Regression Analysis with R

More Information
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.
About

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.

Features
  • 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
Page Count 422
Course Length 12 hours 39 minutes
ISBN 9781788627306
Date Of Publication 31 Jan 2018

Authors

Giuseppe Ciaburro

Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master's degrees. His research was focused on machine learning applications in the study of urban sound environments. He works at the Built Environment Control Laboratory at the Università degli Studi della Campania Luigi Vanvitelli, Italy. He has over 15 years' professional experience in programming (Python, R, and MATLAB), first in the field of combustion, and then in acoustics and noise control. He has several publications to his credit.