Regression Analysis with R

Extend your analysis options by using regression instead of traditional analysis
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Regression Analysis with R

Giuseppe Ciaburro

Extend your analysis options by using regression instead of traditional analysis

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Book Details

ISBN 139781788627306
Paperback261 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 arrange 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. 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

What You Will Learn

  • The different types of learning in data science and how they differ
  • Get started with the journey of data science using Simple linear regression
  • Learn how to deal with interaction, collinearity and other problems using multiple linear regression
  • Understand diagnostics and what to do if the assumptions fail with proper analysis
  • Loading dataset, treating missing values, plotting relationships with exploratory data analysis
  • Develop a perfect model keeping Overfitting, under-fitting, and cross-validation into consideration
  • Dealing with classification problems by applying Logistic regression
  • Explore other regression techniques – decision trees, bagging, boosting techniques
  • Learn by getting it all in action with the help of a real world case study.

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Table of Contents

Book Details

ISBN 139781788627306
Paperback261 pages
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