| Partial contents |
Part I. Data Preparation -- Chapter 1. An Introduction to Data Mining and Predictive Analytics -- Chapter 2. Data Preprocessing -- Chapter 3. Exploratory Data Analysis -- Chapter 4. Dimension-Reduction Methods -- Part II Statistical Analysis -- Chapter 5 Univariate Statistical Analysis -- Chapter 6. Multivariate Statistics -- Chapter 7. Preparing to Model the data -- Chapter 8. Simple Linear Regression -- Chapter 9. Multiple Regression and Model Building -- Part III. Classification -- Chapter 10. k-Nearest Neighbor Algorithm -- Chapter 11. Decision trees -- Chapter 12. Neural Networks -- Chapter 13. Logistic Regression -- Chapter 14. Naïve Bayes and Bayesian Networks -- Chapter 15. Model Evaluation Techniques -- Chapter 16. Cost-Benefit Analysis Using Data-Driven Costs -- Chapter 17. Cost-Benefit Analysis For Trinary and k-Nary Classification Models -- Chapter 18. Graphical Evaluation of Classification Models -- Part IV. Clustering -- Chapter 19. Hierarchical and k-Means Clustering -- Chapter 20. Kohonen Networks --Chapter 21. Birch Clustering-- Chapter 22. Measuring Cluster Goodness -- Part V. Association Rules -- Chapter 23. Association Rules -- Part VI. Enhancing Model Performance -- Chapter 24. Segmentation Models -- Chapter 25. Ensemble Methods: Bagging and Boosting -- Chapter 26. Model Voting and Propensity Averaging -- Part VII. Further Topics -- Chapter 27. Genetic Algorithms -- Chapter 28. Imputation of Missing Data -- Part VIII. Case Study: Predicting Response to Direct-Mail Marketing --Chapter 29. Case Study, Part 1: Business Understanding, Data Preparation, and Eda -- Chapter 30. Case Study, Part 2: Clustering and Principal Components Analysis -- Chapter 31. Case Study, Part 3: Modeling And Evaluation For Performance And Interpretability -- Chapter 32. Case Study, Part 4: Modeling And Evaluation For High Performance Only. |
