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Machine Learning with R Cookbook, Second Edition, is your hands-on guide to applying machine learning principles using R. Through simple, actionable examples and detailed step-by-step recipes, this book will help you build predictive models, analyze data, and derive actionable insights. Explore core topics in data science, including regression, classification, clustering, and more. What this Book will help me do Apply the Apriori algorithm for association analysis to uncover relationships in transaction datasets. Effectively visualize data patterns and associations using a variety of plots and graphing methods. Master the application of regression techniques to address predictive modeling challenges. Leverage the power of R and Hadoop for performing big data machine learning efficiently. Conduct advanced analyses such as survival analysis and improve machine learning model performance. Author(s) Yu-Wei, Chiu (David Chiu), the author, is an experienced data scientist and R programmer who specializes in applying data science and machine learning principles to solve real-world problems. David's pragmatic and comprehensive teaching style provides readers with deep insights and practical methodologies for using R effectively in their projects. His passion for data science and expertise in R and big data make this book a reliable resource for learners. Who is it for? This book is ideal for data scientists, analysts, and professionals working with machine learning and R. It caters to intermediate users who are versed in the basics of R and want to deepen their skills. If you aim to become the go-to expert for machine learning challenges and enhance your efficiency and capability in machine learning projects, this book is for you.