We will explore some of the different methods that can be performed to improve the accuracy of the model. Note that these are just techniques that can be tried; they can't guarantee improvement in the accuracy. Some of the methods might work for some kinds of data. Let's understand the popular options available to us.
R Data Science Essentials
R Data Science Essentials
Overview of this book
With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world.
R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards.
By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Table of Contents (15 chapters)
R Data Science Essentials
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Getting Started with R
Exploratory Data Analysis
Pattern Discovery
Segmentation Using Clustering
Developing Regression Models
Time Series Forecasting
Recommendation Engine
Communicating Data Analysis
Index
Customer Reviews