Predictive Analytics and Data Mining

  • ID: 2899500
  • Book
  • 446 Pages
  • Elsevier Science and Technology
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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 [external URL]

  • 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
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  1. Introduction
  2. Data Mining Process
  3. Data Exploration
  4. Classification
  5. Regression
  6. Association
  7. Clustering
  8. Model Evaluation
  9. Text Mining
  10. Time Series
  11. Anomaly Detection
  12. Advanced Data Mining
  13. Getting Started with RapidMiner
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Kotu, Vijay
Vijay Kotu is Senior Director of Analytics at Yahoo. He leads the implementation of large-scale data and analytics systems to support the company's online business. He has practiced Analytics for over a decade with focus on business intelligence, data mining, web analytics, experimentation, information design, data warehousing, data engineering and developing analytical teams. Prior to joining Yahoo, he worked at Life Technologies and Adteractive where he led marketing analytics, created algorithms to optimize online purchase behaviors, and developed data platforms to manage marketing campaigns. He is a member of Association of Computing Machinery and is a certified Six Sigma Black Belt from American Society of Quality.
Deshpande, Bala
Bala Deshpande is the founder of SimaFore, a custom analytics app development and consulting company. He has more than 20 years of experience in using analytical techniques in a wide range of application areas. His first exposure to predictive models and analytics was in the field of biomechanics - in identifying correlations and building multiple regression models. He began his career as an engineering consultant following which he spent several years analyzing data from automobile crash tests and helping to build safer cars at Ford Motor Company. He is the co-chair of Predictive Analytics World - Manufacturing, an annual conference focused on promoting and evangelizing predictive analytics in the industry. He blogs regularly about data mining and predictive analytics for his company at www.simafore.com/blog. He holds a PhD in Bioengineering from Carnegie Mellon University and an MBA from Ross School of Business (Michigan).
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