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OverviewAs data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds. Full Product DetailsAuthor: Dothang TruongPublisher: Taylor & Francis Ltd Imprint: Chapman & Hall/CRC Weight: 1.090kg ISBN: 9780367751968ISBN 10: 0367751968 Pages: 577 Publication Date: 31 December 2025 Audience: College/higher education , Professional and scholarly , Professional and scholarly , Tertiary & Higher Education , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Not yet available This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release. Table of ContentsPart I: Introduction to Data Mining. 1. Introduction to Data Mining and Data Science. 2. Data Mining Processes, Methods, and Software. 3. Data Sampling and Partitioning. 4. Data Visualization and Exploration. 5. Data Modification. Part II: Data Mining Methods. 6. Model Evaluation. 7. Regression Methods. 8. Decision Trees. 9. Neural Networks. 10. Ensemble Modeling. 11. Presenting Results and Writing Data Mining Reports. 12. Principal Component Analysis. 13. Cluster Analysis. Part III: Advanced Data Mining Methods. 14. Random Forest. 15. Gradient Boosting. 16. Bayesian Networks.ReviewsAuthor InformationDothang Truong, PhD, is a Professor of Graduate Studies at Embry Riddle Aeronautical University, Daytona Beach, Florida. He has extensive teaching and research experience in machine learning, data analytics, air transportation management, and supply chain management. In 2022, Dr. Truong received the Frank Sorenson Award for outstanding achievement of excellence in aviation research and scholarship. Tab Content 6Author Website:Countries AvailableAll regions |
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