Regression analysis with R : design and develop statistical nodes to identify unique relationships within data at scale

Regression analysis with R design and develop statistical nodes to identify unique relationships within data at scale

Build effective regression models in R to extract valuable insights from real data About This Book Implement different regression analysis techniques to solve common problems in data science - from data exploration to dealing with missing values From Simple Linear Regression to Logistic Regression -...

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Detalles Bibliográficos
Otros Autores: Ciaburro, Giuseppe, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England ; Mumbai, [India] : Packt 2018.
Edición:1st edition
Materias:
R (Computer program language)
Regression analysis.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630604106719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Packt Upsell
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Getting Started with Regression
  • Going back to the origin of regression
  • Regression in the real world
  • Understanding regression concepts
  • Regression versus correlation
  • Discovering different types of regression
  • The R environment
  • Installing R
  • Using precompiled binary distribution
  • Installing on Windows
  • Installing on macOS
  • Installing on Linux
  • Installation from source code
  • RStudio
  • R packages for regression
  • The R stats package
  • The car package
  • The MASS package
  • The caret package
  • The glmnet package
  • The sgd package
  • The BLR package
  • The Lars package
  • Summary
  • Chapter 2: Basic Concepts - Simple Linear Regression
  • Association between variables - covariance and correlation
  • Searching linear relationships
  • Least squares regression
  • Creating a linear regression model
  • Statistical significance test
  • Exploring model results
  • Diagnostic plots
  • Modeling a perfect linear association
  • Summary
  • Chapter 3: More Than Just One Predictor - MLR
  • Multiple linear regression concepts
  • Building a multiple linear regression model
  • Multiple linear regression with categorical predictor
  • Categorical variables
  • Building a model
  • Gradient Descent and linear regression
  • Gradient Descent
  • Stochastic Gradient Descent
  • The sgd package
  • Linear regression with SGD
  • Polynomial regression
  • Summary
  • Chapter 4: When the Response Falls into Two Categories - Logistic Regression
  • Understanding logistic regression
  • The logit model
  • Generalized Linear Model
  • Simple logistic regression
  • Multiple logistic regression
  • Customer satisfaction analysis with the multiple logistic regression
  • Multiple logistic regression with categorical data.
  • Multinomial logistic regression
  • Summary
  • Chapter 5: Data Preparation Using R Tools
  • Data wrangling
  • A first look at data
  • Change datatype
  • Removing empty cells
  • Replace incorrect value
  • Missing values
  • Treatment of NaN values
  • Finding outliers in data
  • Scale of features
  • Min-max normalization
  • z score standardization
  • Discretization in R
  • Data discretization by binning
  • Data discretization by histogram analysis
  • Dimensionality reduction
  • Principal Component Analysis
  • Summary
  • Chapter 6: Avoiding Overfitting Problems - Achieving Generalization
  • Understanding overfitting
  • Overfitting detection - cross-validation
  • Feature selection
  • Stepwise regression
  • Regression subset selection
  • Regularization
  • Ridge regression
  • Lasso regression
  • ElasticNet regression
  • Summary
  • Chapter 7: Going Further with Regression Models
  • Robust linear regression
  • Bayesian linear regression
  • Basic concepts of probability
  • Bayes' theorem
  • Bayesian model using BAS package
  • Count data model
  • Poisson distributions
  • Poisson regression model
  • Modeling the number of warp breaks per loom
  • Summary
  • Chapter 8: Beyond Linearity - When Curving Is Much Better
  • Nonlinear least squares
  • Multivariate Adaptive Regression Splines
  • Generalized Additive Model
  • Regression trees
  • Support Vector Regression
  • Summary
  • Chapter 9: Regression Analysis in Practice
  • Random forest regression with the Boston dataset
  • Exploratory analysis
  • Multiple linear model fitting
  • Random forest regression model
  • Classifying breast cancer using logistic regression
  • Exploratory analysis
  • Model fitting
  • Regression with neural networks
  • Exploratory analysis
  • Neural network model
  • Summary
  • Other Books You May Enjoy
  • Index.

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