This book is designed to cater to readers who prefer a hands-on guide using SPSS Modeler, a widely popular software that does not require coding or programming skills. It targets a broad audience, including students, lecturers, researchers, and industry professionals.
This book is designed to cater to readers who prefer a hands-on guide using SPSS Modeler, a widely popular software that does not require coding or programming skills. It targets a broad audience, including students, lecturers, researchers, and industry professionals.
Dr. Dothang Truong is the Associate Dean and Professor of Graduate Studies at Embry Riddle Aeronautical University, Daytona Beach, Florida. He has extensive teaching and research experience in machine learning, artificial intelligence, data analytics, air transportation management, and supply chain management. In 2022, Dr. Truong received the Frank Sorenson Award for the outstanding achievement of excellence in aviation research and scholarship.
Inhaltsangabe
PART I: INTRODUCTION TO DATA MINING. 1. Introduction to Data Mining and Data Science. 2. Data Mining Process, 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.
PART I: INTRODUCTION TO DATA MINING. 1. Introduction to Data Mining and Data Science. 2. Data Mining Process, 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.
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