly Detection Concepts
      • 11.2. Distance-Based Outlier Detection
      • 11.3. Density-Based Outlier Detection
      • 11.4. Local Outlier Factor
      • Conclusion
    • Chapter 12. Feature Selection
      • 12.1. Classifying Feature Selection Methods
      • 12.2. Principal Component Analysis
      • 12.3. Information Theory–Based Filtering for Numeric Data
      • 12.4. Chi-Square-Based Filtering for Categorical Data
      • 12.5. Wrapper-Type Feature Selection
      • Conclusion
    • Chapter 13. Getting Started with RapidMiner
      • 13.1. User Interface and Terminology
      • 13.2. Data Importing and Exporting Tools
      • 13.3. Data Visualization Tools
      • 13.4. Data Transformation Tools
      • 13.5. Sampling and Missing Value Tools
      • 13.6. Optimization Tools
      • Conclusion
    • Comparison of Data Mining Algorithms
    • Index
    • About the Authors

Product details

  • No. of pages: 448
  • Language: English
  • Copyright: © Morgan Kaufmann 2014
  • Published: November 27, 2014
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9780128016503
  • Paperback ISBN: 9780128014608

About the Authors

Vijay Kotu

Vijay Kotu is Vice President of Analytics at ServiceNow. He leads the implementation of large-scale data platforms and services to support the company's enterprise business. He has led analytics organizations for over a decade with focus on data strategy, business intelligence, machine learning, experimentation, engineering, enterprise adoption, and building analytics talent. Prior to joining ServiceNow, he was Vice President of Analytics at Yahoo. He worked at Life Technologies and Adteractive where he led marketing analytics, created algorithms to optimize online purchasing behavior, and developed data platforms to manage marketing campaigns. He is a member of the Association of Computing Machinery and a member of the Advisory Board at RapidMiner.

Affiliations and Expertise

Vice President of Analytics at ServiceNow

Bala Deshpande

Dr. Deshpande has extensive experience in working with companies ranging from startups to Fortune 5 in fields ranging from automotive, aerospace, retail, food, and manufacturing verticals delivering business analysis; designing and developing custom data products for implementing business intelligence, data science, and predictive analytics solutions. He was the Founder of SimaFore, a predictive analytics consulting company which was acquired by Soliton Inc., a provider of testing solutions for the semiconductor industry. He was also the Founding Co-chair of the annual Predictive Analytics World-Manufacturing conference. In his professional career he has worked with Ford Motor Company on their product development, with IBM at their IBM Watson Center of Competence, and with Domino’s Pizza at their data science and artificial intelligence groups. He has a Ph.D. from Carnegie Mellon and an MBA from Ross School of Business, Michigan.

Affiliations and Expertise

Founder, SimaFore

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