HOME > Detail View

Detail View

Data mining for business analytics : concepts, techniques and applications in Python

Data mining for business analytics : concepts, techniques and applications in Python (Loan 3 times)

Material type
단행본
Personal Author
Shmueli, Galit.
Title Statement
Data mining for business analytics : concepts, techniques and applications in Python / Galit Shmueli ... [et al.].
Publication, Distribution, etc
Hoboken, NJ :   Wiley,   2020.  
Physical Medium
xxix, 574 p. : ill.(some col.) ; 27 cm.
ISBN
9781119549840
Bibliography, Etc. Note
Includes bibliographical references and index.
Subject Added Entry-Topical Term
Business mathematics --Computer programs. Business --Data processing. Data mining. Python (Computer program language).
000 00000nam u2200205 a 4500
001 000046010910
005 20200102153551
008 191231s2020 njua b 001 0 eng d
020 ▼a 9781119549840
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 658.403802856312 ▼2 23
084 ▼a 658.40380285 ▼2 DDCK
090 ▼a 658.40380285 ▼b D232
245 0 0 ▼a Data mining for business analytics : ▼b concepts, techniques and applications in Python / ▼c Galit Shmueli ... [et al.].
260 ▼a Hoboken, NJ : ▼b Wiley, ▼c 2020.
300 ▼a xxix, 574 p. : ▼b ill.(some col.) ; ▼c 27 cm.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Business mathematics ▼x Computer programs.
650 0 ▼a Business ▼x Data processing.
650 0 ▼a Data mining.
650 0 ▼a Python (Computer program language).
700 1 ▼a Shmueli, Galit.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/Western Books/ Call Number 658.40380285 D232 Accession No. 111821084 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

