book

Data Mining for Business Analytics

Name: Data Mining for Business Analytics
ISBN: 9781119549840

by Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel

November 2019

Beginner to intermediate

608 pages

16h 33m

English

Wiley

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Cover
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 Terms1.4 Big Data1.5 Data Science1.6 Why Are There So Many Different Methods?1.7 Terminology and Notation1.8 Road Maps to This Book
Chapter 2 Overview of the Data Mining Process
2.1 Introduction2.2 Core Ideas in Data Mining2.3 The Steps in Data Mining2.4 Preliminary Steps2.5 Predictive Power and Overfitting2.6 Building a Predictive Model2.7 Using Python for Data Mining on a Local Machine2.8 Automating Data Mining Solutions2.9 Ethical Practice in Data Mining5ProblemsNotes
Part II Data Exploration and Dimension Reduction
Chapter 3 Data Visualization
3.1 Introduction13.2 Data Examples3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots3.4 Multidimensional Visualization3.5 Specialized Visualizations3.6 Summary: Major Visualizations and Operations, by Data Mining GoalProblemsNotes

Chapter 4 Dimension Reduction
4.1 Introduction4.2 Curse of Dimensionality4.3 Practical Considerations4.4 Data Summaries4.5 Correlation Analysis4.6 Reducing the Number of Categories in Categorical Variables4.7 Converting a Categorical Variable to a Numerical Variable4.8 Principal Components Analysis4.9 Dimension Reduction Using Regression Models4.10 Dimension Reduction Using Classification and Regression TreesProblemsNotes
Part III Performance Evaluation
Chapter 5 Evaluating Predictive Performance
5.1 Introduction5.2 Evaluating Predictive Performance5.3 Judging Classifier Performance5.4 Judging Ranking Performance5.5 OversamplingProblemsNotes
Part IV Prediction and Classification Methods
Chapter 6 Multiple Linear Regression
6.1 Introduction6.2 Explanatory vs. Predictive Modeling6.3 Estimating the Regression Equation and Prediction6.4 Variable Selection in Linear RegressionAppendix: Using StatmodelsProblems
Chapter 7 k-Nearest Neighbors (k-NN)
7.1 The k-NN Classifier (Categorical Outcome)7.2 k-NN for a Numerical Outcome7.3 Advantages and Shortcomings of k-NN AlgorithmsProblemsNotes
Chapter 8 The Naive Bayes Classifier
8.1 Introduction8.2 Applying the Full (Exact) Bayesian Classifier8.3 Advantages and Shortcomings of the Naive Bayes ClassifierProblems
Chapter 9 Classification and Regression Trees
9.1 Introduction9.2 Classification Trees9.3 Evaluating the Performance of a Classification Tree9.4 Avoiding Overfitting9.5 Classification Rules from Trees9.6 Classification Trees for More Than Two Classes9.7 Regression Trees9.8 Improving Prediction: Random Forests and Boosted Trees9.9 Advantages and Weaknesses of a TreeProblemsNotes
Chapter 10 Logistic Regression
10.1 Introduction10.2 The Logistic Regression Model10.3 Example: Acceptance of Personal Loan10.4 Evaluating Classification Performance10.5 Logistic Regression for Multi-class Classification10.6 Example of Complete Analysis: Predicting Delayed FlightsAppendix: Using StatmodelsProblemsNotes
Chapter 11 Neural Nets
11.1 Introduction11.2 Concept and Structure of a Neural Network11.3 Fitting a Network to Data11.4 Required User Input11.5 Exploring the Relationship Between Predictors and Outcome11.6 Deep Learning311.7 Advantages and Weaknesses of Neural NetworksProblemsNotes
Chapter 12 Discriminant Analysis
12.1 Introduction12.2 Distance of a Record from a Class12.3 Fisher’s Linear Classification Functions12.4 Classification Performance of Discriminant Analysis12.5 Prior Probabilities12.6 Unequal Misclassification Costs12.7 Classifying More Than Two Classes12.8 Advantages and WeaknessesProblemsNotes
Chapter 13 Combining Methods: Ensembles and Uplift Modeling
13.1 Ensembles113.2 Uplift (Persuasion) Modeling13.3 SummaryProblemsNotes
Part V Mining Relationships Among Records
Chapter 14 Association Rules and Collaborative Filtering
14.1 Association Rules14.2 Collaborative Filtering14.3 SummaryProblemsNotes
Chapter 15 Cluster Analysis
15.1 Introduction15.2 Measuring Distance Between Two Records15.3 Measuring Distance Between Two Clusters15.4 Hierarchical (Agglomerative) Clustering15.5 Non-Hierarchical Clustering: The k-Means AlgorithmProblems
Part VI Forecasting Time Series
Chapter 16 Handling Time Series
16.1 Introduction116.2 Descriptive vs. Predictive Modeling16.3 Popular Forecasting Methods in Business16.4 Time Series Components16.5 Data-Partitioning and Performance EvaluationProblemsNotes
Chapter 17 Regression-Based Forecasting
17.1 A Model with Trend117.2 A Model with Seasonality17.3 A Model with Trend and Seasonality17.4 Autocorrelation and ARIMA ModelsProblemsNotes
Chapter 18 Smoothing Methods
18.1 Introduction118.2 Moving Average18.3 Simple Exponential Smoothing18.4 Advanced Exponential SmoothingProblemsNotes
PART VII Data Analytics
Chapter 19 Social Network Analytics1
19.1 Introduction219.2 Directed vs. Undirected Networks19.3 Visualizing and Analyzing Networks19.4 Social Data Metrics and Taxonomy19.5 Using Network Metrics in Prediction and Classification19.6 Collecting Social Network Data with Python19.7 Advantages and DisadvantagesProblemsNotes
Chapter 20 Text Mining
20.1 Introduction120.2 The Tabular Representation of Text: Term-Document Matrix and “Bag-of-Words”20.3 Bag-of-Words vs. Meaning Extraction at Document Level20.4 Preprocessing the Text20.5 Implementing Data Mining Methods20.6 Example: Online Discussions on Autos and Electronics20.7 SummaryProblemsNotes
PART VIII Cases
Chapter 21 Cases
21.1 Charles Book Club121.2 German Credit21.3 Tayko Software Cataloger321.4 Political Persuasion421.5 Taxi Cancellations521.6 Segmenting Consumers of Bath Soap621.7 Direct-Mail Fundraising21.8 Catalog Cross-Selling721.9 Time Series Case: Forecasting Public Transportation DemandNotes
References
Data Files Used in the Book
Python Utilities Functions
Index
End User License Agreement

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References

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Data Mining for Business Analytics

by Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel

References

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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