Author Description

Master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential conceptsAbout This Book• Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding• Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types• Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easilyWho This Book Is ForAlthough budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status, will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure.What You Will Learn• Master the steps involved in the predictive modeling process• Grow your expertise in using R and its diverse range of packages• Learn how to classify predictive models and distinguish which models are suitable for a particular problem• Understand steps for tidying data and improving the performing metrics• Recognize the assumptions, strengths, and weaknesses of a predictive model• Understand how and why each predictive model works in R• Select appropriate metrics to assess the performance of different types of predictive model• Explore word embedding and recurrent neural networks in R• Train models in R that can work on very large datasetsIn DetailR offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the f