Key Features
Apply R to simplify predictive modeling with short and simple code
Use machine learning to solve problems ranging from small to big data
Build a training and testing dataset from the churn dataset, applying different classification methods
Book Description
The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics.

This book covers the basics of R by setting up a user-friendly programming environment and performing data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationships. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimension reduction.

What you will learn
Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm
Visualize patterns and associations using a range of graphs and find frequent itemsets using the Eclat algorithm
Compare differences between each regression method to discover how they solve problems
Predict possible churn users with the classification approach
Implement the clustering method to segment customer data
Compress images with the dimension reduction method
Incorporate R and Hadoop to solve machine learning problems on Big Data