In this chapter, we understood the importance and usage of the transactional dataset, and Apriori rules, algorithms, and analysis that can be implemented on a transactional dataset with examples. The support, confidence, and lift parameters that define an output in an Apriori analysis have been covered. We have seen the filtering rules that can be generated and applied to a dataset. Moreover, we performed the plotting of a dataset in the form of graphs to provide a pictorial representation and understand the trends in a dataset more clearly. The sequential dataset that can be used to predict the occurrence of an event through a pattern from the historic data has been explained. Apriori sequence analysis techniques that look out for the statistically significant patterns in the sequence of the data have been explained. We have read and understood the dataset extracted and analyzed through the aforementioned analysis algorithms, and finally, an exercise to understand the sequence of...
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.
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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