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R : predictive analysis : master the art of predictive modeling /

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Fischetti, Tony (Autor), Mayor, Eric (Autor), Forte, Rui Miguel (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2017.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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
  • Outlier detection; Formatting plots; Summary; Chapter 2: Data Visualization with Lattice.