Data Mining for Business Analytics: Concepts, Techniques and Applications in Python
- Length: 608 pages
- Edition: 1
- Language: English
- Publisher: Wiley
- Publication Date: 2019-11-05
- ISBN-10: 1119549841
- ISBN-13: 9781119549840
- Sales Rank: #194909 (See Top 100 Books)
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 Contents 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 Order of Topics CHAPTER 2 Overview of the Data Mining Process 2.1 Introduction 2.2 Core Ideas in Data Mining Classification Prediction Association Rules and Recommendation Systems Predictive Analytics Data Reduction and Dimension Reduction Data Exploration and Visualization Supervised and Unsupervised Learning 2.3 The Steps in Data Mining 2.4 Preliminary Steps Organization of Datasets Predicting Home Values in the West Roxbury Neighborhood Loading and Looking at the Data in Python Python Imports Sampling from a Database Oversampling Rare Events in Classification Tasks Preprocessing and Cleaning the Data 2.5 Predictive Power and Overfitting Overfitting Creation and Use of Data Partitions 2.6 Building a Predictive Model Modeling Process 2.7 Using Python for Data Mining on a Local Machine 2.8 Automating Data Mining Solutions 2.9 Ethical Practice in Data Mining Data Mining Software: The State of the Market (by Herb Edelstein) Problems PART II DATA EXPLORATION AND DIMENSION REDUCTION CHAPTER 3 Data Visualization 3.1 Introduction 3.2 Data Examples Example 1: Boston Housing Data Example 2: Ridership on Amtrak Trains 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots Distribution Plots: Boxplots and Histograms Heatmaps: Visualizing Correlations and Missing Values 3.4 Multidimensional Visualization Adding Variables: Color, Size, Shape, Multiple Panels, and Animation Manipulations: Rescaling, Aggregation and Hierarchies, Zooming, Filtering Reference: Trend Lines and Labels Scaling Up to Large Datasets Multivariate Plot: Parallel Coordinates Plot Interactive Visualization 3.5 Specialized Visualizations Visualizing Networked Data Visualizing Hierarchical Data: Treemaps Visualizing Geographical Data: Map Charts 3.6 Summary: Major Visualizations and Operations, by Data Mining Goal Prediction Classification Time Series Forecasting Unsupervised Learning Problems CHAPTER 4 Dimension Reduction 4.1 Introduction 4.2 Curse of Dimensionality 4.3 Practical Considerations Example 1: House Prices in Boston 4.4 Data Summaries Summary Statistics Aggregation and Pivot Tables 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 Example 2: Breakfast Cereals Principal Components Normalizing the Data Using Principal Components for Classification and Prediction 4.9 Dimension Reduction Using Regression Models 4.10 Dimension Reduction Using Classification and Regression Trees Problems PART III PERFORMANCE EVALUATION CHAPTER 5 Evaluating Predictive Performance 5.1 Introduction 5.2 Evaluating Predictive Performance Naive Benchmark: The Average Prediction Accuracy Measures Comparing Training and Validation Performance Cumulative Gains and Lift Charts 5.3 Judging Classifier Performance Benchmark: The Naive Rule Class Separation The Confusion (Classification) Matrix Using the Validation Data Accuracy Measures Propensities and Cutoff for Classification Performance in Case of Unequal Importance of Classes Asymmetric Misclassification Costs Generalization to More Than Two Classes 5.4 Judging Ranking Performance Gains and Lift Charts for Binary Data Decile Lift Charts Beyond Two Classes Gains and Lift Charts Incorporating Costs and Benefits Cumulative Gains as a Function of Cutoff 5.5 Oversampling Oversampling the Training Set Evaluating Model Performance Using a Non-oversampled Validation Set Evaluating Model Performance if Only Oversampled Validation Set Exists Problems 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 Example: Predicting the Price of Used Toyota Corolla Cars 6.4 Variable Selection in Linear Regression Reducing the Number of Predictors How to Reduce the Number of Predictors Regularization (Shrinkage Models) Appendix: Using Statmodels Problems CHAPTER 7 k-Nearest Neighbors (kNN) 7.1 The k-NN Classifier (Categorical Outcome) Determining Neighbors Classification Rule Example: Riding Mowers Choosing k Setting the Cutoff Value k-NN with More Than Two Classes Converting Categorical Variables to Binary Dummies 7.2 k-NN for a Numerical Outcome 7.3 Advantages and Shortcomings of k-NN Algorithms Problems CHAPTER 8 The Naive Bayes Classifier 8.1 Introduction Cutoff Probability Method Conditional Probability Example 1: Predicting Fraudulent Financial Reporting 8.2 Applying the Full (Exact) Bayesian Classifier Using the “Assign to the Most Probable Class” Method Using the Cutoff Probability Method Practical Difficulty with the Complete (Exact) Bayes Procedure Solution: Naive Bayes The Naive Bayes Assumption of Conditional Independence Using the Cutoff Probability Method Example 2: Predicting Fraudulent Financial Reports, Two Predictors Example 3: Predicting Delayed Flights 8.3 Advantages and Shortcomings of the Naive Bayes Classifier Problems CHAPTER 9 Classification and Regression Trees 9.1 Introduction Tree Structure Decision Rules Classifying a New Record 9.2 Classification Trees Recursive Partitioning Example 1: Riding Mowers Measures of Impurity 9.3 Evaluating the Performance of a Classification Tree Example 2: Acceptance of Personal Loan Sensitivity Analysis Using Cross Validation 9.4 Avoiding Overfitting Stopping Tree Growth Fine-tuning Tree Parameters Other Methods for Limiting Tree Size 9.5 Classification Rules from Trees 9.6 Classification Trees for More Than Two Classes 9.7 Regression Trees Prediction Measuring Impurity Evaluating Performance 9.8 Improving Prediction: Random Forests and Boosted Trees Random Forests Boosted Trees 9.9 Advantages and Weaknesses of a Tree Problems CHAPTER 10 Logistic Regression 10.1 Introduction 10.2 The Logistic Regression Model 10.3 Example: Acceptance of Personal Loan Model with a Single Predictor Estimating the Logistic Model from Data: Computing Parameter Estimates Interpreting Results in Terms of Odds (for a Profiling Goal) 10.4 Evaluating Classification Performance Variable Selection 10.5 Logistic Regression for Multi-class Classification Ordinal Classes Nominal Classes Comparing Ordinal and Nominal Models 10.6 Example of Complete Analysis: Predicting Delayed Flights Data Preprocessing Model Training Model Interpretation Model Performance Variable Selection Appendix: Using Statmodels Problems CHAPTER 11 Neural Nets 11.1 Introduction 11.2 Concept and Structure of a Neural Network 11.3 Fitting a Network to Data Example 1: Tiny Dataset Computing Output of Nodes Preprocessing the Data Training the Model Example 2: Classifying Accident Severity Avoiding Overfitting Using the Output for Prediction and Classification 11.4 Required User Input 11.5 Exploring the Relationship Between Predictors and Outcome 11.6 Deep Learning Convolutional Neural Networks (CNNs) Local Feature Map A Hierarchy of Features The Learning Process Unsupervised Learning Conclusion 11.7 Advantages and Weaknesses of Neural Networks Problems CHAPTER 12 Discriminant Analysis 12.1 Introduction Example 1: Riding Mowers Example 2: Personal Loan Acceptance 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 Example 3: Medical Dispatch to Accident Scenes 12.8 Advantages and Weaknesses Problems CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling 13.1 Ensembles Why Ensembles Can Improve Predictive Power Simple Averaging Bagging Boosting Bagging and Boosting in Python Advantages and Weaknesses of Ensembles 13.2 Uplift (Persuasion) Modeling A–B Testing Uplift Gathering the Data A Simple Model Modeling Individual Uplift Computing Uplift with Python Using the Results of an Uplift Model 13.3 Summary Problems PART V MINING RELATIONSHIPS AMONG RECORDS CHAPTER 14 Association Rules and Collaborative Filtering 14.1 Association Rules Discovering Association Rules in Transaction Databases Example 1: Synthetic Data on Purchases of Phone Faceplates Generating Candidate Rules The Apriori Algorithm Selecting Strong Rules Data Format The Process of Rule Selection Interpreting the Results Rules and Chance Example 2: Rules for Similar Book Purchases 14.2 Collaborative Filtering Data