Foreword by Gareth James xix


Foreword by Ravi Bapna xxi


Preface to the Python Edition xxiii


Acknowledgments xxvii


Part I Preliminaries


Chapter 1 Introduction 3


1.1 What is Business Analytics? 3


1.2 What is Data Mining? 5


1.3 Data Mining and Related Terms 5


1.4 Big Data 6


1.5 Data Science 7


1.6 Why are There So Many Different Methods? 8


1.7 Terminology and Notation 9


1.8 Road Maps to This Book 11


Chapter 2 Overview of the Data Mining Process 15


2.1 Introduction 15


2.2 Core Ideas in Data Mining 16


2.3 The Steps in Data Mining 19


2.4 Preliminary Steps 21


2.5 Predictive Power and Overfitting 34


2.6 Building a Predictive Model 40


2.7 Using Python for Data Mining on a Local Machine 44


2.8 Automating Data Mining Solutions 45


2.9 Ethical Practice in Data Mining 47


Problems 56


Part II Data Exploration and Dimension Reduction


Chapter 3 Data Visualization 61


3.1 Introduction 61


3.2 Data Examples 64


3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 65


3.4 Multidimensional Visualization 74


3.5 Specialized Visualizations 88


3.6 Summary: Major Visualizations and Operations, by Data Mining Goal 93


Problems 97


Chapter 4 Dimension Reduction 99


4.1 Introduction 100


4.2 Curse of Dimensionality 100


4.3 Practical Considerations 100


4.4 Data Summaries 102


4.5 Correlation Analysis 105


4.6 Reducing the Number of Categories in Categorical Variables 106


4.7 Converting a Categorical Variable to a Numerical Variable 108


4.8 Principal Components Analysis 108


4.9 Dimension Reduction Using Regression Models 119


4.10 Dimension Reduction Using Classification and Regression Trees 119


Problems 120


Part III Performance Evaluation


Chapter 5 Evaluating Predictive Performance 125


5.1 Introduction 126


5.2 Evaluating Predictive Performance 126


5.3 Judging Classifier Performance 131


5.4 Judging Ranking Performance 144


5.5 Oversampling 149


Problems 155


Part IV Prediction and Classification Methods


Chapter 6 Multiple Linear Regression 161


6.1 Introduction 162


6.2 Explanatory vs. Predictive Modeling 162


6.3 Estimating the Regression Equation and Prediction 164


6.4 Variable Selection in Linear Regression 169


Appendix: Using Statmodels 179


Problems 180


Chapter 7 k-Nearest Neighbors (kNN) 185


7.1 The k-NN Classifier (Categorical Outcome) 185


7.2 k-NN for a Numerical Outcome 193


7.3 Advantages and Shortcomings of k-NN Algorithms 195


Problems 197


Chapter 8 The Naive Bayes Classifier 199


8.1 Introduction 199


Example 1: Predicting Fraudulent Financial Reporting 201


8.2 Applying the Full (Exact) Bayesian Classifier 201


8.3 Advantages and Shortcomings of the Naive Bayes Classifier 210


Problems 214


Chapter 9 Classification and Regression Trees 217


9.1 Introduction 218


9.2 Classification Trees 220


9.3 Evaluating the Performance of a Classification Tree 228


9.4 Avoiding Overfitting 232


9.5 Classification Rules from Trees 238


9.6 Classification Trees for More Than Two Classes 239


9.7 Regression Trees 239


9.8 Improving Prediction: Random Forests and Boosted Trees 243


9.9 Advantages and Weaknesses of a Tree 246


Problems 248


Chapter 10 Logistic Regression 251


10.1 Introduction 252


10.2 The Logistic Regression Model 253


10.3 Example: Acceptance of Personal Loan 255


10.4 Evaluating Classification Performance 261


10.5 Logistic Regression for Multi-class Classification 264


10.6 Example of Complete Analysis: Predicting Delayed Flights 269


Appendix: Using Statmodels 278


Problems 280


Chapter 11 Neural Nets 283


11.1 Introduction 284


11.2 Concept and Structure of a Neural Network 284


11.3 Fitting a Network to Data 285


11.4 Required User Input 297


11.5 Exploring the Relationship Between Predictors and Outcome 299


11.6 Deep Learning 299


11.7 Advantages and Weaknesses of Neural Networks 305


Problems 306


Chapter 12 Discriminant Analysis 309


12.1 Introduction 310


12.2 Distance of a Record from a Class 311


12.3 Fisher''s Linear Classification Functions 314


12.4 Classification Performance of Discriminant Analysis 317


12.5 Prior Probabilities 318


12.6 Unequal Misclassification Costs 319


12.7 Classifying More Than Two Classes 319


12.8 Advantages and Weaknesses 322


Problems 324


Chapter 13 Combining Methods: Ensembles and Uplift Modeling 327


13.1 Ensembles 328


13.2 Uplift (Persuasion) Modeling 334


13.3 Summary 340


Problems 341


Part V Mining Relationships among Records


Chapter 14 Association Rules and Collaborative Filtering 345


14.1 Association Rules 346


14.2 Collaborative Filtering 357


14.3 Summary 368


Problems 370


Chapter 15 Cluster Analysis 375


15.1 Introduction 376


15.2 Measuring Distance Between Two Records 379


15.3 Measuring Distance Between Two Clusters 385


15.4 Hierarchical (Agglomerative) Clustering 387


15.5 Non-Hierarchical Clustering: The k-Means Algorithm 395


Problems 401


Part VI Forecasting Time Series


Chapter 16 Handling Time Series 407


16.1 Introduction 408


16.2 Descriptive vs. Predictive Modeling 409


16.3 Popular Forecasting Methods in Business 409


16.4 Time Series Components 410


16.5 Data-Partitioning and Performance Evaluation 415


Problems 419


Chapter 17 Regression-Based Forecasting 423


17.1 A Model with Trend 424


17.2 A Model with Seasonality 429


17.3 A Model with Trend and Seasonality 432


17.4 Autocorrelation and ARIMA Models 433


Problems 442


Chapter 18 Smoothing Methods 451


18.1 Introduction 452


18.2 Moving Average 452


18.3 Simple Exponential Smoothing 457


18.4 Advanced Exponential Smoothing 460


Problems 464


Part VII Data Analytics


Chapter 19 Social Network Analytics 473


19.1 Introduction 473


19.2 Directed vs. Undirected Networks 475


19.3 Visualizing and Analyzing Networks 476


19.4 Social Data Metrics and Taxonomy 480


19.5 Using Network Metrics in Prediction and Classification 485


19.6 Collecting Social Network Data with Python 491


19.7 Advantages and Disadvantages 491


Problems 494


Chapter 20 Text Mining 495


20.1 Introduction 496


20.2 The Tabular Representation of Text: Term-Document Matrix and "Bag-of-Words'''' 496


20.3 Bag-of-Words vs. Meaning Extraction at Document Level 497


20.4 Preprocessing the Text 498


20.5 Implementing Data Mining Methods 506


20.6 Example: Online Discussions on Autos and Electronics 506


20.7 Summary 510


Problems 511


Part VIII Cases


Chapter 21 Cases 515


21.1 Charles Book Club 515


21.2 German Credit 522


21.3 Tayko Software Cataloger 527


21.4 Political Persuasion 531


21.5 Taxi Cancellations 535


21.6 Segmenting Consumers of Bath Soap 537


21.7 Direct-Mail Fundraising 541


21.8 Catalog Cross-Selling 544


21.9 Time Series Case: Forecasting Public Transportation Demand 546


References 549


Data Files Used in the Book 551


Python Utilities Functions 555


Index 565

New Arrivals Books in Related Fields

한국파렛트컨테이너협회 (2021)