Machine Learning with R Cookbook - Second Edition by Yu-Wei, Chiu (David Chiu)

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, applying different classification methods.Book DescriptionBig data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. Youll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.What you will learnCreate and inspect transaction datasets and perform association analysis with the Apriori algorithmVisualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithmCompare differences between each regression method to discover how they solve problemsDetect and impute missing values in air quality dataPredict possible churn users with the classification approachPlot the autocorrelation function with time series analysisUse the Cox proportional hazards model for survival analysisImplement 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 AuthorAshishSingh Bhatia is a reader and learner at his core. He has more than 11 years of rich experience in different IT sectors, encompassing training, development, and management. He has worked in many domains, such as software development, ERP, banking, and training. He is passionate about Python and Java, and recently he has been exploring R. He is mostly involved in web and mobile developments in various capacity. He always likes to explore new technologies and share his views and thoughts through various online medium and magazines. He believes in sharing his experience with new generation and do take active part in training and teaching also.Yu-Wei, Chiu (David Chiu) is the founder of LargitData Company. He has previously worked for Trend Micro as a software engineer, with the responsibility of building up big data platforms for business intelligence and customer relationship management systems. In addition to being a startup entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques to data analysis. Yu-Wei is also a professional lecturer, and has delivered talks on Python, R, Hadoop, and tech talks at a variety of conferences.In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, a book compiled for Packt Publishing.Table of ContentsPractical Machine Learning with RData Exploration with Air Quality DatasetAnalysing Time Series DataR and StatisticsUnderstanding Regression AnalysisSurvival AnalysisClassification (I) – Tree, Lazy, and ProbabilisticClassification (II) – Neural Network and SVMModel EvaluationEnsemble LearningClusteringAssociation Analysis and Sequence MiningDimension ReductionBig Data Analysis (R and Hadoop)