About This Book
- Develop a sound strategy for solving predictive modeling problems using the most popular data mining algorithms
- Gain understanding of the major methods of predictive modeling
- Packed with practical advice and tips to help you get to grips with data mining
Who This Book Is For
This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining concepts and processes.
What You Will Learn
- Discover how you can manipulate data with R using code snippets
- Get to know the top classification algorithms written in R
- Develop best practices in the fields of graph mining and network analysis
- Find out the solutions to mine text and web data with appropriate support from R
- Familiarize yourself with algorithms written in R for spatial data mining, text mining, and web data mining
- Explore solutions written in R based on RHadoop projects
In Detail
Being able to deal with the array of problems that you may encounter during complex statistical projects can be difficult. If you have only a basic knowledge of R, this book will provide you with the skills and knowledge to successfully create and customize the most popular data mining algorithms to overcome these difficulties.
You will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. Discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on RHadoop projects. You will finish this book feeling confident in your ability to know which data mining algorithm to apply in any situation.
