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Synopsis
Predictive Modeling in R is a case-study based book emphasizing the iterative nature of the predictive modeling process. For each case study presented in Predictive Modeling in R , the four major phases of the modeling process are 1) data acquisition, cleaning, and reshaping; 2) exploratory data analysis; 3) model construction; and 4) model tuning and validation. At each phase, the authors describe the actual challenges encountered and the tools necessary for achieving successful predictive modeling with R. In practice, most of your data nor the analysis will come in a neatly organized package. So by working through the examples in detail, Predictive Modeling in R can help you develop into a smarter, more confident modeler. This Predictive Modeling in R is for people who are familiar with basic probability and statistics and common distributions like normal, exponential, and student-t, who have done some analysis using linear regression and maybe some general linear modeling. The reader should have basic knowledge of R, including data types, conditionals, loops, and the use of data frames. Familiarity with vectorized computations and apply family of functions will be helpful, but not required.
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About the Author
Eric Novik is an applied statistician with experience working in financial services and derivatives trading. He obtained an MA in Statistics from Columbia University where he teaches a seminar called Introduction to Statistical Computing in R. Eric has built systems for analyzing complex options portfolios and profitability of order flows in large block equities trading. He is also interested in the applications of Machine Learning to Natural Language Processing. His toolset currently includes R, ggplot, Matlab, and SQL. In his other life, Eric managed IT outsourcing projects for large Commercial and Federal clients.
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