Larose:

Data Mining and Predictive Analytics, 2nd Edition

Instructor Companion Site

Table Of Contents

Chapter 1: AN INTRODUCTION TO DATA MINING AND PREDICTIVE ANALYTICS

Chapter 2: DATA PREPROCESSING

Chapter 3: EXPLORATORY DATA ANALYSIS

Chapter 4: DIMENSION-REDUCTION METHODS

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

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

Chapter 19: HIERARCHICAL AND k-MEANS CLUSTERING

Chapter 20: KOHONEN NETWORKS

Chapter 21: BIRCH CLUSTERING

Chapter 22: MEASURING CLUSTER GOODNESS

Chapter 23: ASSOCIATION RULES

Chapter 24: SEGMENTATION MODELS

Chapter 25: ENSEMBLE METHODS: BAGGING AND BOOSTING

Chapter 26: MODEL VOTING AND PROPENSITY AVERAGING

Chapter 27: GENETIC ALGORITHMS

Chapter 28: IMPUTATION OF MISSING DATA

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

APPENDIX A: DATA SUMMARIZATION AND VISUALIZATION

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