Bhatia A., Yu-Wei Chiu (Chiu David). Machine Learning With R Cookbook

2nd ed. — Packt Publishing, 2017. — 588 p. — ISBN: 978-1787284395.Key FeaturesApply R to simplify predictive modelling with short and simple code
Use machine learning to solve problems ranging from small to big data
Build a training and testing dataset, applying different classification methods.Book DescriptionThe R language is a powerful open source functional programming language. At its core, R is a statistical 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 programming ETL in R. Data exploration examples are provided that demonstrate how powerful data visualisation and machine learning is in discovering hidden relationships. You will also explore air quality data, steps to fix the missing values and visualising the same. You will then dive into important machine learning topics, including data classification, regression, survival analysis, time series analysis, clustering association rule mining, and dimension reduction.This book will include the latest code and examples based on R 3.3 and above-updated for better computation, accuracy, and speed with R.What you will learnCreate 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
Detect and impute missing values in air quality data
Predict possible churn users with the classification approach
Plot autocorrelation function with time series analysis
Use Cox proportional hazards model for survival analysis
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