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(Massachusetts Institute of Technology), (Collaborative Drug Discovery), (Cytel Inc., Cambridge, MA; Massachusetts Institute of Technology; Harvard University), (University of Maryland, College Park)
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  • Ilmumisaeg: 25-Nov-2019
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119549841
  • ISBN-13: 9781119549840
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Data Mining for Business Analytics: Concepts, Techniques and Applications in PythonSuurem pilt
  • Formaat: Hardback, 608 pages, kõrgus x laius x paksus: 257x183x31 mm, kaal: 1111 g
  • Ilmumisaeg: 25-Nov-2019
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119549841
  • ISBN-13: 9781119549840
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119549840.html
Märksõnad:

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 

Foreword xix
Gareth James
Foreword xxi
Ravi Bapna
Preface to the Python Edition xxiii
Acknowledgments xxvii
Part I Preliminaries
Chapter 1 Introduction
3(12)
1.1 What Is Business Analytics'
3(2)
1.2 What Is Data Mining?
5(1)
1.3 Data Mining and Related Terms
5(1)
1.4 Big Data
6(1)
1.5 Data Science
7(1)
1.6 Why Are There So Many Different Methods?
8(1)
1.7 Terminology and Notation
9(2)
1.8 Road Maps to This Book
11(4)
Order of Topics
11(4)
Chapter 2 Overview of the Data Mining Process
15(46)
2.1 Introduction
15(1)
2.2 Core Ideas in Data Mining
16(3)
Classification
16(1)
Prediction
16(1)
Association Rules and Recommendation Systems
16(1)
Predictive Analytics
17(1)
Data Reduction and Dimension Reduction
17(1)
Data Exploration and Visualization
17(1)
Supervised and Unsupervised Learning
18(1)
2.3 The Steps in Data Mining
19(2)
2.4 Preliminary Steps
21(13)
Organization of Datasets
21(1)
Predicting Home Values in the West Roxbury Neighborhood
21(1)
Loading and Looking at the Data in Python
22(3)
Python Imports
25(1)
Sampling from a Database
25(1)
Oversampling Rare Events in Classification Tasks
26(1)
Preprocessing and Cleaning the Data
27(7)
2.5 Predictive Power and Overfitting
34(6)
Overfitting
34(2)
Creation and Use of Data Partitions
36(4)
2.6 Building a Predictive Model
40(4)
Modeling Process
40(4)
2.7 Using Python for Data Mining on a Local Machine
44(1)
2.8 Automating Data Mining Solutions
45(2)
2.9 Ethical Practice in Data Mining
47(9)
Data Mining Software: The State of the Market (by Herb Edelstein)
52(4)
Problems
56(5)
Part II Data Exploration And Dimension Reduction
Chapter 3 Data Visualization
61(38)
3.1 Introduction
61(3)
3.2 Data Examples
64(1)
Example 1: Boston Housing Data
64(1)
Example 2: Ridership on Amtrak Trains
65(1)
3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots
65(9)
Distribution Plots: Boxplots and Histograms
68(3)
Heatmaps: Visualizing Correlations and Missing Values
71(3)
3.4 Multidimensional Visualization
74(14)
Adding Variables: Color, Size, Shape, Multiple Panels, and Animation
74(3)
Manipulations: Rescaling, Aggregation and Hierarchies, Zooming, Filtering
77(4)
Reference: Trend Lines and Labels
81(1)
Scaling Up to Large Datasets
82(1)
Multivariate Plot: Parallel Coordinates Plot
83(1)
Interactive Visualization
83(5)
3.5 Specialized Visualizations
88(5)
Visualizing Networked Data
88(2)
Visualizing Hierarchical Data: Treemaps
90(1)
Visualizing Geographical Data: Map Charts
91(2)
3.6 Summary: Major Visualizations and Operations, by Data Mining Goal
93(4)
Prediction
93(1)
Classification
94(2)
Time Series Forecasting
96(1)
