Data Mining for Business Analytics Concepts Techniques and Applications in Python 1st edition by Galit Shmueli, Peter Bruce, Peter Gedeck, Nitin Patel ISBN 1119549840 978-1119549840

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ISBN 10: 1119549840
ISBN 13: 978-1119549840
Author: Galit Shmueli, Peter Bruce, Peter Gedeck, Nitin Patel 

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration

Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.

This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:

• A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process
• A new section on ethical issues in data mining
• Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
• More than a dozen case studies demonstrating applications for the data mining techniques described
• End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
• A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

“This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.”

—Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R 

Data Mining for Business Analytics Concepts Techniques and Applications in Python 1st Table of contents:

Part I Preliminaries

Chapter 1 Introduction

• What is Business Analytics?

• What is Data Mining?

• Data Mining and Related Terms

• Big Data

• Data Science

• Why are There So Many Different Methods?

• Terminology and Notation

• Road Maps to This Book

Chapter 2 Overview of the Data Mining Process

• Introduction

• Core Ideas in Data Mining

• The Steps in Data Mining

• Preliminary Steps

• Predictive Power and Overfitting

• Building a Predictive Model

• Using Python for Data Mining on a Local Machine

• Automating Data Mining Solutions

• Ethical Practice in Data Mining

• Problems

---

Part II Data Exploration and Dimension Reduction

Chapter 3 Data Visualization

• Introduction

• Data Examples

• Basic Charts: Bar Charts, Line Graphs, and Scatter Plots

• Multidimensional Visualization

• Specialized Visualizations

• Summary: Major Visualizations and Operations, by Data Mining Goal

• Problems

Chapter 4 Dimension Reduction

• Introduction

• Curse of Dimensionality

• Practical Considerations

• Data Summaries

• Correlation Analysis

• Reducing the Number of Categories in Categorical Variables

• Converting a Categorical Variable to a Numerical Variable

• Principal Components Analysis

• Dimension Reduction Using Regression Models

• Dimension Reduction Using Classification and Regression Trees

• Problems

---

Part III Performance Evaluation

Chapter 5 Evaluating Predictive Performance

• Introduction

• Evaluating Predictive Performance

• Judging Classifier Performance

• Judging Ranking Performance

• Oversampling

• Problems

---

Part IV Prediction and Classification Methods

Chapter 6 Multiple Linear Regression

• Introduction

• Explanatory vs. Predictive Modeling

• Estimating the Regression Equation and Prediction

• Variable Selection in Linear Regression

• Appendix: Using Statmodels

• Problems

Chapter 7 k-Nearest Neighbors (kNN)

• The k-NN Classifier (Categorical Outcome)

• k-NN for a Numerical Outcome

• Advantages and Shortcomings of k-NN Algorithms

• Problems

Chapter 8 The Naive Bayes Classifier

• Introduction

• Example: Predicting Fraudulent Financial Reporting

• Applying the Full (Exact) Bayesian Classifier

• Advantages and Shortcomings of the Naive Bayes Classifier

• Problems

Chapter 9 Classification and Regression Trees

• Introduction

• Classification Trees

• Evaluating the Performance of a Classification Tree

• Avoiding Overfitting

• Classification Rules from Trees

• Classification Trees for More Than Two Classes

• Regression Trees

• Improving Prediction: Random Forests and Boosted Trees

• Advantages and Weaknesses of a Tree

• Problems

Chapter 10 Logistic Regression

• Introduction

• The Logistic Regression Model

• Example: Acceptance of Personal Loan

• Evaluating Classification Performance

• Logistic Regression for Multi-class Classification

• Example of Complete Analysis: Predicting Delayed Flights

• Appendix: Using Statmodels

• Problems

Chapter 11 Neural Nets

• Introduction

• Concept and Structure of a Neural Network

• Fitting a Network to Data

• Required User Input

• Exploring the Relationship Between Predictors and Outcome

• Deep Learning

• Advantages and Weaknesses of Neural Networks

• Problems

Chapter 12 Discriminant Analysis

• Introduction

• Distance of a Record from a Class

• Fisher’s Linear Classification Functions

• Classification Performance of Discriminant Analysis

• Prior Probabilities

• Unequal Misclassification Costs

• Classifying More Than Two Classes

• Advantages and Weaknesses

• Problems

Chapter 13 Combining Methods: Ensembles and Uplift Modeling

• Ensembles

• Uplift (Persuasion) Modeling

• Summary

• Problems

---

Part V Mining Relationships among Records

Chapter 14 Association Rules and Collaborative Filtering

• Association Rules

• Collaborative Filtering

• Summary

• Problems

Chapter 15 Cluster Analysis

• Introduction

• Measuring Distance Between Two Records

• Measuring Distance Between Two Clusters

• Hierarchical (Agglomerative) Clustering

• Non-Hierarchical Clustering: The k-Means Algorithm

• Problems

---

Part VI Forecasting Time Series

Chapter 16 Handling Time Series

• Introduction

• Descriptive vs. Predictive Modeling

• Popular Forecasting Methods in Business

• Time Series Components

• Data-Partitioning and Performance Evaluation

• Problems

Chapter 17 Regression-Based Forecasting

• A Model with Trend

• A Model with Seasonality

• A Model with Trend and Seasonality

• Autocorrelation and ARIMA Models

• Problems

Chapter 18 Smoothing Methods

• Introduction

• Moving Average

• Simple Exponential Smoothing

• Advanced Exponential Smoothing

• Problems

---

Part VII Data Analytics

Chapter 19 Social Network Analytics

• Introduction

• Directed vs. Undirected Networks

• Visualizing and Analyzing Networks

• Social Data Metrics and Taxonomy

• Using Network Metrics in Prediction and Classification

• Collecting Social Network Data with Python

• Advantages and Disadvantages

• Problems

Chapter 20 Text Mining

• Introduction

• The Tabular Representation of Text: Term-Document Matrix and “Bag-of-Words”

• Bag-of-Words vs. Meaning Extraction at Document Level

• Preprocessing the Text

• Implementing Data Mining Methods

• Example: Online Discussions on Autos and Electronics

• Summary

• Problems

---

Part VIII Cases

Chapter 21 Cases

• Charles Book Club

• German Credit

• Tayko Software Cataloger

• Political Persuasion

• Taxi Cancellations

• Segmenting Consumers of Bath Soap

• Direct-Mail Fundraising

• Catalog Cross-Selling

• Time Series Case: Forecasting Public Transportation Demand

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