Book Image

Regression Analysis with R

By : Giuseppe Ciaburro
Book Image

Regression Analysis with R

By: Giuseppe Ciaburro

Overview of this book

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 (15 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Summary


In this chapter, we explored the data preparation techniques to obtain a high-performing regression analysis. These techniques can improve the quality of the data, thereby helping to improve the accuracy and efficiency of the subsequent knowledge extraction process. Analyzing data that has not been carefully screened for such problems can produce misleading results. For this reason, we have to get the data into a form that the algorithm can use to build a predictive analytical model. We started by discovering different ways to transform data, and the degree of cleaning the data. We analyzed the techniques available for the preparation of the most suitable data for analysis and modeling, which includes imputation of missing data, detecting and eliminating outliers, and adding derived variables.

Then we learned how to scale the data, in which data units are eliminated, allowing you to easily compare data from different locations. Data scaling is a preprocessing technique usually employed...