Unsupervised Learning
96(1)
Problems
97(2)
Chapter 4 Dimension Reduction
99(26)
4.1 Introduction
100(1)
4.2 Curse of Dimensionality
100(1)
4.3 Practical Considerations
100(2)
Example 1: House Prices in Boston
101(1)
4.4 Data Summaries
102(3)
Summary Statistics
102(2)
Aggregation and Pivot Tables
104(1)
4.5 Correlation Analysis
105(1)
4.6 Reducing the Number of Categories in Categorical Variables
106(2)
4.7 Converting a Categorical Variable to a Numerical Variable
108(1)
4.8 Principal Components Analysis
108(11)
Example 2: Breakfast Cereals
109(5)
Principal Components
114(1)
Normalizing the Data
114(3)
Using Principal Components for Classification and Prediction
117(2)
4.9 Dimension Reduction Using Regression Models
119(1)
4.10 Dimension Reduction Using Classification and Regression Trees
119(1)
Problems
120(5)
Part III Performance Evaluation
Chapter 5 Evaluating Predictive Performance
125(36)
5.1 Introduction
126(1)
5.2 Evaluating Predictive Performance
126(5)
Naive Benchmark: The Average
127(1)
Prediction Accuracy Measures
127(1)
Comparing Training and Validation Performance
128(1)
Cumulative Gains and Lift Charts
128(3)
5.3 Judging Classifier Performance
131(13)
Benchmark: The Naive Rule
132(1)
Class Separation
133(1)
The Confusion (Classification) Matrix
133(1)
Using the Validation Data
134(1)
Accuracy Measures
135(1)
Propensities and Cutoff for Classification
136(2)
Performance in Case of Unequal Importance of Classes
138(2)
Asymmetric Misclassification Costs
140(4)
Generalization to More Than Two Classes
144(1)
5.4 Judging Ranking Performance
144(5)
Gains and Lift Charts for Binary Data
144(3)
Decile Lift Charts
147(1)
Beyond Two Classes
148(1)
Gains and Lift Charts Incorporating Costs and Benefits
148(1)
Cumulative Gains as a Function of Cutoff
148(1)
5.5 Oversampling
149(6)
Oversampling the Training Set
152(1)
Evaluating Model Performance Using a Non-oversampled Validation Set
152(1)
Evaluating Model Performance if Only Oversampled Validation Set Exists
152(3)
Problems
155(6)
Part IV Prediction And Classification Methods
Chapter 6 Multiple Linear Regression
161(24)
6.1 Introduction
162(1)
6.2 Explanatory vs. Predictive Modeling
162(2)
6.3 Estimating the Regression Equation and Prediction
164(5)
Example: Predicting the Price of Used Toyota Corolla Cars
165(4)
6.4 Variable Selection in Linear Regression
169(11)
Reducing the Number of Predictors
169(1)
How to Reduce the Number of Predictors
170(6)
Regularization (Shrinkage Models)
176(3)
Appendix: Using Statmodels
179(1)
Problems
180(5)
Chapter 7 k-Nearest Neighbors (kNN)
185(14)
7.1 The k-NN Classifier (Categorical Outcome)
185(8)
Determining Neighbors
186(1)
Classification Rule
186(1)
Example: Riding Mowers
187(1)
Choosing k
188(3)
Setting the Cutoff Value
191(1)
k-NN with More Than Two Classes
192(1)
Converting Categorical Variables to Binary Dummies
193(1)
7.2 k-NN for a Numerical Outcome
193(2)
7.3 Advantages and Shortcomings of k-NN Algorithms
195(2)
Problems
197(2)
Chapter 8 The Naive Bayes Classifier
199(18)
8.1 Introduction
199(2)
Cutoff Probability Method
200(1)
Conditional Probability
200(1)
Example 1: Predicting Fraudulent Financial Reporting
201(1)
8.2 Applying the Full (Exact) Bayesian Classifier
201(9)
Using the "Assign to the Most Probable Class" Method
202(1)
Using the Cutoff Probability Method
202(1)
Practical Difficulty with the Complete (Exact) Bayes Procedure
202(1)
Solution: Naive Bayes
203(1)
The Naive Bayes Assumption of Conditional Independence
204(1)
Using the Cutoff Probability Method