Type and Format Example 3: Netflix Prize Contest User-Based Collaborative Filtering: “People Like You” Item-Based Collaborative Filtering Advantages and Weaknesses of Collaborative Filtering Collaborative Filtering vs. Association Rules 14.3 Summary Problems CHAPTER 15 Cluster Analysis 15.1 Introduction Example: Public Utilities 15.2 Measuring Distance Between Two Records Euclidean Distance Normalizing Numerical Measurements Other Distance Measures for Numerical Data Distance Measures for Categorical Data Distance Measures for Mixed Data 15.3 Measuring Distance Between Two Clusters Minimum Distance Maximum Distance Average Distance Centroid Distance 15.4 Hierarchical (Agglomerative) Clustering Single Linkage Complete Linkage Average Linkage Centroid Linkage Ward’s Method Dendrograms: Displaying Clustering Process and Results Validating Clusters Limitations of Hierarchical Clustering 15.5 Non-Hierarchical Clustering: The k-Means Algorithm Choosing the Number of Clusters (k) Problems PART VI FORECASTING TIME SERIES CHAPTER 16 Handling Time Series 16.1 Introduction 16.2 Descriptive vs. Predictive Modeling 16.3 Popular Forecasting Methods in Business Combining Methods 16.4 Time Series Components Example: Ridership on Amtrak Trains 16.5 Data-Partitioning and Performance Evaluation Benchmark Performance: Naive Forecasts Generating Future Forecasts Problems CHAPTER 17 Regression-Based Forecasting 17.1 A Model with Trend Linear Trend Exponential Trend Polynomial Trend 17.2 A Model with Seasonality 17.3 A Model with Trend and Seasonality 17.4 Autocorrelation and ARIMA Models Computing Autocorrelation Improving Forecasts by Integrating Autocorrelation Information Evaluating Predictability Problems CHAPTER 18 Smoothing Methods 18.1 Introduction 18.2 Moving Average Centered Moving Average for Visualization Trailing Moving Average for Forecasting Choosing Window Width (w) 18.3 Simple Exponential Smoothing Choosing Smoothing Parameter Relation Between Moving Average and Simple Exponential Smoothing 18.4 Advanced Exponential Smoothing Series with a Trend Series with a Trend and Seasonality Series with Seasonality (No Trend) Problems PART VII DATA ANALYTICS CHAPTER 19 Social Network Analytics 19.1 Introduction 19.2 Directed vs. Undirected Networks 19.3 Visualizing and Analyzing Networks Plot Layout Edge List Adjacency Matrix Using Network Data in Classification and Prediction 19.4 Social Data Metrics and Taxonomy Node-Level Centrality Metrics Egocentric Network Network Metrics 19.5 Using Network Metrics in Prediction and Classification Link Prediction Entity Resolution Collaborative Filtering 19.6 Collecting Social Network Data with Python 19.7 Advantages and Disadvantages Problems 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 Tokenization Text Reduction Presence/Absence vs. Frequency Term Frequency–Inverse Document Frequency (TF-IDF) From Terms to Concepts: Latent Semantic Indexing Extracting Meaning 20.5 Implementing Data Mining Methods 20.6 Example: Online Discussions on Autos and Electronics Importing and Labeling the Records Text Preprocessing in Python Producing a Concept Matrix Fitting a Predictive Model Prediction 20.7 Summary Problems PART VIII CASES CHAPTER 21 Cases 21.1 Charles Book Club The Book Industry Database Marketing at Charles Data Mining Techniques Assignment 21.2 German Credit Background Data Assignment 21.3 Tayko Software Cataloger Background The Mailing Experiment Data Assignment 21.4 Political Persuasion Background Predictive Analytics Arrives in US Politics Political Targeting Uplift Data Assignment 21.5 Taxi Cancellations Business Situation Assignment 21.6 Segmenting Consumers of Bath Soap Business Situation Key Problems Data Measuring Brand Loyalty Assignment 21.7 Direct-Mail Fundraising Background Data Assignment 21.8 Catalog Cross-Selling Background Assignment 21.9 Time Series Case: Forecasting Public Transportation Demand Background Problem Description Available Data Assignment Goal Assignment Tips and Suggested Steps References Data Files Used in the Book Python Utilities Functions Index EULA
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