Data Mining for Business Analytics by Shmueli, Galit -- Read -- Imperial Library of Trantor

Index

Cover Title Page Copyright Dedication Foreword by Gareth James Foreword by Ravi Bapna Preface to the Python Edition Acknowledgments Part I Preliminaries

Chapter 1 Introduction

1.1 What Is Business Analytics? 1.2 What Is Data Mining? 1.3 Data Mining and Related Terms 1.4 Big Data 1.5 Data Science 1.6 Why Are There So Many Different Methods? 1.7 Terminology and Notation 1.8 Road Maps to This Book

Chapter 2 Overview of the Data Mining Process

2.1 Introduction 2.2 Core Ideas in Data Mining 2.3 The Steps in Data Mining 2.4 Preliminary Steps 2.5 Predictive Power and Overfitting 2.6 Building a Predictive Model 2.7 Using Python for Data Mining on a Local Machine 2.8 Automating Data Mining Solutions 2.9 Ethical Practice in Data Mining5 Problems Notes

Part II Data Exploration and Dimension Reduction

Chapter 3 Data Visualization

3.1 Introduction1 3.2 Data Examples 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 3.4 Multidimensional Visualization 3.5 Specialized Visualizations 3.6 Summary: Major Visualizations and Operations, by Data Mining Goal Problems Notes

Chapter 4 Dimension Reduction

4.1 Introduction 4.2 Curse of Dimensionality 4.3 Practical Considerations 4.4 Data Summaries 4.5 Correlation Analysis 4.6 Reducing the Number of Categories in Categorical Variables 4.7 Converting a Categorical Variable to a Numerical Variable 4.8 Principal Components Analysis 4.9 Dimension Reduction Using Regression Models 4.10 Dimension Reduction Using Classification and Regression Trees Problems Notes

Part III Performance Evaluation

Chapter 5 Evaluating Predictive Performance

5.1 Introduction 5.2 Evaluating Predictive Performance 5.3 Judging Classifier Performance 5.4 Judging Ranking Performance 5.5 Oversampling Problems Notes

Part IV Prediction and Classification Methods

Chapter 6 Multiple Linear Regression

6.1 Introduction 6.2 Explanatory vs. Predictive Modeling 6.3 Estimating the Regression Equation and Prediction 6.4 Variable Selection in Linear Regression Appendix: Using Statmodels Problems

Chapter 7 k-Nearest Neighbors (k-NN)

7.1 The k-NN Classifier (Categorical Outcome) 7.2 k-NN for a Numerical Outcome 7.3 Advantages and Shortcomings of k-NN Algorithms Problems Notes

Chapter 8 The Naive Bayes Classifier

8.1 Introduction 8.2 Applying the Full (Exact) Bayesian Classifier 8.3 Advantages and Shortcomings of the Naive Bayes Classifier Problems

Chapter 9 Classification and Regression Trees

9.1 Introduction 9.2 Classification Trees 9.3 Evaluating the Performance of a Classification Tree 9.4 Avoiding Overfitting 9.5 Classification Rules from Trees 9.6 Classification Trees for More Than Two Classes 9.7 Regression Trees 9.8 Improving Prediction: Random Forests and Boosted Trees 9.9 Advantages and Weaknesses of a Tree Problems Notes

Chapter 10 Logistic Regression

10.1 Introduction 10.2 The Logistic Regression Model 10.3 Example: Acceptance of Personal Loan 10.4 Evaluating Classification Performance 10.5 Logistic Regression for Multi-class Classification 10.6 Example of Complete Analysis: Predicting Delayed Flights Appendix: Using Statmodels Problems Notes

Chapter 11 Neural Nets

11.1 Introduction 11.2 Concept and Structure of a Neural Network 11.3 Fitting a Network to Data 11.4 Required User Input 11.5 Exploring the Relationship Between Predictors and Outcome 11.6 Deep Learning3 11.7 Advantages and Weaknesses of Neural Networks Problems Notes

Chapter 12 Discriminant Analysis

12.1 Introduction 12.2 Distance of a Record from a Class 12.3 Fisher’s Linear Classification Functions 12.4 Classification Performance of Discriminant Analysis 12.5 Prior Probabilities 12.6 Unequal Misclassification Costs 12.7 Classifying More Than Two Classes 12.8 Advantages and Weaknesses Problems Notes

Chapter 13 Combining Methods: Ensembles and Uplift Modeling

13.1 Ensembles1 13.2 Uplift (Persuasion) Modeling 13.3 Summary Problems Notes

Part V Mining Relationships Among Records

Chapter 14 Association Rules and Collaborative Filtering

14.1 Association Rules 14.2 Collaborative Filtering]Collaborative Filtering3 14.3 Summary Problems Notes

Chapter 15 Cluster Analysis

15.1 Introduction 15.2 Measuring Distance Between Two Records 15.3 Measuring Distance Between Two Clusters 15.4 Hierarchical (Agglomerative) Clustering 15.5 Non-Hierarchical Clustering: The k-Means Algorithm Problems

Part VI Forecasting Time Series

Chapter 16 Handling Time Series

16.1 Introduction1 16.2 Descriptive vs. Predictive Modeling 16.3 Popular Forecasting Methods in Business 16.4 Time Series Components 16.5 Data-Partitioning and Performance Evaluation Problems Notes

Chapter 17 Regression-Based Forecasting

17.1 A Model with Trend1 17.2 A Model with Seasonality 17.3 A Model with Trend and Seasonality 17.4 Autocorrelation and ARIMA Models Problems Notes

Chapter 18 Smoothing Methods

18.1 Introduction1 18.2 Moving Average 18.3 Simple Exponential Smoothing 18.4 Advanced Exponential Smoothing Problems Notes

PART VII Data Analytics

Chapter 19 Social Network Analytics1

19.1 Introduction2 19.2 Directed vs. Undirected Networks 19.3 Visualizing and Analyzing Networks 19.4 Social Data Metrics and Taxonomy 19.5 Using Network Metrics in Prediction and Classification 19.6 Collecting Social Network Data with Python 19.7 Advantages and Disadvantages Problems Notes

Chapter 20 Text Mining

20.1 Introduction1 20.2 The Tabular Representation of Text: Term-Document Matrix and “Bag-of-Words” 20.3 Bag-of-Words vs. Meaning Extraction at Document Level 20.4 Preprocessing the Text 20.5 Implementing Data Mining Methods 20.6 Example: Online Discussions on Autos and Electronics 20.7 Summary Problems Notes

PART VIII Cases

Chapter 21 Cases

21.1 Charles Book Club1 21.2 German Credit 21.3 Tayko Software Cataloger3 21.4 Political Persuasion4 21.5 Taxi Cancellations5 21.6 Segmenting Consumers of Bath Soap6 21.7 Direct-Mail Fundraising 21.8 Catalog Cross-Selling7 21.9 Time Series Case: Forecasting Public Transportation Demand Notes

References Data Files Used in the Book Python Utilities Functions Index End User License Agreement

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