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Predictive Analytics and Data Mining,

Edition 1 Concepts and Practice with RapidMiner

Editors: By Vijay Kotu and Bala Deshpande

Publication Date: 03 Dec 2014

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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 www.LearnPredictiveAnalytics.com

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

About the author

By Vijay Kotu, Vice President of Analytics at ServiceNow and Bala Deshpande, Founder, SimaFore

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

Title Reviews

"...an excellent introductory data science textbook to expose students to the essential concepts in predictive analytics. For the seasoned professional, it can serve as a handy reference book to choose the best predictive analytics tool for a given data set."—Computing Reviews

"...ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining."—AnalyticBridge.com, 2015

"If learning-by-doing is your mantra — as well it should be for predictive analytics — this book will jumpstart your practice. Covering a broad, foundational collection of techniques, authors Kotu and Deshpande deliver crystal-clear explanations of the analytical methods that empower organizations to learn from data. After each concept, screenshots make the 'how to' immediately concrete, revealing the steps needed to set things up and go; you're guided through real hands-on execution." —Eric Siegel, Ph.D., founder of Predictive Analytics World and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

"The analytics that turns Big Data into actionable intelligence is no longer the exclusive realm of data scientists - it is impacting nearly every business function. Business intelligence is a significant competitive advantage, if applied properly. Gaining that advantage requires that business decision makers and data analyst have a good understanding of the available analytics tools and how to apply them. Predictive Analytics and Data Mining book provides an easy to understand framework of predictive analytics and data mining concepts. The framework is reinforced with examples and sample datasets that demonstrate how to apply the new tools to real-world problems. I highly recommend this book to anyone who wants a better understanding of how to make analytics a game changer for your organization"—David Dowhan, President, TruSignal

"Predictive analytics and insights has become the most critical skill-set in decision making and running the modern business. Predictive Analytics and Data Mining provides you the advanced concepts and practical implementation techniques to incorporate analytics in your business process. The two dozen data mining algorithms covered in this book forms the underpinnings of the field of business analytics that has transformed the way data is treated in business. So far, advanced analytics has been practiced only by select few. With your interest and this book, you can master it too."—Sy Fahimi, Operating Partner & Executive-in-Residence, Symphony Technology Group

"There are two kinds of predictive analytics books. One kind gives a high level informal overview to those who just want to understand this field conceptually. Another kind gives a textbook technical introduction for the experts with extensive knowledge of statistics and computer science. Kotu and Deshpande's book is unusual because it strikes a nice balance. It can be understood by anyone who can understand basic computations of probability. The choice of RapidMiner is brilliant, since anyone who has used excel in the past can follow the hands-on examples, and learn to get more out of their data. The book does an excellent job of appealing to our intuitions about probabilities and then expanding on these intuitions to cover very advanced topics, such as decision trees and association rules. The material is accessible to anyone who took elementary statistics in college, even if college was many years ago."--Maria Stone, Vice President, Data and User Experience, Yahoo

ISBN: 9780128014608

Page Count: 448

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