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

Format:

Book

Author/Creator:

Ciaburro, Giuseppe, author.

Language:

English

Subjects (All):

R (Computer program language).

Regression analysis.

Physical Description:

1 online resource (416 pages) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt, 2018.

System Details:

text file

Biography/History:

Ciaburro Giuseppe: Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Universita degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).

Summary:

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 – l...

Contents:

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

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

Linear regression with SGD

Polynomial regression

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

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

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

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

Chapter 8: Beyond Linearity - When Curving Is Much Better

Nonlinear least squares

Multivariate Adaptive Regression Splines

Generalized Additive Model

Regression trees

Support Vector Regression

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

Model fitting

Regression with neural networks

Neural network model

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Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (EBC, viewed March 6, 2018).

OCLC:

1026400915