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

Important concepts in predictive modeling

We already looked at several concepts when we talked about the machine learning pipeline. In this section, we will look at typical terms which are used in predictive modeling, and also discuss about model building and evaluation concepts in detail.

Preparing the data

The data preparation step, as discussed earlier, involves preparing the datasets necessary for feature selection and building the predictive models using the data. We frequently use the following terms in this context:

  • Datasets: They are typically a collection of data points or observations. Most datasets usually correspond to some form of structured data which involves a two dimensional data structure, such as a data matrix or data table (in R this is usually represented using a data frame) containing various values. An example is our german_credit_dataset.csv file from Chapter 5, Credit Risk Detection and Prediction – Descriptive Analytics.

  • Data observations: They are the rows in a dataset...