Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner | 1 Edition

Details about Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner:

Data Mining has become an essential tool for any enterprise as vast amounts of data are gathered leading to what is now being popularly called "Big Data". Data is also termed as the new oil, and companies which know how to process/refine and harness this data are the ones which will thrive. Data Science or data mining is the art and science of finding useful patterns in the data and making it actionable. These patterns generate new insights and allow us to convert future uncertainties into actionable probabilities.

Whether you are brand new to data mining or working on your tenth predictive analytics project, Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner will show you how to analyze data and uncover hidden patterns and relationships to aid important decisions. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Vijay Kotu and Bala Deshpande show you the keys to mastering predictive analysis. You’ll be able to:

• Get a comprehensive understanding of different data mining techniques, be prepared to select the right technique for a given data problem and be ready to create a general purpose analytics process.
• Use RapidMiner, an open source, GUI based data mining tool to create and develop your own data mining processes without having to write complicated lines of programming code.
• Get up and running fast with 20 commonly used powerful techniques for predictive analysis
• Implement a simple 5 step process for implementing algorithms that can be used for performing predictive analytics.

• 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