Regression analysis is a statistical process that enables predictions of relationships between variables. The predictions are based on the effect of one variable on another. Regression techniques for modeling and analyzing are employed on large sets of data in order to reveal hidden relationships among the variables.
This book will give you a rundown of regression analysis and will explain the process from scratch. The first few chapters explain what the different types of learning are—supervised and unsupervised—and how they differ from each other. We then move on to cover supervised learning in detail, covering the various aspects of regression analysis. The chapters are arranged in such a way that they give 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 their understanding. The practical examples are illustrated using R code, including different packages in R such as R stats and caret. 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 what you have learnt in your projects.