204(1)
Example 2: Predicting Fraudulent Financial Reports, Two Predictors
205(1)
Example 3: Predicting Delayed Flights
206(4)
8.3 Advantages and Shortcomings of the Naive Bayes Classifier
210(4)
Problems
214(3)
Chapter 9 Classification and Regression Trees
217(34)
9.1 Introduction
218(2)
Tree Structure
219(1)
Decision Rules
219(1)
Classifying a New Record
220(1)
9.2 Classification Trees
220(8)
Recursive Partitioning
220(1)
Example 1: Riding Mowers
221(2)
Measures of Impurity
223(5)
9.3 Evaluating the Performance of a Classification Tree
228(4)
Example 2: Acceptance of Personal Loan
228(2)
Sensitivity Analysis Using Cross Validation
230(2)
9.4 Avoiding Overfitting
232(6)
Stopping Tree Growth
233(1)
Fine-tuning Tree Parameters
234(2)
Other Methods for Limiting Tree Size
236(2)
9.5 Classification Rules from Trees
238(1)
9.6 Classification Trees for More Than Two Classes
239(1)
9.7 Regression Trees
239(4)
Prediction
240(1)
Measuring Impurity
240(1)
Evaluating Performance
241(2)
9.8 Improving Prediction: Random Forests and Boosted Trees
243(3)
Random Forests
243(1)
Boosted Trees
244(2)
9.9 Advantages and Weaknesses of a Tree
246(2)
Problems
248(3)
Chapter 10 Logistic Regression
251(32)
10.1 Introduction
252(1)
10.2 The Logistic Regression Model
253(2)
10.3 Example: Acceptance of Personal Loan
255(6)
Model with a Single Predictor
255(2)
Estimating the Logistic Model. from Data: Computing Parameter Estimates
257(2)
Interpreting Results in Terms of Odds (for a Profiling Goal)
259(2)
10.4 Evaluating Classification Performance
261(3)
Variable Selection
262(2)
10.5 Logistic Regression for Multi-class Classification
264(5)
Ordinal Classes
264(2)
Nominal Classes
266(1)
Comparing Ordinal and Nominal Models
267(2)
10.6 Example of Complete Analysis: Predicting Delayed Flights
269(11)
Data Preprocessing
270(2)
Model Training
272(1)
Model Interpretation
273(1)
Model Performance
273(3)
Variable Selection
276(2)
Appendix: Using Statmodels
278(2)
Problems
280(3)
Chapter 11 Neural Nets
283(26)
11.1 Introduction
284(1)
11.2 Concept and Structure of a Neural Network
284(1)
11.3 Fitting a Network to Data
285(12)
Example 1: Tiny Dataset
285(1)
Computing Output of Nodes
286(3)
Preprocessing the Data
289(1)
Training the Model
290(2)
Example 2: Classifying Accident Severity
292(3)
Avoiding Overfitting
295(2)
Using the Output for Prediction and Classification
297(1)
11.4 Required User Input
297(2)
11.5 Exploring the Relationship Between Predictors and Outcome
299(1)
11.6 Deep Learning
299(6)
Convolutional Neural Networks (CNNs)
300(1)
Local Feature Map
301(1)
A Hierarchy of Features
302(1)
The Learning Process
302(1)
Unsupervised Learning
303(1)
Conclusion
304(1)
11.7 Advantages and Weaknesses of Neural Networks
305(1)
Problems
306(3)
Chapter 12 Discriminant Analysis
309(18)
12.1 Introduction
310(1)
Example 1: Riding Mowers
310(1)
Example 2: Personal Loan Acceptance
310(1)
12.2 Distance of a Record from a Class
311(3)
12.3 Fisher's Linear Classification Functions
314(3)
12.4 Classification Performance of Discriminant Analysis
317(1)
12.5 Prior Probabilities
318(1)
12.6 Unequal Misclassification Costs
319(1)
12.7 Classifying More Than Two Classes
319(3)
Example 3: Medical Dispatch to Accident Scenes
319(3)
12.8 Advantages and Weaknesses
322(2)
Problems
324(3)
Chapter 13 Combining Methods: Ensembles and Uplift Modeling
327(18)
13.1 Ensembles
328(6)
Why Ensembles Can Improve Predictive Power
329(1)
Simple Averaging
330(1)
Bagging
331(1)
Boosting
331(1)
Bagging and Boosting in Python
