Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R
by Galit Shmueli, Peter C. Bruce, Peter Gedeck, Inbal Yahav, Nitin R. Patel
Machine Learning for Business Analytics offers a detailed exploration of concepts, techniques, and applications in R. Authored by experts in data science and business, it equips readers with the tools to implement machine learning models for effective data analysis and decision-making in professional settings. Ideal for analysts seeking to advance their skills in predictive analytics.
About This Book
This book provides a thorough introduction to machine learning tailored for business analytics professionals. It covers foundational concepts that form the basis of predictive modeling and data analysis in a business context.
Readers will delve into a variety of techniques, from supervised learning to unsupervised methods, designed to extract insights from complex datasets. The focus remains on practical implementation to solve real-world business problems.
Applications are demonstrated through hands-on examples in R, a powerful open-source language for statistical computing. This approach ensures learners can immediately apply what they study to enhance business operations.
The text emphasizes the integration of machine learning into analytics workflows, helping organizations leverage data for competitive advantage. It serves as an accessible resource for both beginners and experienced analysts.
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