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Regression Analysis with R电子书

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74人正在读 | 0人评论 6.2

作       者:Giuseppe Ciaburro

出  版  社:Packt Publishing

出版时间:2018-01-31

字       数:49.9万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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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 - this book covers all regression techniques and their implementation in R ? A complete guide to building effective regression models in R and interpreting results from them to make valuable predictions Who This Book Is For This book is intended for budding data scientists and data analysts who want to implement regression analysis techniques using R. If you are interested in statistics, data science, machine learning and wants to get an easy introduction to the topic, then this book is what you need! Basic understanding of statistics and math will help you to get the most out of the book. Some programming experience with R will also be helpful What You Will Learn ? Get started with the journey of data science using Simple linear regression ? Deal with interaction, collinearity and other problems using multiple linear regression ? Understand diagnostics and what to do if the assumptions fail with proper analysis ? Load your dataset, treat missing values, and plot relationships with exploratory data analysis ? Develop a perfect model keeping overfitting, under-fitting, and cross-validation into consideration ? Deal with classification problems by applying Logistic regression ? Explore other regression techniques – Decision trees, Bagging, and Boosting techniques ? Learn by getting it all in action with the help of a real world case study. In Detail Regression analysis is a statistical process which enables prediction of relationships between variables. The predictions are based on the casual effect of one variable upon another. Regression techniques for modeling and analyzing are employed on large set of data in order to reveal hidden relationship among the variables. This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. The first few chapters give an understanding of what the different types of learning are – supervised and unsupervised, how these learnings differ from each other. We then move to covering the supervised learning in details covering the various aspects of regression analysis. The outline of chapters are arranged in a way that gives a feel of all the steps covered in a data science process – loading the training dataset, handling missing values, EDA on the dataset, transformations and feature engineering, model building, assessing the model fitting and performance, and finally making predictions on unseen datasets. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. The practical examples are illustrated using R code including the different packages in R such as R Stats, Caret and so on. Each chapter is a mix of theory and practical examples. By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects. Style and approach An easy-to-follow step by step guide which will help you get to grips with real world application of Regression Analysis with R
目录展开

Title Page

Title Page

Copyright and Credits

Copyright and Credits

Regression Analysis with R

Regression Analysis with R

Packt Upsell

Packt Upsell

Why subscribe?

Why subscribe?