332(1)
Advantages and Weaknesses of Ensembles
332(2)
13.2 Uplift (Persuasion) Modeling
334(6)
A-B Testing
334(1)
Uplift
334(1)
Gathering the Data
335(1)
A Simple Model
336(1)
Modeling Individual Uplift
337(1)
Computing Uplift with Python
338(1)
Using the Results of an Uplift Model
339(1)
13.3 Summary
340(1)
Problems
341(4)
Part V Mining Relationships Among Records
Chapter 14 Association Rules and Collaborative Filtering
345(30)
14.1 Association Rules
346(11)
Discovering Association Rules in Transaction Databases
346(2)
Example 1: Synthetic Data on Purchases of Phone Faceplates
348(1)
Generating Candidate Rules
348(1)
The Apriori Algorithm
349(1)
Selecting Strong Rules
349(3)
Data Format
352(1)
The Process of Rule Selection
353(1)
Interpreting the Results
354(1)
Rules and Chance
355(2)
Example 2: Rules for Similar Book Purchases
357(1)
14.2 Collaborative Filtering
357(11)
Data Type and Format
359(1)
Example 3: Netflix Prize Contest
360(1)
User-Based Collaborative Filtering: "People Like You"
361(2)
Item-Based Collaborative Filtering
363(1)
Advantages and Weaknesses of Collaborative Filtering
364(2)
Collaborative Filtering vs. Association Rules
366(2)
14.3 Summary
368(2)
Problems
370(5)
Chapter 15 Cluster Analysis
375(32)
15.1 Introduction
376(3)
Example: Public Utilities
377(2)
15.2 Measuring Distance Between Two Records
379(6)
Euclidean Distance
380(1)
Normalizing Numerical Measurements
380(1)
Other Distance Measures for Numerical Data
381(2)
Distance Measures for Categorical Data
383(1)
Distance Measures for Mixed Data
384(1)
15.3 Measuring Distance Between Two Clusters
385(2)
Minimum Distance
385(1)
Maximum Distance
385(1)
Average Distance
385(1)
Centroid Distance
385(2)
15.4 Hierarchical (Agglomerative) Clustering
387(8)
Single Linkage
388(1)
Complete Linkage
388(1)
Average Linkage
388(1)
Centroid Linkage
389(1)
Ward's Method
389(1)
Dendrograms: Displaying Clustering Process and Results
390(1)
Validating Clusters
390(3)
Limitations of Hierarchical Clustering
393(2)
15.5 Non-Hierarchical Clustering: The k-Means Algorithm
395(6)
Choosing the Number of Clusters (k)
396(5)
Problems
401(6)
Part VI Forecasting Time Series
Chapter 16 Handling Time Series
407(16)
16.1 Introduction
408(1)
16.2 Descriptive vs. Predictive Modeling
409(1)
16.3 Popular Forecasting Methods in Business
409(1)
Combining Methods
410(1)
16.4 Time Series Components
410(5)
Example: Ridership on Amtrak Trains
411(4)
16.5 Data-Partitioning and Performance Evaluation
415(4)
Benchmark Performance: Naive Forecasts
415(1)
Generating Future Forecasts
416(3)
Problems
419(4)
Chapter 17 Regression-Based Forecasting
423(28)
17.1 A Model with Trend
424(5)
Linear Trend
424(2)
Exponential Trend
426(1)
Polynomial Trend
427(2)
17.2 A Model with Seasonality
429(3)
17.3 A Model with Trend and Seasonality
432(1)
17.4 Autocorrelation and ARIMA Models
433(9)
Computing Autocorrelation
434(2)
Improving Forecasts by Integrating Autocorrelation Information
436(4)
Evaluating Predictability
440(2)
Problems
442(9)
Chapter 18 Smoothing Methods
451(22)
18.1 Introduction
452(1)
18.2 Moving Average
452(5)
Centered Moving Average for Visualization
452(1)
Trailing Moving Average for Forecasting
453(2)
Choosing Window Width (w)
455(2)
18.3 Simple Exponential Smoothing
457(3)
Choosing Smoothing Parameter a
458(2)
Relation Between Moving Average and Simple Exponential Smoothing
460(1)
18.4 Advanced Exponential Smoothing
460(4)
Series with a Trend
460(1)
Series with a Trend and Seasonality
461(1)
Series with Seasonality (No Trend)
