This is the code repository for Regression Analysis with R, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
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.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
$ mkdir css
$ cd css
This book is focused on regression analysis in an R environment. We have used R version 3.4.2 to build various applications and the open source and enterprise-ready professional software for R, RStudio version 1.0.153. We've focused on how to utilize various R libraries in the best possible way to build real-world applications. These libraries (called packages) are available for free at the following URL: https://cran.r-project.org/web/packages/index.html. In that spirit, we have tried to keep all of the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.
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