R
Subtitle: predictive analysis : master the art of predictive modeling
By: Tony Fischetti, Eric Mayor, Rui Miguel
Contributors: Mayor, Eric, author; Forte, Rui Miguel, author
Publisher: Packt Publishing
Published: 2017
Place of publication: Birmingham, UK
Language: eng
ISBN-13: 9781788290852; 9781788290371
Subjects: R (Computer program language); Quantitative research; Data mining; R (Langage de programmation); Recherche quantitative; Exploration de données (Informatique); COMPUTERS / Databases / Data Mining; COMPUTERS / Programming Languages / General
Description: Master the art of predictive modeling About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Familiarize yourself with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, Naïve Bayes, decision trees, text mining and so on. We emphasize important concepts, such as the bias-variance trade-off and over-fitting, which are pervasive in predictive modeling Who This Book Is For If you work with data and want to become an expert in predictive analysis and modeling, then this Learning Path will serve you well. It is intended for budding and seasoned practitioners of predictive modeling alike. You should have basic knowledge of the use of R, although it's not necessary to put this Learning Path to great use. What You Will Learn Get to know the basics of R's syntax and major data structures Write functions, load data, and install packages Use different data sources in R and know how to interface with databases, and request and load JSON and XML Identify the challenges and apply your knowledge about data analysis in R to imperfect real-world data Predict the future with reasonably simple algorithms Understand key data visualization and predictive analytic skills using R Understand the language of models and the predictive modeling process In Detail Predictive analytics is a field that uses data to build models that predict a future outcome of interest. It can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. This Learning Path will provide you with all the steps you need to master the art of predictive modeling with R. We start with an introduction to data analysis with R, and then gradually you'll get your feet wet with predictive modeling. You will get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. You will be able to solve the difficulties relating to performing data analysis in practice and find solutions to working with ?messy data?, large data, communicating results, and facilitating reproducibility. You will then perform key predictive...
Notes: "Learning path."

Includes bibliographical references and index.
Table of contents: title: Cover; Copyright; Credits; Preface; Table of Content; Module 1: Data Analysis with R; Chapter 1: RefresheR; Navigating the basics; Getting help in R; Vectors; Functions; Matrices; Loading data into R; Working with packages; Chapter 2: The Shape of Data; Univariate data; Frequency distributions; Central tendency; Spread; Populations, samples, and estimation; Probability distributions; Visualization methods; Exercises; Summary; Chapter 3: Describing Relationships; Multivariate data; Relationships between a categorical and a continuous variable; Relationships between two categorical variables.; title: The relationship between two continuous variablesVisualization methods; Exercises; Summary; Chapter 4: Probability; Basic probability; A tale of two interpretations; Sampling from distributions; The normal distribution; Exercises; Summary; Chapter 5: Using Data to Reason About the World; Estimating means; The sampling distribution; Interval estimation; Smaller samples; Exercises; Summary; Chapter 6: Testing Hypotheses; Null Hypothesis Significance Testing; Testing the mean of one sample; Testing two means; Testing more than two means; Testing independence of proportions.; title: What if my assumptions are unfounded?Exercises; Summary; Chapter 7: Bayesian Methods; The big idea behind Bayesian analysis; Choosing a prior; Who cares about coin flips; Enter MCMC; title: stage left; Using JAGS and runjags; Fitting distributions the Bayesian way; The Bayesian independent samples t-test; Exercises; Summary; Chapter 8: Predicting Continuous Variables; Linear models; Simple linear regression; Simple linear regression with a binary predictor; Multiple regression; Regression with a non-binary predictor; Kitchen sink regression; The bias-variance trade-off.; title: Linear regression diagnosticsAdvanced topics; Exercises; Summary; Chapter 9: Predicting Categorical Variables; k-Nearest Neighbors; Logistic regression; Decision trees; Random forests; Choosing a classifier; Exercises; Summary; Chapter 10: Sources of Data; Relational Databases; Using JSON; XML; Other data formats; Online repositories; Exercises; Summary; Chapter 11: Dealing with Messy Data; Analysis with missing data; Analysis with unsanitized data; Other messiness; Exercises; Summary; Chapter 12: Dealing with Large Data; Wait to optimize; Using a bigger and faster machine.; title: Be smart about your codeUsing optimized packages; Using another R implementation; Use parallelization; Using Rcpp; Be smarter about your code; Exercises; Summary; Chapter 13: Reproducibility and Best Practices; R Scripting; R projects; Version control; Communicating results; Exercises; Summary; Module 2: Learning Predictive Analytics with R; Chapter 1: Visualizing and Manipulating Data Using R; The roulette case; Histograms and bar plots; Scatterplots; Boxplots; Line plots; Application; title: Outlier detection; Formatting plots; Summary; Chapter 2: Data Visualization with Lattice.