PacktPub.com

PacktPub.com

Contributors

Contributors

About the author

About the author

About the reviewer

About the reviewer

Packt is searching for authors like you

Packt is searching for authors like you

Preface

Preface

Who this book is for

Who this book is for

What this book covers

What this book covers

To get the most out of this book

To get the most out of this book

Download the example code files

Download the example code files

Download the color images

Download the color images

Conventions used

Conventions used

Get in touch

Get in touch

Reviews

Reviews

Getting Started with Regression

Getting Started with Regression

Going back to the origin of regression

Going back to the origin of regression

Regression in the real world

Regression in the real world

Understanding regression concepts

Understanding regression concepts

Regression versus correlation

Regression versus correlation

Discovering different types of regression

Discovering different types of regression

The R environment

The R environment

Installing R

Installing R

Using precompiled binary distribution

Using precompiled binary distribution

Installing on Windows

Installing on Windows

Installing on macOS

Installing on macOS

Installing on Linux

Installing on Linux

Installation from source code

Installation from source code

RStudio

RStudio

R packages for regression

R packages for regression

The R stats package

The R stats package

The car package

The car package

The MASS package

The MASS package

The caret package

The caret package

The glmnet package

The glmnet package

The sgd package

The sgd package

The BLR package

The BLR package

The Lars package

The Lars package

Summary

Summary

Basic Concepts – Simple Linear Regression

Basic Concepts – Simple Linear Regression

Association between variables – covariance and correlation

Association between variables – covariance and correlation

Searching linear relationships

Searching linear relationships

Least squares regression

Least squares regression

Creating a linear regression model

Creating a linear regression model

Statistical significance test

Statistical significance test

Exploring model results

Exploring model results

Diagnostic plots

Diagnostic plots

Modeling a perfect linear association

Modeling a perfect linear association

Summary

Summary

More Than Just One Predictor – MLR

More Than Just One Predictor – MLR

Multiple linear regression concepts

Multiple linear regression concepts

Building a multiple linear regression model

Building a multiple linear regression model

Multiple linear regression with categorical predictor

Multiple linear regression with categorical predictor

Categorical variables

Categorical variables

Building a model

Building a model

Gradient Descent and linear regression

Gradient Descent and linear regression

Gradient Descent

Gradient Descent

Stochastic Gradient Descent

Stochastic Gradient Descent

The sgd package

The sgd package

Linear regression with SGD

Linear regression with SGD

Polynomial regression

Polynomial regression

Summary

Summary

When the Response Falls into Two Categories – Logistic Regression

When the Response Falls into Two Categories – Logistic Regression

Understanding logistic regression

Understanding logistic regression

The logit model

The logit model

Generalized Linear Model

Generalized Linear Model

Simple logistic regression

Simple logistic regression

Multiple logistic regression

Multiple logistic regression

Customer satisfaction analysis with the multiple logistic regression

Customer satisfaction analysis with the multiple logistic regression

Multiple logistic regression with categorical data

Multiple logistic regression with categorical data

Multinomial logistic regression

Multinomial logistic regression

Summary

Summary

Data Preparation Using R Tools

Data Preparation Using R Tools

Data wrangling

Data wrangling

A first look at data

A first look at data

Change datatype

Change datatype

Removing empty cells

Removing empty cells

Replace incorrect value

Replace incorrect value

Missing values

Missing values

Treatment of NaN values

Treatment of NaN values

Finding outliers in data

Finding outliers in data

Scale of features

Scale of features

Min–max normalization

Min–max normalization

z score standardization

z score standardization

Discretization in R

Discretization in R

Data discretization by binning

Data discretization by binning

Data discretization by histogram analysis

Data discretization by histogram analysis

Dimensionality reduction

Dimensionality reduction

Principal Component Analysis

Principal Component Analysis

Summary

Summary

Avoiding Overfitting Problems - Achieving Generalization

Avoiding Overfitting Problems - Achieving Generalization

Understanding overfitting

Understanding overfitting

Overfitting detection – cross-validation

Overfitting detection – cross-validation

Feature selection

Feature selection

Stepwise regression

Stepwise regression

Regression subset selection

Regression subset selection

Regularization

Regularization

Ridge regression

Ridge regression

Lasso regression

Lasso regression

ElasticNet regression

ElasticNet regression

Summary

Summary

Going Further with Regression Models

Going Further with Regression Models

Robust linear regression

Robust linear regression

Bayesian linear regression

Bayesian linear regression

Basic concepts of probability

Basic concepts of probability

Bayes' theorem

Bayes' theorem

Bayesian model using BAS package

Bayesian model using BAS package

Count data model

Count data model

Poisson distributions

Poisson distributions

Poisson regression model

Poisson regression model

Modeling the number of warp breaks per loom

Modeling the number of warp breaks per loom

Summary

Summary

Beyond Linearity – When Curving Is Much Better

Beyond Linearity – When Curving Is Much Better

Nonlinear least squares

Nonlinear least squares

Multivariate Adaptive Regression Splines

Multivariate Adaptive Regression Splines

Generalized Additive Model

Generalized Additive Model

Regression trees

Regression trees

Support Vector Regression

Support Vector Regression

Summary

Summary

Regression Analysis in Practice

Regression Analysis in Practice

Random forest regression with the Boston dataset

Random forest regression with the Boston dataset

Exploratory analysis

Exploratory analysis

Multiple linear model fitting

Multiple linear model fitting

Random forest regression model

Random forest regression model

Classifying breast cancer using logistic regression

Classifying breast cancer using logistic regression

Exploratory analysis

Exploratory analysis

Model fitting

Model fitting

Regression with neural networks

Regression with neural networks

Exploratory analysis

Exploratory analysis

Neural network model

Neural network model

Summary

Summary

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