Data Mining for Business Analytics, An Indian Adaptation: Concepts, Techniques, and Applications in R

O P Wali, Shmueli, Bruce, Yahav, Patel, Lichtendahl Jr.,O P Wali

ISBN: 9789390421701

584 pages

INR 929


Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is a comprehensive resource for learners pursuing  graduate and undergraduate level courses in data mining, business analytics, and related courses within domain of AI. This is an excellent reference for analysts, researchers, and practitioners working in various domains of business like finance, marketing, human resource, operations, information services, consultancy etc., who want to tap more opportunities for insights  in variety of data driven decision-making scenarios.



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

Order of Topics


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 R for Data Mining on a Local Machine

2.8 Automating Data Mining Solutions




CHAPTER 3 Data Visualization

3.1 Uses of Data Visualization

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


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




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




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


CHAPTER 7 k-Nearest Neighbors (kNN)

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


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


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


CHAPTER 10 Logistic Regression

10.1 Introduction

10.2 The Logistic Regression Model

10.3 Example: Rating Behavior Towards Online Quick Food Service Providers In Indian Cities

10.4 Evaluating Classification Performance

10.5 Example of Complete Analysis: Predicting Delayed Flights

10.6 Appendix: Logistic Regression for Profiling


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 Advantages and Weaknesses of Neural Networks


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


CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling

13.1 Ensembles

13.2 Uplift (Persuasion) Modeling

13.3 Summary



CHAPTER 14 Association Rules and Collaborative Filtering

14.1 Association Rules

14.2 Collaborative Filtering

14.3 Summary


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



CHAPTER 16 Handling Time Series

16.1 Introduction

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


CHAPTER 17 Regression-Based Forecasting

17.1 A Model with Trend

17.2 A Model with Seasonality

17.3 A Model with Trend and Seasonality

17.4 Autocorrelation and ARIMA Models


CHAPTER 18 Smoothing Methods

18.1 Introduction

18.2 Moving Average

18.3 Simple Exponential Smoothing

18.4 Advanced Exponential Smoothing



CHAPTER 19 Social Network Analytics

19.1 Introduction

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 R

19.7 Advantages and Disadvantages


CHAPTER 20 Text Mining

20.1 Introduction

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



CHAPTER 21 Cases

21.1 Charles Book Club

21.2 German Credit

21.3 Tayko Software Cataloger

21.4 Political Persuasion

21.5 Taxi Cancellations

21.6 Segmenting Consumers of Bath Soap

21.7 Direct-Mail Fundraising

21.8 Predicting Tourist Travel Packages

21.9 Predicting Bankruptcy

21.10 Time Series Case: Forecasting Public Transportation Demand

21.11 Predicting Attrition

21.12 Attitude Towards Therapy Suggestions In Covid-19 Times



Data Files Used in the Book



“I have gone through the book and I must share my good thoughts about it. The contents of the book are quite comprehensive covering various approaches including the contemporary ones such as Neural nets, Text Mining among others. It comprises various examples, cases and offers hand-on experience of the tools such as R. The book is really good for the analytics domain.

Dr. Parul Singh
Consultant at the level of Assistant Professor, IIFT New Delhi

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