Regression Analysis with R

Build effective regression models in R to extract valuable insights from real data
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Regression Analysis with R

Giuseppe Ciaburro

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Build effective regression models in R to extract valuable insights from real data
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Book Details

ISBN 139781788627306
Paperback422 pages

Book Description

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.

Table of Contents

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
RStudio
R packages for regression
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
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
Gradient Descent and linear regression
Polynomial regression
Summary
Chapter 4: When the Response Falls into Two Categories – Logistic Regression
Understanding logistic regression
Generalized Linear Model
Multiple logistic regression
Multinomial logistic regression
Summary
Chapter 5: Data Preparation Using R Tools
Data wrangling
Finding outliers in data
Scale of features
Discretization in R
Dimensionality reduction
Summary
Chapter 6: Avoiding Overfitting Problems - Achieving Generalization
Understanding overfitting
Feature selection
Regularization
Summary
Chapter 7: Going Further with Regression Models
Robust linear regression
Bayesian linear regression
Count data model
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
Classifying breast cancer using logistic regression
Regression with neural networks
Summary

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.

Authors

Table of Contents

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
RStudio
R packages for regression
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
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
Gradient Descent and linear regression
Polynomial regression
Summary
Chapter 4: When the Response Falls into Two Categories – Logistic Regression
Understanding logistic regression
Generalized Linear Model
Multiple logistic regression
Multinomial logistic regression
Summary
Chapter 5: Data Preparation Using R Tools
Data wrangling
Finding outliers in data
Scale of features
Discretization in R
Dimensionality reduction
Summary
Chapter 6: Avoiding Overfitting Problems - Achieving Generalization
Understanding overfitting
Feature selection
Regularization
Summary
Chapter 7: Going Further with Regression Models
Robust linear regression
Bayesian linear regression
Count data model
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
Classifying breast cancer using logistic regression
Regression with neural networks
Summary

Book Details

ISBN 139781788627306
Paperback422 pages
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