Key FeaturesApply R to simplify predictive modeling with short and simple codeUse machine learning to solve problems ranging from small to big dataBuild a training and testing dataset from the churn dataset, applying different classification methodsBook DescriptionThe 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 learnCreate and inspect the transaction dataset, performing association analysis with the Apriori algorithmVisualize patterns and associations using a range of graphs and find frequent itemsets using the Eclat algorithmCompare differences between each regression method to discover how they solve problemsPredict possible churn users with the classification approachImplement the clustering method to segment customer dataCompress images with the dimension reduction methodIncorporate R and Hadoop to solve machine learning problems on Big DataAbout the AuthorYu-Wei, Chiu (David Chiu) is the founder of LargitData. He has previously worked for Trend Micro as a software engineer, with the responsibility of building big data platforms for business intelligence and customer relationship management systems. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis.Table of ContentsPractical Machine Learning with RData Exploration with RMS TitanicR and StatisticsUnderstanding Regression AnalysisClassification (I) – Tree, Lazy, and ProbabilisticClassification (II) – Neural Network and SVMModel EvaluationEnsemble LearningClusteringAssociation Analysis and Sequence MininDimension ReductionBig Data Analysis (R and Hadoop)
