Machine Learning with R Cookbook, 2nd Edition

Key Features

• Apply 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 Description

The 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 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
• 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

Table of Contents

Chapter 1. Practical Machine Learning With R
Chapter 2. Data Exploration With Air Quality Datasets
Chapter 3. Analyzing Time Series Data
Chapter 4. R And Statistics
Chapter 5. Understanding Regression Analysis
Chapter 6. Survival Analysis
Chapter 7. Classification 1 – Tree, Lazy, And Probabilistic
Chapter 8. Classification 2 – Neural Network And Svm
Chapter 9. Model Evaluation
Chapter 10. Ensemble Learning
Chapter 11. Clustering
Chapter 12. Association Analysis And Sequence Mining
Chapter 13. Dimension Reduction
Chapter 14. Big Data Analysis (R And Hadoop)