Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner

✍ Scribed by Vijay Kotu, Bala Deshpande

• Publisher: Morgan Kaufmann
• Year: 2014
• Tongue: English
• Leaves: 426
• Edition: 1
• Category: Library

✦ Synopsis

Put Predictive Analytics into Action Learn 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

• 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

✦ Table of Contents

Content:
Front Matter, Page iii
Copyright, Page iv
Dedication, Page v
Foreword, Pages xi-xiii
Preface, Pages xv-xvii
Acknowledgments, Page xix
Chapter 1 - Introduction, Pages 1-16
Chapter 2 - Data Mining Process, Pages 17-36
Chapter 3 - Data Exploration, Pages 37-61
Chapter 4 - Classification, Pages 63-163
Chapter 5 - Regression Methods, Pages 165-193
Chapter 6 - Association Analysis, Pages 195-216
Chapter 7 - Clustering, Pages 217-255
Chapter 8 - Model Evaluation, Pages 257-273
Chapter 9 - Text Mining, Pages 275-303
Chapter 10 - Time Series Forecasting, Pages 305-327
Chapter 11 - Anomaly Detection, Pages 329-345
Chapter 12 - Feature Selection, Pages 347-370
Chapter 13 - Getting Started with RapidMiner, Pages 371-406
Comparison of Data Mining Algorithms, Pages 407-416
Index, Pages 417-423
About the Authors, Page 425