Predictive Analytics in R: From Data Acquisition to Validation

9781430259688: Predictive Analytics in R: From Data Acquisition to Validation

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 covered: 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 book:
  • Uses a practical, case-study approach to explain key concepts and techniques in the predictive modeling process with R.
  • Takes you through the steps of a real predictive analysis from data acquisition to model validation.
  • Teaches common approaches to modeling in genetics, social media, marketing, and algorithmic trading.
  • Acknowledges that formal modeling is a small part of the framework, and emphasizes data and model visualizations and comparisons.

What you’ll learn

  • Build your own financial data repository using a SQL database, and integrate it with R.
  • Access the Twitter firehose from R, and prepare social media data for analysis.
  • Make extensive use of the ggplot library to explore relationships in your data and to visualize your models.
  • Learn to apply natural language processing techniques to find meaning in text.
  • Understand how to use principle component analysis to uncover structure in high dimensional genetics dataset.
  • Learn key components of a multi-level marketing attribution model, and develop your own algorithmic trading system.

Who this book is for

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.

Jacqueline Buros is a Senior Data Scientist at Random House with extensive experience in statistical modeling, exploratory data analysis, and programming Web based systems. She has been developing longitudinal, multilevel, and SEM models for purposes of marketing attribution, genomics, and clinical research, and she often grapples with acquiring and manipulating unstructured and inconsistently encoded data. Jacki also routinely works with datasets in excess of 2 Terabytes. She has published over 30 academic papers in Bioinformatics and Clinical Research. Jacki has a BA in Biology from Dartmouth College.

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