462(2)
Problems
464(9)
Part VII Data Analytics
Chapter 19 Social Network Analytics
473(22)
19.1 Introduction
473(2)
19.2 Directed vs. Undirected Networks
475(1)
19.3 Visualizing and Analyzing Networks
476(4)
Plot Layout
476(2)
Edge List
478(1)
Adjacency Matrix
479(1)
Using Network Data in Classification and Prediction
479(1)
19.4 Social Data Metrics and Taxonomy
480(5)
Node-Level Centrality Metrics
480(1)
Egocentric Network
481(2)
Network Metrics
483(2)
19.5 Using Network Metrics in Prediction and Classification
485(6)
Link Prediction
485(1)
Entity Resolution
485(3)
Collaborative Filtering
488(3)
19.6 Collecting Social Network Data with Python
491(1)
19.7 Advantages and Disadvantages
491(3)
Problems
494(1)
Chapter 20 Text Mining
495(20)
20.1 Introduction
496(1)
20.2 The Tabular Representation of Text: Term-Document Matrix and "Bag-of-Words"
496(1)
20.3 Bag-of-Words vs. Meaning Extraction at Document Level
497(1)
20.4 Preprocessing the Text
498(8)
Tokenization
499(2)
Text Reduction
501(1)
Presence/Absence vs. Frequency
501(1)
Term Frequency-Inverse Document Frequency (TF-IDF)
502(3)
From Terms to Concepts: Latent Semantic Indexing
505(1)
Extracting Meaning
505(1)
20.5 Implementing Data Mining Methods
506(1)
20.6 Example: Online Discussions on Autos and Electronics
506(4)
Importing and Labeling the Records
507(1)
Text Preprocessing in Python
508(1)
Producing a Concept Matrix
508(1)
Fitting a Predictive Model
508(1)
Prediction
509(1)
20.7 Summary
510(1)
Problems
511(4)
Part VIII Cases
Chapter 21 Cases
515(34)
21.1 Charles Book Club
515(7)
The Book Industry
515(1)
Database Marketing at Charles
516(2)
Data Mining Techniques
518(2)
Assignment
520(2)
21.2 German Credit
522(5)
Background
522(1)
Data
522(4)
Assignment
526(1)
21.3 Tayko Software Cataloger
527(4)
Background
527(1)
The Mailing Experiment
527(1)
Data
527(2)
Assignment
529(2)
21.4 Political Persuasion
531(4)
Background
531(1)
Predictive Analytics Arrives in US Politics
531(1)
Political Targeting
531(1)
Uplift
532(1)
Data
533(1)
Assignment
533(2)
21.5 Taxi Cancellations
535(2)
Business Situation
535(1)
Assignment
535(2)
21.6 Segmenting Consumers of Bath Soap
537(4)
Business Situation
537(1)
Key Problems
537(1)
Data
538(1)
Measuring Brand Loyalty
538(1)
Assignment
538(3)
21.7 Direct-Mail Fundraising
541(3)
Background
541(1)
Data
541(1)
Assignment
541(3)
21.8 Catalog Cross-Selling
544(2)
Background
544(1)
Assignment
544(2)
21.9 Time Series Case: Forecasting Public Transportation Demand
546(3)
Background
546(1)
Problem Description
546(1)
Available Data
546(1)
Assignment Goat
546(1)
Assignment
547(1)
Tips and Suggested Steps
547(2)
References 549(2)
Data Files Used in the Book 551(4)
Python Utilities Functions 555(10)
Index 565
GALIT SHMUELI, PHD, is Distinguished Professor at National Tsing Hua University's Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 100 publications including books.

PETER C. BRUCE is President and Founder of the Institute for Statistics Education at Statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective (Wiley) and co-author of Practical Statistics for Data Scientists: 50 Essential Concepts (O'Reilly).

PETER GEDECK, PHD, is a Senior Data Scientist at Collaborative Drug Discovery, where he helps develop cloud-based software to manage the huge amount of data involved in the drug discovery process. He also teaches data mining at Statistics.com.

NITIN R. PATEL, PhD, is cofounder and board member of Cytel Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years.