Book Image

Practical Machine Learning with R

By : Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu
Book Image

Practical Machine Learning with R

By: Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu

Overview of this book

With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it.
Table of Contents (8 chapters)

Applications of Clustering

Clustering is useful in a variety of fields. We will look at two of them in detail:

  • Market segmentation: Segmentation is the process of splitting a heterogeneous group of consumers or customers into smaller homogeneous groups. These smaller groups can be targeted differently based on their characteristics and behavior. Segmentation is important to businesses because it shapes both marketing efforts and product development. Businesses designing products must decide which product is targeted to which segment of consumers or customers. They must consequently decide what features to include, how to price the product, and how to take it to market. All these actions are heavily influenced by the characteristics of the different segments.

    Most businesses start off not knowing how to group their customers. Clustering is useful in helping these businesses to identify segments in their data.

  • Document clustering and information retrieval: In the information and knowledge...