Book Image

R Machine Learning By Example

By : Raghav Bali
Book Image

R Machine Learning By Example

By: Raghav Bali

Overview of this book

Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems. You’ll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms. Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.
Table of Contents (15 chapters)
R Machine Learning By Example
About the Authors
About the Reviewer

Data preprocessing

In this section, we will be focusing on data preprocessing which includes data cleaning, transformation, and normalizations if required. Basically, we perform operations to get the data ready before we start performing any analysis on it.

Dealing with missing values

There will be situations when the data you are dealing with will have missing values, which are often represented as NA in R. There are several ways to detect them and we will show you a couple of ways next. Note that there are several ways in which you can do this.

> # check if data frame contains NA values
> sum(
[1] 0
> # check if total records reduced after removing rows with NA 
> # values
> sum(complete.cases(credit.df))
[1] 1000

The function is really useful as it helps in finding out if any element has an NA value in the dataset. There is another way of doing the same by using the complete.cases function, which essentially returns a logical vector saying whether...